MATH 7111. Seminar in Teaching.

Seminar on individual study projects concerned with selected problems in the teaching of mathematics. This course does not earn graduate degree credit.

MATH 7188. Seminar in Mathematics Education.

This course requires students to participate in weekly research seminars in mathematics education that emphasize scholarly discussion, critical engagement with current research, and professional communication. Students attend presentations by faculty, visiting scholars, and peers, and they contribute to seminar dialogue through questioning and discussion. Each student delivers at least one formal research presentation during the semester, drawing on original research, dissertation work, or a critical analysis of existing literature in mathematics education. The course supports the development of research communication skills, familiarity with ongoing research agendas, and participation in the professional community. This course is repeatable for credit when seminar content varies.

MATH 7199A. Dissertation.

This course provides doctoral students with the opportunity to conduct an independent, original research project that contributes new knowledge to mathematics, mathematics education, or a closely related field under the supervision of a faculty advisor. Students identify a significant research problem, engage deeply with the scholarly literature, and employ appropriate theoretical, empirical, or methodological approaches to address the problem. Emphasis is placed on originality, rigor, and sustained scholarly inquiry consistent with professional standards of doctoral research. The course culminates in the completion and defense of a written dissertation that demonstrates the student’s ability to conduct independent research and communicate results at a professional level. While conducting dissertation research and writing, students must be continuously enrolled each long semester.

MATH 7299A. Dissertation.

This course provides doctoral students with the opportunity to conduct an independent, original research project that contributes new knowledge to mathematics, mathematics education, or a closely related field under the supervision of a faculty advisor. Students identify a significant research problem, engage deeply with the scholarly literature, and employ appropriate theoretical, empirical, or methodological approaches to address the problem. Emphasis is placed on originality, rigor, and sustained scholarly inquiry consistent with professional standards of doctoral research. The course culminates in the completion and defense of a written dissertation that demonstrates the student’s ability to conduct independent research and communicate results at a professional level. While conducting dissertation research and writing, students must be continuously enrolled each long semester. The course can be repeated as necessary. The dissertation credit (18 hours) will not be awarded until the dissertation is submitted for binding. Prerequisite: Instructor Approval.

MATH 7301. Studies in Mathematics.

This course provides foundational preparation in graduate mathematics for students entering doctoral study in mathematics or mathematics education. Topics may include essential concepts and methods from advanced algebra, analysis, topology, discrete mathematics, and proof-based reasoning, depending on student background and program needs. Emphasis is placed on strengthening mathematical maturity, rigorous communication, abstraction, and the transition to graduate-level expectations in reading, writing, and problem solving. This course may be repeated and does not earn graduate degree credit.

MATH 7302. History of Mathematics.

This course emphasizes the development of mathematics and the accomplishments of mathematicians who contributed to its progress across cultures and historical periods. Topics include the emergence of major mathematical ideas, the historical development of algebra, geometry, calculus, and other significant areas, original and secondary historical sources, and the cultural and philosophical settings in which mathematics evolved. Students will be able to explain the historical significance of various mathematical achievements and discuss connections between earlier developments, contemporary mathematical thought, and the teaching and learning of mathematics.

MATH 7303. Analysis I.

This course covers measure theory with special emphasis on the Lebesgue measure. Topics include the outer measure, sigma-algebra of measurable sets, properties of measurable sets, Borell-Canteli lemma, non-measurable sets, Cantor-Lebesgue function, Lebesgue measurable functions, pointwise limits, simple approximations, and Littlewood’s three principles. Additional attention may be given to convergence theorems, and the role of approximation theory in modern analysis. Emphasis is placed on rigorous proof, abstract reasoning, and the analytical foundations needed for advanced work in real analysis, probability, and related mathematical fields.

MATH 7306. Current Research in Math Education.

This course examines foundational and contemporary research in mathematics education with attention to the social, political, and economic trends that shape research agendas in local, state, national, and international settings. Topics include major themes in mathematics education research, research traditions and methods, contemporary debates in the field, and the interpretation of scholarly literature within broader educational contexts. Students will be able to discuss contemporary and historical trends and issues in mathematics education, develop skills for written synthesis of academic arguments, and identify research areas of interest and develop expertise in those areas.

MATH 7307. Algebra I.

This course examines the structure and methods of modern algebra with emphasis on group‑theoretic foundations and select topics from ring theory. Topics include permutation groups, symmetry groups, Sylow theorems, and selected topics from ring theory. Additional attention is given to homomorphisms, quotient structures, and related algebraic constructions that support advanced study in abstract algebra. Emphasis is placed on rigorous proof, structural reasoning, and the analysis of algebraic systems that arise throughout advanced mathematics. The course prepares students for further doctoral‑level work in algebra and related fields by strengthening abstract reasoning and proof‑writing skills.

MATH 7309. Topology I.

This course studies point-set topology at the doctoral level. Topics include topological spaces, continuous functions, connectedness, compactness, countability, separability, metrizability, CW complexes, simplicial complexes, nerves, and dimension theory. Additional attention may be given to product and quotient constructions, subspace topology, and examples that connect foundational topology to later study in geometry, algebra, and analysis. Emphasis is placed on rigorous proof, precise use of definitions, and structural reasoning in abstract topological settings.

MATH 7313. Analysis II.

This course covers the theory of integration with special emphasis on Lebesgue integrals. Topics include the Lebesgue integral for bounded, finitely supported, and measurable functions, convergence theorems, differentiability of monotone functions, absolute continuity, Lp spaces, and Lp completeness. Additional attention may be given to convergence theorems, and the role of integration theory in modern analysis. Emphasis is placed on rigorous proof, abstract reasoning, and the analytical foundations needed for advanced work in real analysis, probability, and related mathematical fields. Prerequisite: Math 7303 with a grade of "B" or better.

MATH 7315. Calculus of Variations.

This course examines the theoretical foundations of the calculus of variations with emphasis on variational principles and their applications. Topics include properties of functionals, first and second variations, extremal problems, Euler–Lagrange equations, and stability theory. The course considers variational formulations in multiple settings. Emphasis is placed on rigorous analysis, derivation of variational conditions, and interpretation of solutions. The course prepares doctoral students for advanced research involving variational methods and related analytical techniques. Prerequisite: MATH 7303 with a grade of "B" or higher.

MATH 7317. Algebra II.

This course examines advanced algebraic structures of rings and fields at the doctoral level. Topics include rings, ideals, modules, polynomial rings, the Euclidean algorithm, finite fields, and field extensions, along with an introduction to Galois theory and selected geometric applications. Emphasis is placed on rigorous proof, structural reasoning, and the analysis of algebraic systems that support advanced study in algebra, geometry, number theory, and related areas of mathematics. The course prepares students for further doctoral‑level work by strengthening abstract reasoning, proof construction, and the ability to connect algebraic structures across mathematical disciplines. Prerequisite: MATH 7307 with a grade of 'B' or better.

MATH 7319. Topology II: Algebraic Topology.

This course covers the fundamental concepts and tools of algebraic topology. Topics include the fundamental group, covering spaces, homotopy type, the higher homotopy groups, singular homology theory, and the computation of homology groups via exact sequences and applications. Additional attention may be given to representative examples, computational methods, and the role of algebraic invariants in distinguishing and analyzing topological spaces. Emphasis is placed on rigorous proof, structural reasoning, and the use of algebraic methods to study topological phenomena. Prerequisite: MATH 7307 and MATH 7309 with grades of 'B' or better.

MATH 7321. Graph Theory.

This course studies graph theory at an advanced level with emphasis on both structural and applied aspects of graphs. Topics include trees, connectivity of graphs, Eulerian graphs, Hamiltonian graphs, planar graphs, graph coloring, matchings, factorizations, digraphs, networks, and network flow problems. Attention may also be given to algorithms, optimization questions, and representative applications in discrete mathematics and related fields. Emphasis is placed on rigorous proof, structural analysis, and graph-theoretic reasoning.

MATH 7323. Theories of Knowing and Learning in Mathematics Education.

This course surveys the major theories of knowing and learning that have influenced mathematics education. Topics include behaviorism, constructivism, sociocultural theories, situated cognition, and other theoretical perspectives used to explain how learners develop mathematical understanding. Attention is given to how these theories define knowledge, learning, teaching, and participation, and to the ways they shape curriculum, research, and classroom practice in mathematics education. Students will be able to compare theoretical frameworks and interpret their implications for the teaching and learning of mathematics.

MATH 7324. Curriculum Design & Analysis.

This course examines, analyzes, and evaluates the various concepts, topics, methods, and techniques related to curriculum design in mathematics education for grade levels P-16. Topics may include curriculum theory, the historical development of mathematics curricula, standards and policy, curricular coherence across grade bands, implementation issues, and the evaluation of instructional materials and curricular models. Students will be able to analyze curricular materials, evaluate design principles, and analyze relationships among curriculum, instruction, assessment, and equity in mathematics education.

MATH 7328. Instructional Techniques & Assessments.

This course examines, analyzes, and evaluates the various concepts, topics, methods, and techniques of instruction in mathematics education and the related assessment procedures for grade levels P–20. Topics may include instructional design, teaching practices, classroom discourse, formative and summative assessment, task design, feedback, evaluation of student thinking, and the interpretation of assessment data in mathematics education. Students will be able to apply research-based perspectives on teaching and learning to classroom practices and to evaluate alignment among mathematical learning goals, instructional decisions, and assessment practices.

MATH 7331. Combinatorics.

This course is a study of fundamental principles of combinatorics. Topics include permutations and combinations, the Pigeonhole principle, the principle of inclusion–exclusion, binomial and multinomial theorems, special counting sequences, partitions, posets, extremal set theory, generating functions, recurrence relations, and the Pólya theory of counting. Emphasis is placed on rigorous proof, enumeration methods, structural reasoning, and the analysis of finite discrete structures that support further study in combinatorics, graph theory, and related areas of mathematics.

MATH 7342. Research in Undergraduate Mathematics Education I.

This course examines the theoretical foundations of Research in Undergraduate Mathematics Education (RUME). Students study historical and contemporary theoretical perspectives that inform research on the teaching and learning of mathematics at the undergraduate level. Emphasis is placed on critically reading, analyzing, and interpreting research literature in the field. Students will be able to discuss how theoretical frameworks shape research questions, methodologies, and interpretations within RUME. Prerequisite: Math 7306 with a grade of a "B" or better.

MATH 7344. Research in Undergraduate Mathematics Education II.

This course examines advanced research design and development in Research in Undergraduate Mathematics Education (RUME). Through a topic-driven analysis of current RUME literature, students will examine how research is designed and conducted in relation to the teaching and learning of advanced undergraduate mathematics topics such as proof, calculus and analysis, abstract algebra, linear algebra, and differential equations. At the end of the course, students will be able to connect theoretical perspectives to research questions, methodologies, and data interpretations. Students will be able to design and execute research studies in RUME that are appropriate for dissertation-level work. Prerequisite: MATH 7306 and MATH 7342 with grades of a "B" or better.

MATH 7346. Quantitative Research Analysis in Mathematics Education.

This course surveys research techniques used in quantitative analysis for mathematics education. Topics include experimental design, statistical analysis, and the use of appropriate design methodologies to achieve the strongest possible evidence to support or refute a knowledge claim. Additional attention may be given to measurement, validity, inferential reasoning, interpretation of quantitative findings, and the alignment of research questions, methods, and evidence. Students will be able to evaluate the quality of quantitative studies and design rigorous quantitative research in mathematics education. Prerequisite: MATH 7306 and MATH 7325 with grades of 'B' or better.

MATH 7352. Introduction to Qualitative Research in Mathematics Education.

This course introduces doctoral students to principles and techniques of qualitative research as applied to mathematics education. Topics include qualitative research design, sampling strategies, data sources, methods of constant comparison, and conceptualizations of validity and rigor within qualitative research paradigms. Students examine how qualitative methodologies are used to investigate teaching and learning in mathematics education and how such studies are evaluated within the scholarly literature. At the end of the course, students will be able to implement basic qualitative methodologies, interpret qualitative data, and critique published research. Prerequisite: Math 7306 with a grade of "B" or better.

MATH 7354. Advanced Qualitative Research.

This course examines advanced qualitative research methods used in mathematics education. Emphasis is placed on techniques for qualitative data collection and analysis, including interpreting data and representing findings. Topics include qualitative research design, data collection strategies, analytic frameworks, trustworthiness, ethics, and methodological coherence. Through engaging in sustained analyses of qualitative evidence, students will be able to apply established qualitative methods to research problems in mathematics education. Students will be able to critique qualitative methods, evaluate published studies, and discuss strengths and weaknesses of various approaches relative to the aims of the research problem. Prerequisite: MATH 7352 or ED 7352 with a grade of "B" or better.

MATH 7356C. Action Research in Mathematics Education.

This course examines the theoretical foundations, methodological approaches, and practical considerations of action research in mathematics education. Emphasis is placed on the systematic study of instructional practices, curriculum design, assessment strategies, and classroom‑based problem solving. Students analyze published action research studies, evaluate issues of validity, ethics, and rigor, and design an original action research proposal grounded in relevant literature. The course supports the development of research questions, data collection strategies, and analytic techniques appropriate for educational settings. Attention is given to the role of reflective practice and evidence‑based decision making in mathematics education contexts. Prerequisite: MATH 7346 or MATH 7352 or ED 7352 with a grade of "B" or better.

MATH 7358. Advanced Quantitative Research in Mathematics Education.

This course examines advanced quantitative research methods used in mathematics education. Topics include experimental design, statistical modeling, multivariate and multilevel analysis, and methodological considerations for producing and interpreting quantitative evidence. Emphasis is placed on aligning research questions with appropriate quantitative designs and analytic strategies, as well as critically evaluating the strength and limitations of quantitative findings. Students will be able to investigate questions in mathematics education using advanced quantitative techniques and to interpret and evaluate published research literature in mathematics education. Prerequisite: MATH 7346 with a grade of "B" or better.

MATH 7361. Seminar in Advanced Mathematics.

This course is a doctoral‑level seminar in advanced mathematics, where course content varies by offering and is determined by faculty expertise and student research interests. Topics are drawn from areas such as analysis, algebra, topology and geometry, applied mathematics, or probability and statistics. Instructional modality will be appropriate for the topic and determined by the instructor, and may include student‑led presentations, guided discussion, collaborative problem analysis, or directed study of advanced literature. This course may be repeated for credit when the seminar topic differs.

MATH 7363A. COMPLEX ANALYSIS.

This course is a brief introduction to the complex number system and the basic point-set topology of the complex plane, followed by a proof-based and rigorous study of the principal results in the analysis of functions of a single complex variable. Topics may include analytic functions, contour integration, the Cauchy integral theorem and formula, Laurent series, residues, conformal mappings, and selected extensions to more advanced geometric viewpoints such as Riemann surfaces. Emphasis is placed on rigorous proof, conceptual understanding, and the role of complex analysis in advanced mathematics.

MATH 7363B. NUMERICAL ANALYSIS.

This course examines advanced numerical analysis techniques for the analysis and implementation of mathematical algorithms. Emphasis is placed on the theoretical foundations of numerical methods, including stability, convergence, consistency, and error analysis, as well as practical implementation using computational and computer algebra systems. Symbolic, numerical, and graphical techniques are used to analyze algorithm performance. Applications are drawn from mathematics, science, and engineering. Instructional modality will be appropriate for the topic and determined by the instructor.

MATH 7363C. Functional Analysis.

This course examines foundational results and methods in functional analysis at the doctoral level. Core topics include the Hahn-Banach theorem, the uniform boundedness principle, and the open mapping theorem, along with their consequences for normed linear spaces and Banach spaces. Additional topics may include bounded linear operators, dual spaces, weak topologies, and selected applications to analysis and partial differential equations. Emphasis is placed on rigorous proof, abstract reasoning, and the role of functional analytic techniques in modern mathematics. Prerequisite: MATH 7303 with a "C" or better.

MATH 7363E. Numerical Analysis II.

This course examines advanced numerical methods for the solution of partial differential equations. Emphasis is placed on the analysis and numerical implementation of algorithms for linear and selected nonlinear partial differential equations. Topics may include finite difference, finite element, and spectral methods; stability, consistency, and convergence analysis; and the solution of large linear systems arising from discretized PDEs. Applications are drawn from mathematics, science, and engineering to illustrate methodological principles rather than prescribe applied outcomes. Prerequisite: MATH 7363B with a grade of a "C" or better.

MATH 7363F. Functional Analysis II.

This course examines advanced topics in functional analysis with a focus on infinite‑dimensional vector spaces and their applications. The course studies spaces of functions, measures, and distributions, emphasizing the structural and analytical differences between finite‑ and infinite‑dimensional settings. Topics may include Banach and Hilbert space theory, Fourier transform methods, bounded and unbounded linear operators, and selected aspects of operator theory. Attention is given to the role of functional analysis in modern analysis, partial differential equations, and numerical analysis. Emphasis is placed on rigorous proof, abstract reasoning, and the development of techniques used to analyze complex mathematical problems. Prerequisite: MATH 7363C with a grade of "C" or better.

MATH 7366C. Teaching Teachers (In-Service; Pre-Service).

This course examines research‑based approaches to the preparation and professional development of mathematics teachers. Topics include the education of pre‑service and in‑service teachers, theoretical frameworks in teacher learning, and models of mathematics teacher education. The course analyzes research literature, policy documents, and professional standards relevant to mathematics teacher preparation, treating these sources as objects of scholarly study rather than prescriptive mandates. Students will be able to evaluate research-based models of professional development, compare perspectives, and discuss the implications of differing approaches to the teaching and learning of mathematics. Prerequisite: MATH 7306 with a grade of 'B' or better.

MATH 7366D. Teaching Specialized Content.

This course provides an in-depth study of a specialized content area in mathematics with emphasis on teaching and learning. The specific content area will vary by instructor and their specialization. Some examples include geometry, quantitative reasoning, probability and statistics. Attention is given to implications for curriculum, classroom practice, teacher professional development, theories of teaching and learning, and methods for research. Students will be able to interpret, discuss, and synthesize scholarly work on the focal topic.

MATH 7366E. Developmental Mathematics Curriculum.

This course examines research, development, and evaluation related to developmental mathematics curricula. Emphasis is placed on the study of curriculum scope and sequence, instructional goals, and design principles underlying developmental mathematics programs. The course analyzes research literature, research‑based models, and selected policy and professional documents relevant to developmental mathematics, treating these materials as objects of scholarly inquiry rather than prescriptive mandates. Students examine how curricular frameworks are designed, evaluated, and revised in response to research findings.

MATH 7367B. Advanced Group Theory.

This course examines advanced topics in group theory at the doctoral level. Topics may include solvable, p‑solvable, and nilpotent groups; group actions; transfer theorems; simple groups and composition series; the generalized Fitting subgroup; automorphism groups; classical groups; and linear representations of groups. Emphasis is placed on structural results, proof techniques, and the role of group theory in modern algebra. The course develops rigorous reasoning and abstraction skills necessary for advanced study and research in algebra. Prerequisite: MATH 7307 with a grade of "C" or better.

MATH 7369C. Low-Dimensional Topology.

This course introduces advanced topics in low‑dimensional topology at the doctoral level. Topics include the study of surfaces, knots and links, 3‑manifolds, and selected aspects of 4‑manifold theory. Emphasis is placed on foundational results, key techniques, and current research directions in low‑dimensional topology. Students examine how geometric, algebraic, and topological methods are used to analyze low‑dimensional spaces. The course develops rigorous reasoning and familiarity with ideas central to contemporary research in topology, preparing students for further study and research in geometric and topological fields. Prerequisite: MATH 7307 and MATH 7309 with grades of "C" or better.

MATH 7369D. Characteristic Classes.

This course examines vector bundles and characteristic classes at the doctoral level. Topics include Stiefel-Whitney classes, Chern classes, the Euler class, and Pontrjagin classes, with emphasis on their definitions, properties, and methods of computation. Additional topics may include applications to manifold immersion and embedding problems. The course explores how characteristic classes serve as fundamental tools in topology and geometry, connecting algebraic, geometric, and topological techniques. Emphasis is placed on rigorous proof, abstract reasoning, and the role of characteristic classes in contemporary mathematical research. Prerequisite: MATH 7317 and MATH 7319 with grades of a "C" or better.

MATH 7369E. Differential Geometry.

This course examines modern tools and methods of differential geometry at the doctoral level. Topics include smooth manifolds, Riemannian metrics, connections, covariant derivatives, geodesics, curvature, and intrinsic and extrinsic geometric computations. Additional topics may include hyperbolic geometry, Lie groups, Chern-Weil theory, surfaces of prescribed mean curvature, the Gauss-Bonnet theorem, and the Cartan-Hadamard theorem. Emphasis is placed on rigorous proof, geometric intuition, and the role of differential geometry in contemporary mathematical research. Prerequisite: MATH 7307 and MATH 7309 with grades of "C" or better.

MATH 7371A. Advanced Graph Theory.

This course is an advanced study of graph theory, emphasizing both classical results and modern research directions selected by the instructor. Topics may include Turán‑type problems, Ramsey theory, extremal graph theory, random graph theory, algebraic graph theory, domination and distance parameters, and selected applications. The course focuses on theoretical frameworks, proof techniques, and the analysis of graph structures that arise in contemporary combinatorics. Students engage with foundational and current research results to develop advanced problem‑solving skills and mathematical maturity, preparing them for independent research in graph theory and related areas. Prerequisite: MATH 7321 with a grade of "B" or better.

MATH 7371B. Advanced Combinatorics.

This course provides an advanced study of combinatorics with emphasis on both classical structures and modern theoretical developments. Topics may include block designs, Latin squares, combinatorial optimization, coding theory, matroids, difference sets, and finite geometry. The course focuses on rigorous definitions, proof techniques, and the analysis of combinatorial structures that arise across mathematics. Students examine foundational results and selected contemporary work to develop advanced problem‑solving skills and mathematical maturity. The course prepares students for further research in combinatorics and related areas of mathematics. Prerequisite: MATH 7331 with a grade of 'B' or better.

MATH 7371C. Combinatorial Number Theory.

This course provides an advanced study of fundamental techniques in combinatorial number theory. Topics may include additive number theory, Waring’s problem, and probabilistic methods in number theory. Emphasis is placed on rigorous definitions, proof techniques, and structural analysis of number‑theoretic problems using combinatorial methods. Students engage with classical results and selected modern developments to build mathematical maturity and research readiness. The course is designed to support doctoral‑level study by strengthening abstract reasoning skills and preparing students for advanced research in number theory and related areas. Prerequisite: MATH 7331 with a grade of 'B' or better.

MATH 7371D. Discrete Optimization.

This course provides an advanced study of fundamental techniques in discrete optimization. Topics may include linear programming, integer and nonlinear integer programming, dynamic programming, matroids, and computational complexity, as well as classical optimization problems such as scheduling, location, transportation, postman, and traveling salesman problems. The course emphasizes rigorous problem formulation, mathematical modeling, and algorithmic analysis. Students examine theoretical foundations and complexity considerations, including NP‑completeness, to assess problem feasibility and solution approaches. The course prepares students for advanced research and applications in optimization, operations research, theoretical computer science, and applied mathematics. Prerequisite: MATH 7321 and 7331 with grades of "B" or better.

MATH 7371F. Probabilistic Methods in Discrete Mathematics.

This course provides an advanced study of probabilistic techniques used in discrete mathematics. Topics may include linearity of expectation, alterations, second‑moment methods, the Lovász local lemma, correlation inequalities, martingales, the Poisson paradigm, and pseudo‑randomness. These methods are applied to problems arising in graph theory, combinatorics, combinatorial number theory, combinatorial geometry, and the analysis of algorithms. Emphasis is placed on rigorous proofs, careful probabilistic reasoning, and the interpretation of random structures. The course prepares students for advanced research by developing mathematical maturity and familiarity with probabilistic tools central to modern discrete mathematics. Prerequisite: MATH 7321 and 7331 with grades of "B" or better.

MATH 7371G. Applied Discrete Mathematics.

This course introduces fundamental concepts in applied discrete mathematics, including logic, Boolean algebra, binomial coefficients, graph theory, and combinatorics. Emphasis is placed on the application of discrete mathematical methods to problems arising in areas such as algorithmic complexity and network theory. Topics may vary by instructor, allowing flexibility in the selection of applications and discrete structures. The course focuses on rigorous reasoning, problem formulation, and the use of discrete techniques to analyze applied problems.

MATH 7371H. Combinatorial Networks.

This course provides an advanced study of combinatorial networks, focusing on the use of combinatorial methods to model and analyze interconnected structures. The course introduces fundamental concepts as well as selected recent developments in the field. Emphasis is placed on structural reasoning, abstraction, and rigorous mathematical analysis of networks arising in discrete settings. Students examine theoretical frameworks used to represent complex relationships and dependencies within networked systems. The course is designed for graduate students preparing for research in mathematics and related disciplines and develops mathematical maturity, proof‑based reasoning, and familiarity with modern research directions. Prerequisite: MATH 7307 with a grade of "B" or better.

MATH 7371I. Advanced Set Theory.

This course introduces foundational methods and structures used in contemporary set theory research. Topics include the axioms of Zermelo-Fraenkel set theory with Choice (ZFC), ordinals and cardinals, transfinite recursion, and the von Neumann universe. The course also examines selected advanced topics such as large cardinals, Gödel’s constructible universe, and forcing techniques. Emphasis is placed on formal proof methods, internal model construction, and independence results.

MATH 7373B. Partial Differential Equations I.

This course examines foundational theory and methods for partial differential equations at the graduate level. Topics include typical equations arising in mathematical physics, first‑order equations and the Cauchy problem, classification of second‑order equations, and the Cauchy problem for hyperbolic equations. Additional topics include Duhamel’s principle, potential theory and elliptic equations, the maximum principle, and parabolic equations. Emphasis is placed on rigorous analysis, solution techniques, and interpretation of results within a mathematical framework. The course develops analytical tools essential for advanced study in applied mathematics, analysis, and related fields.

MATH 7373C. Partial Differential Equations II.

This course examines advanced theory of partial differential equations with emphasis on existence and uniqueness results for boundary value problems. Topics include linear evolution equations, variational and non‑variational techniques, Hamilton–Jacobi equations, and conservation laws. Emphasis is placed on rigorous reasoning, theoretical foundations, and connections between partial differential equations, analysis, optimization, and numerical methods. The course builds on prior graduate‑level study of partial differential equations and prepares students for advanced research and coursework in applied and theoretical mathematics. Prerequisite: MATH 7373B with a grade of "B" or better.

MATH 7373G. Spectral Methods.

This course examines spectral methods for the numerical solution of differential equations. Emphasis is placed on spectral collocation techniques and the efficient numerical implementation of algorithms. Topics include Fourier and Chebyshev methods applied to ordinary and partial differential equations arising in areas such as fluid mechanics, wave phenomena, and quantum mechanics. The course addresses accuracy, stability, and computational efficiency of spectral algorithms. By integrating theoretical analysis with computational practice, the course prepares students for advanced study and research involving high‑accuracy numerical methods for differential equations. Prerequisite: MATH 7363E with a grade of "B" or better.

MATH 7378A. Problem Solving, Reasoning, and Proof.

This course examines fundamental concepts of mathematical problem solving, logical reasoning, set theory, and proof within mathematics education. Students study how these concepts are developed, represented, and analyzed across mathematics curricula spanning pre‑college through undergraduate levels P-20. Emphasis is placed on theoretical perspectives, research findings, and instructional frameworks related to reasoning and proof. Through examination of curricular materials and educational practices spanning pre-school through college, students will be able to discuss how these concepts are introduced and developed. Prerequisite: MATH 7306 with a grade of "B" or better.

MATH 7378B. Connecting and Communicating Math.

This course examines one of the basic principles involved in mathematics education: Connecting and Communicating Mathematics. This fundamental theme will be reviewed, researched, and discussed. Prerequisite: MATH 7306.

MATH 7378C. Students’ Mathematical Ideas.

This course examines research‑based perspectives on students’ mathematical ideas and ways of thinking. Emphasis is placed on understanding how students conceptualize mathematical concepts, reason about mathematical problems, and develop mathematical understanding across educational contexts. The course surveys theoretical frameworks and research methodologies used to study students’ mathematical thinking, treating instructional practices and learning theories as objects of scholarly inquiry. Students analyze and interpret students’ mathematical reasoning, evaluate and critique research on student thinking, and synthesize findings across empirical studies. Prerequisite: MATH 7306 with a grade of a "B" or better.

MATH 7378G. Discourse Processes, Traditions, and Analysis in Mathematics Education.

This course examines theories, traditions, and methods of discourse analysis as applied to mathematics education. Drawing on interdisciplinary perspectives from the humanities and social sciences, the course focuses on how discourse is conceptualized, studied, and analyzed in mathematical settings. Students examine theoretical frameworks and methodological approaches used to investigate classroom discourse, mathematical communication, and meaning‑making in mathematics learning. Students explain how different discourse traditions are used to address research questions in mathematics education. Prerequisite: MATH 7306 with a grade of "B" or better.

MATH 7385. Independent Study in Mathematics.

This course provides an individualized graduate‑level study opportunity in mathematics under the supervision of a faculty member. Students investigate a focused topic selected in consultation with the supervising faculty member, engaging with advanced mathematical concepts, methods, and literature appropriate to the chosen area. Emphasis is placed on developing depth of understanding, rigorous reasoning, and scholarly independence. The specific content is determined by the instructor and may include directed readings, problem analysis, or research‑oriented activities. This course may be repeated for credit when the topic emphasis differs.

MATH 7386. Independent Study in Mathematics Education.

This course provides an individualized doctoral‑level study opportunity in mathematics education under the supervision of a faculty member. Students investigate a focused topic selected in consultation with the supervising faculty member, examining relevant research, theories, and methodological approaches. Emphasis is placed on developing scholarly depth, engaging critically with the literature, and applying appropriate analytical or research‑based approaches within the chosen topic area. Instructional modality and expectations are determined by the instructor and may include directed readings, research activities, or analytical projects. This course may be repeated for credit when the topic of emphasis differs.

MATH 7387. Consulting.

This course focuses on developing skills in the collaborative practice of mathematics and statistics. The course will consist of class discussion, readings, and different projects. Topics include the application of mathematics or statistics to solve real-world problems through case studies and collaborative projects, as well as the application of ethical considerations to their professional practice. Taking this course will allow students to gain skills in problem solving and providing mathematical and statistical consulting services.

MATH 7389. Internship.

This course provides a supervised internship experience designed to develop practical and professional skills in mathematics or mathematics education. Students work under the guidance of a faculty supervisor while engaging in applied activities in industry, government agencies, educational institutions, or other approved settings. Internship experiences must directly contribute to the student’s understanding of mathematical applications or mathematics education practice. Emphasis is placed on the integration of academic knowledge with professional experience, reflective analysis of applied work, and communication of outcomes through written documentation or presentations.

MATH 7396. Mathematics Education Research Seminar.

This course engages students in collaborative mathematics education research through supervised faculty mentorship and structured seminar activities. Students identify a researchable problem in mathematics education, review and synthesize relevant literature, formulate a research question, and design an appropriate methodology. Students analyze data using methods aligned with the study design, interpret results, and articulate conclusions and limitations consistent with scholarly standards. Emphasis is placed on research ethics, methodological rigor, and clear academic writing. Students create a draft research manuscript suitable for scholarly review or further development. Prerequisite: MATH 7356 and [ED 7352 or MATH 7352 or MATH 7346] with grades of "B" or better.

MATH 7399A. Dissertation.

This course provides doctoral students with the opportunity to conduct an independent, original research project that contributes new knowledge to mathematics, mathematics education, or a closely related field under the supervision of a faculty advisor. Students identify a significant research problem, engage deeply with the scholarly literature, and employ appropriate theoretical, empirical, or methodological approaches to address the problem. Emphasis is placed on originality, rigor, and sustained scholarly inquiry consistent with professional standards of doctoral research. The course culminates in the completion and defense of a written dissertation that demonstrates the student’s ability to conduct independent research and communicate results at a professional level. While conducting dissertation research and writing, students must be continuously enrolled each long semester. The course can be repeated as necessary. The dissertation credit (18 hours) will not be awarded until the dissertation is submitted for binding. Prerequisite: Instructor Approval.

MATH 7599A. Dissertation.

This course provides doctoral students with the opportunity to conduct an independent, original research project that contributes new knowledge to mathematics, mathematics education, or a closely related field under the supervision of a faculty advisor. Students identify a significant research problem, engage deeply with the scholarly literature, and employ appropriate theoretical, empirical, or methodological approaches to address the problem. Emphasis is placed on originality, rigor, and sustained scholarly inquiry consistent with professional standards of doctoral research. The course culminates in the completion and defense of a written dissertation that demonstrates the student’s ability to conduct independent research and communicate results at a professional level. While conducting dissertation research and writing, students must be continuously enrolled each long semester. The course can be repeated as necessary. The dissertation credit (18 hours) will not be awarded until the dissertation is submitted for binding. Prerequisite: Instructor Approval.

MATH 7699A. Dissertation.

This course provides doctoral students with the opportunity to conduct an independent, original research project that contributes new knowledge to mathematics, mathematics education, or a closely related field under the supervision of a faculty advisor. Students identify a significant research problem, engage deeply with the scholarly literature, and employ appropriate theoretical, empirical, or methodological approaches to address the problem. Emphasis is placed on originality, rigor, and sustained scholarly inquiry consistent with professional standards of doctoral research. The course culminates in the completion and defense of a written dissertation that demonstrates the student’s ability to conduct independent research and communicate results at a professional level. While conducting dissertation research and writing, students must be continuously enrolled each long semester. The course can be repeated as necessary. The dissertation credit (18 hours) will not be awarded until the dissertation is submitted for binding. Prerequisite: Instructor Approval.

MATH 7999A. Dissertation.

This course provides doctoral students with the opportunity to conduct an independent, original research project that contributes new knowledge to mathematics, mathematics education, or a closely related field under the supervision of a faculty advisor. Students identify a significant research problem, engage deeply with the scholarly literature, and employ appropriate theoretical, empirical, or methodological approaches to address the problem. Emphasis is placed on originality, rigor, and sustained scholarly inquiry consistent with professional standards of doctoral research. The course culminates in the completion and defense of a written dissertation that demonstrates the student’s ability to conduct independent research and communicate results at a professional level. While conducting dissertation research and writing, students must be continuously enrolled each long semester. The course can be repeated as necessary. The dissertation credit (18 hours) will not be awarded until the dissertation is submitted for binding. Prerequisite: Instructor Approval.

CS 7100. Graduate Computer Science Internship.

This course provides doctoral students in computer science with supervised industry internship experience. Students engage in professional activities under the guidance of computer scientists or engineers in an organizational setting. The internship involves applying computing knowledge to practical tasks and includes documentation of work through structured reports. The course focuses on the integration of academic knowledge with professional practice in computing environments.

CS 7199. Dissertation.

This course provides enrollment for doctoral candidates engaged in dissertation research and writing in computer science. Students work under the supervision of a dissertation advisor and complete activities such as research planning, experimental or theoretical investigation, algorithm or system development, and preparation of dissertation chapters. Candidates may employ research methods appropriate to their specialization and disciplinary standards. The course includes documentation of research findings and preparation of written materials associated with dissertation work. Prerequisite: Instructor approval.

CS 7299. Dissertation.

This course provides enrollment for doctoral candidates engaged in dissertation research and writing in computer science. Students work under the supervision of a dissertation advisor and complete activities such as research planning, experimental or theoretical investigation, algorithm or system development, and preparation of dissertation chapters. Candidates may employ research methods appropriate to their specialization and disciplinary standards. The course includes documentation of research findings and preparation of written materials associated with dissertation work. Prerequisite: Instructor approval.

CS 7300. Introduction to Research in Computer Science.

This course introduces foundational concepts and practices in computer science research. Topics include research processes, methodologies, ethics, institutional review considerations, literature review, paper critique, research proposal development, and presentation techniques. Students examine examples of faculty research to understand current research areas and available tools and platforms used in computing research. The course emphasizes analysis of research methods and communication of scholarly work.

CS 7309. Professional Development of Doctoral Assistants.

This course examines the roles and responsibilities of doctoral students serving as instructional and teaching assistants in computer science. Topics include pedagogy for introductory and upper-division courses, ethical and legal considerations, supervision and coordination of lab activities, and technical support practices in instructional settings. Students participate in seminars, guest presentations, and practice-based assignments, including teaching presentations, peer review, and reflective writing. The course addresses instructional methods, mentoring approaches, and professional conduct in academic environments.

CS 7311. Data-Driven AI Systems Design.

This course provides an advanced, research-oriented study of how data science drives the design, development, and evaluation of AI-powered systems. It focuses on end-to-end, data-centric workflows in which data informs task definition, model selection, system architecture, and evaluation protocols across diverse application domains. Students design and implement robust pipelines for collecting, cleaning, transforming, and integrating multi-modal data, and then use these pipelines to develop and assess AI methods under realistic constraints such as scale, noise, and drift. Methodology emphasizes reproducible experimentation, critical comparison of alternative data and model choices, and reflection on how data quality, bias, and feedback loops shape system behavior and downstream impacts. Students conduct original, data-driven research on AI systems and communicate design trade-offs and empirical findings to both technical and interdisciplinary audiences.

CS 7312. Advanced Data Mining.

This course provides in-depth coverage of data mining topics, including classification, cluster analysis, and frequent pattern mining. Students examine theoretical foundations and implement data mining techniques through programming assignments. The course includes the use of data mining tools and frameworks for analysis and experimentation. Students complete a project involving the application of data mining methods to a defined problem, including data preparation, model development, and evaluation.

CS 7313. Advanced Machine Learning and Pattern Recognition.

This course examines advanced theoretical concepts and methods in machine learning and pattern recognition. Topics include traditional algorithms such as support vector machines and ensemble methods, as well as deep learning architectures including convolutional networks, recurrent networks, and transformers. The course addresses feature engineering, model evaluation, and optimization techniques through algorithmic and computational approaches. Students analyze and implement machine learning methods and evaluate their performance across different problem settings.

CS 7314. Bioinformatics.

This course introduces algorithms and computational methods for data-intensive analysis in biological and biomedical applications, including drug response prediction, gene regulatory network analysis, and protein/RNA structure prediction. Topics include greedy algorithms, linear and statistical modeling, clustering, network analysis, expectation-maximization, and Hidden Markov models, as well as machine learning and deep learning approaches for high-dimensional biological data. The course examines integration of classical algorithms with data-driven modeling frameworks and methods for analyzing complex biological datasets.

CS 7315. Network Science.

This course examines theoretical foundations and current research topics in network science. Topics include mathematical models for complex networks, computational algorithms for structural analysis, dynamic processes on networks, and graph-based machine learning and data mining methods. Students analyze research literature and complete project-based assignments involving modeling, analysis, and algorithm design for networked systems.

CS 7321. Human Computer Interaction: Concepts, Models, and Methodologies.

This course explores advanced methods for designing, implementing, and evaluating interaction techniques in computing systems. Topics include input method design, performance evaluation, components of the interaction pipeline, and hardware and software considerations. The course also examines usability, privacy, and the application of artificial intelligence methods in human-computer interaction. Students develop and evaluate interaction techniques through implementation and testing, with attention to contextual and behavioral factors that influence system performance.

CS 7323. Image Processing and Computer Vision.

This course introduces fundamental and advanced concepts in image processing and computer vision. Topics include image formation, filtering, frequency domain, enhancement, feature extraction, segmentation, and object recognition. The course also covers modern approaches based on machine learning and deep learning for visual data analysis, with applications in areas such as autonomous systems, robotics, and video analytics. Emphasis is placed on both theoretical foundations and practical implementation of algorithms for analyzing and interpreting images and videos.

CS 7324. Advanced Multimedia Systems: Perception, Quality, and Immersive Media.

This course explores the foundations of modern multimedia systems with an emphasis on end-to-end Quality of Experience (QoE) in digital content delivery. Students study advanced methods for the representation, compression, processing, and transmission of multimedia, including immersive media such as augmented and virtual reality media. A central focus is the modeling and evaluation of user-perceived quality through principles from perceptual psychology and cognitive science. Topics include subjective quality assessment methodologies, objective quality metrics, and the analysis of system factors such as latency, jitter, and resolution, particularly in immersive environments.

CS 7331. High-Performance Computing.

This course examines the advanced design, analysis, and optimization of high-performance applications on modern computing systems. It covers key topics such as high-performance architectures (including accelerators and systems-on-chip), performance modeling and benchmarking, data and control dependence analysis, data locality, memory hierarchy management, techniques for exposing parallelism, and code transformations across diverse workloads. The course integrates theoretical foundations with analysis of applications and systems to address performance across hardware and software layers.

CS 7332. Advanced Parallel Computing.

This course explores advanced methods for designing, implementing, and evaluating parallel algorithms for shared-memory systems, including GPUs and multicore CPUs. Topics include algorithm design, performance optimization, parallelization techniques, parallel hardware, programming models, and language support for parallel programming. The course covers OpenMP, CUDA, HIP, synchronization primitives, amorphous data parallelism, prefix scans, cache coherence, memory consistency, implementation styles, and case studies of parallelizing complex algorithms. The course includes development and evaluation of parallel software for contemporary platforms, with emphasis on performance profiling and program instrumentation.

CS 7333. Advanced Green Computing.

This course covers advanced Ph.D.-level topics in green computing. Topics include hardware techniques for energy efficiency, software design approaches related to energy use, and methods for analyzing AI systems in relation to energy and resource consumption. The course examines data center efficiency, resource management, and scheduling strategies under energy and carbon-related constraints. It includes analysis of current research literature on energy-efficient computing systems. Students conduct research activities, including power measurement, profiling, and evaluation of computing systems using established methods.

CS 7334. Scalable High Performance Computing Systems.

This course introduces the principles and practice of developing scalable applications for high performance computing (HPC) systems, with an emphasis on distributed infrastructures. It covers distributed-memory parallel computing through message-passing paradigms, including communication, parallel I/O, and data access to storage systems. The course examines system-level abstractions such as parallel file systems and resilience mechanisms, including checkpointing, in relation to performance, scalability, and reliability. It includes analysis of research literature across application domains and the use of performance analysis tools to measure and model application behavior on large-scale computing platforms.

CS 7341. Cyberspace Security.

This course examines advanced principles, research methodologies, and emerging challenges in securing computing systems. Students analyze research literature that established core paradigms, including security models, cryptography, systems security, network security, privacy, and adversarial machine learning. The course emphasizes threat modeling, formal reasoning, and experimental evaluation. Through seminar discussions and a research project, students engage with ethical and societal considerations related to cybersecurity technologies and defenses.

CS 7343. Mobile Networks and Computing.

This course offers an in‑depth exploration of modern wireless and mobile communication networks, emphasizing both foundational principles and emerging technologies. Students examine wireless network measurements and modeling, channel assignment strategies, coverage planning, and the design of wireless network protocols. The course also addresses mobile data management and essential wireless security mechanisms. Applications across diverse wireless environments—such as ad hoc networks, sensor networks, delay‑tolerant networks, and mobile social networks—are studied to illustrate real‑world challenges and innovations in contemporary wireless systems.

CS 7344. Cryptography & Machine Learning for Cyber-Physical Systems Security.

This course introduces the fundamentals of cryptography and machine learning as applied to security and privacy in cyber-physical systems (CPS). Topics include CPS architectures, cryptographic techniques, machine learning algorithms, and security threats and attacks on CPS. The course also covers privacy-preserving machine learning methods and design principles for secure CPS. It examines how cryptographic and machine learning approaches are applied within CPS environments.

CS 7351. Advanced Software Engineering.

This course examines advanced concepts and techniques in software engineering, with an emphasis on automated software generation, analysis, and verification. Topics include software process programming, symbolic execution, model checking, property specification and checking, and runtime verification of complex software systems. The course also considers emerging directions at the intersection of software engineering and artificial intelligence, including software engineering for AI systems and the use of AI techniques to support software development, testing, and maintenance. Students analyze research-driven methods, evaluate their strengths and limitations, and apply formal and automated approaches to improve the reliability, quality, and maintainability of modern software systems.

CS 7352. Real-time Systems.

This course covers issues related to the design and analysis of systems with real-time constraints. It examines scheduling and synchronization mechanisms used to manage computing resources under timing requirements. Topics include real-time scheduling algorithms and synchronization protocols, along with analysis of research literature in real-time systems. The course addresses theoretical and practical aspects of ensuring temporal correctness in computing systems.

CS 7387. Research in Computer Science.

This course provides a faculty-guided independent research experience for doctoral students in computer science. Students conduct an in-depth investigation of a focused research topic, including evaluation of scholarly literature, formulation of research questions, and application of research methodologies. The course includes independent study and analysis related to a defined research area. Students present the results of their work in a formal public presentation. Prerequisite: Instructor approval.

CS 7389F. Secure Cyber-Physical Systems: Cryptography and Machine Learning.

This course is designed to introduce students to the fundamentals of cryptography and machine learning and how they can be used to ensure security and privacy in cyber-physical systems (CPS). Topics will include an overview of cyber-physical systems, cryptographic techniques, machine learning algorithms, and security threats and attacks on CPS. The course will also cover privacy-preserving machine learning techniques and design principles for secure CPS. Students who successfully complete this course will be well-versed in cryptography and machine learning approaches for cybersecurity in CPS and be able to use these techniques to address practical real-world issues.

CS 7389H. Advanced Deep Learning.

This course provides an in-depth exploration of deep learning, emphasizing multi-layer neural networks and their applications. Topics include convolutional, recurrent, and graph neural networks, optimization algorithms, and generative models. The course examines mathematical and computational methods for analyzing datasets in areas such as computer vision, natural language processing, audio analysis, and reinforcement learning. It includes design, implementation, and evaluation of deep learning architectures using contemporary frameworks.

CS 7389I. Extended Reality and Immersive User Interfaces.

This course provides an overview of extended reality (XR) technologies, software systems, immersive user interfaces, and spatial interaction techniques. Topics include the geometry of immersive interfaces, motion and physics in virtual environments, human visual perception, and design considerations for XR systems. The course also covers XR development across heterogeneous hardware platforms, 3D user interface prototyping, and methods for evaluating immersive systems using quantitative and qualitative approaches. It integrates concepts from computer graphics, human-computer interaction, and cognitive science within a multidisciplinary framework.

CS 7389J. Advanced Natural Language Processing.

This course examines core concepts, tasks, and techniques in contemporary Natural Language Processing, with emphasis on neural network–based approaches and large language models. Topics include text classification, multimodal modeling, and computational approaches to human behavior. Students analyze foundational principles, modern architectures, and applications across a range of NLP tasks. The course also addresses methods for collecting and annotating text data, as well as representations of linguistic structure. Emphasis is placed on the interplay among data, model design, and evaluation in current NLP systems.

CS 7389K. Advanced Robotics and Autonomous Systems.

This course examines advanced algorithms and methodologies used in contemporary robotics and autonomous systems research. Topics include motion control, state estimation using Kalman and particle filters, localization, computer vision, object detection, task and motion planning, deep reinforcement learning, and multirobot coordination. Students analyze and implement these techniques within robotic software frameworks and evaluate their performance in representative application domains such as autonomous vehicles and mobile robots. Emphasis is placed on understanding underlying mathematical models, algorithmic trade‑offs, and research methodologies relevant to advanced robotics systems.

CS 7399. Dissertation.

This course provides enrollment for doctoral candidates engaged in dissertation research and writing in computer science. Students work under the supervision of a dissertation advisor and conduct activities such as research planning, experimental or theoretical investigation, algorithm or system development, and preparation of dissertation chapters. Enrollment may be maintained during periods of active research or writing. Candidates employ research methods appropriate to their specialization and disciplinary standards. The course includes documentation of research findings and preparation of written materials in accordance with program and Graduate College requirements. Prerequisite: Instructor approval.

CS 7599. Dissertation.

This course provides enrollment for doctoral candidates engaged in dissertation research and writing in computer science. Students work under the supervision of a dissertation advisor and conduct activities such as research planning, experimental or theoretical investigation, algorithm or system development, and preparation of dissertation chapters. Enrollment may be maintained during periods of active research or writing. Candidates employ research methods appropriate to their specialization and disciplinary standards. The course includes documentation of research findings and preparation of written materials in accordance with program and Graduate College requirements. Prerequisite: Instructor approval.

CS 7699. Dissertation.

This course provides enrollment for doctoral candidates engaged in dissertation research and writing in computer science. Students work under the supervision of a dissertation advisor and conduct activities such as research planning, experimental or theoretical investigation, algorithm or system development, and preparation of dissertation chapters. Enrollment may be maintained during periods of active research or writing. Candidates employ research methods appropriate to their specialization and disciplinary standards. The course includes documentation of research findings and preparation of written materials in accordance with program and Graduate College requirements. Prerequisite: Instructor approval.

CS 7999. Dissertation.

This course provides enrollment for doctoral candidates engaged in dissertation research and writing in computer science. Students work under the supervision of a dissertation advisor and conduct activities such as research planning, experimental or theoretical investigation, algorithm or system development, and preparation of dissertation chapters. Enrollment may be maintained during periods of active research or writing. Candidates employ research methods appropriate to their specialization and disciplinary standards. The course includes documentation of research findings and preparation of written materials in accordance with program and Graduate College requirements. Prerequisite: Instructor approval.

MSEC 7100. Doctoral Assistant Development.

This course examines the roles, responsibilities, and professional practices associated with serving as a doctoral teaching assistant. Course focus rotates among three core themes: (1) classroom management and instructional support practices, (2) research‑informed teaching methods, learning objectives, and assessment strategies, and (3) teaching and research integrity, including the responsible conduct of research as defined by federal agencies such as NSF, NIH, and USDA. The course also addresses institutional policies, ethical considerations, and professional expectations relevant to supporting instruction in undergraduate and graduate settings. This course does not earn graduate degree credit.

MSEC 7101. Commercialization Forum.

This course introduces students to the principles and practices of innovation translation, intellectual property management, technology transfer, and business development in science and engineering. Students engage with entrepreneurs, licensing professionals, and commercialization experts to explore how discoveries move from the laboratory to real-world applications. Topics include patenting strategies, startup formation, licensing agreements, funding mechanisms, and market assessment. Emphasis is placed on integrating technical knowledge with entrepreneurial and managerial decision-making to evaluate and advance emerging technologies in academic, industrial, and commercial settings. Repeatable two times for credit.

MSEC 7102. MSEC Seminar.

This course exposes students to current research topics and technical challenges in materials science and engineering through a weekly seminar series featuring speakers from academia, industry, and government. Students critically examine emerging research, analyze scientific methodologies, and discuss implications for materials science practice and innovation. The course emphasizes the development of professional skills, including scientific communication, research critique, and engagement with experts, preparing students to integrate insights from cutting-edge research into their dissertation work, interdisciplinary collaborations, and future careers in science and engineering. Repeatable two times for credit.

MSEC 7103. Research in Materials Science, Engineering, and Commercialization.

This course provides doctoral students in Materials Science, Engineering, and Commercialization with structured research experience prior to advancement to candidacy. Under the supervision of a PhD research advisor, students examine research problems relevant to their field and engage in scholarly inquiry supporting the development of a dissertation research agenda. The course emphasizes research planning, literature analysis, methodological development, and preliminary data collection and interpretation. Students evaluate research progress and refine research questions in preparation for the doctoral candidacy examination. This course is repeatable for doctoral credit across MSEC 7103, 7203, and 7303 for a total of up to six credit hours.

MSEC 7199. Dissertation.

This course supports the completion of original, independent research in materials science, engineering, and commercialization under the direct supervision of the student’s PhD research advisor. Students engage in the development, execution, and documentation of doctoral-level research that contributes new knowledge to the materials science, engineering, and commercialization discipline. Continuous enrollment during long semesters ensures sustained scholarly progress, faculty mentorship, and academic oversight throughout the dissertation research and writing process. This course is a required component of the PhD with a major in materials science, engineering, and commercialization.

MSEC 7203. Research in Materials Science, Engineering, and Commercialization.

This course provides doctoral students in Materials Science, Engineering, and Commercialization with structured research experience prior to advancement to candidacy. Under the supervision of a PhD research advisor, students examine research problems relevant to their field and engage in scholarly inquiry supporting the development of a dissertation research agenda. The course emphasizes research planning, literature analysis, methodological development, and preliminary data collection and interpretation. Students evaluate research progress and refine research questions in preparation for the doctoral candidacy examination. This course is repeatable for doctoral credit across MSEC 7103, 7203, and 7303 for a total of up to six credit hours.

MSEC 7299. Dissertation.

This course supports the completion of original, independent research in materials science, engineering, and commercialization under the direct supervision of the student’s PhD research advisor. Students engage in the development, execution, and documentation of doctoral-level research that contributes new knowledge to the materials science, engineering, and commercialization discipline. Continuous enrollment during long semesters ensures sustained scholarly progress, faculty mentorship, and academic oversight throughout the dissertation research and writing process. This course is a required component of the PhD with a major in materials science, engineering, and commercialization.

MSEC 7301. Practical Skills in Commercialization and Entrepreneurship.

This course analyzes core principles underlying the commercialization of innovation as the first component of a two-part series. Students evaluate intellectual property regimes, technology transfer mechanisms, licensing approaches, capital formation strategies, governance structures, project management systems, and statistical process control methodologies. Using business plan development as an integrative analytical tool, participants examine strategic alignment, financial feasibility, and operational scalability. The course prioritizes systematic inquiry, application of quantitative and qualitative frameworks, and critical evaluation of commercialization pathways across institutional and entrepreneurial environments.

MSEC 7302. Leadership Skills in Commercialization and Entrepreneurship.

This course analyzes the processes involved in commercializing technology-driven ventures within a structured business planning framework. Students evaluate intellectual property regimes, licensing mechanisms, capital formation strategies, governance models, project management methodologies, and statistical approaches to quality and process control. Using applied exercises and comparative case studies, participants examine how legal, financial, and operational variables influence venture design and scalability. The course emphasizes critical assessment of commercialization strategies and the integration of multidisciplinary tools to support evidence-based business decision-making. Prerequisite: MSEC 7301 with a grade of "B" or better.

MSEC 7303. Research in Materials Science, Engineering, and Commercialization.

This course provides doctoral students in Materials Science, Engineering, and Commercialization with structured research experience prior to advancement to candidacy. Under the supervision of a PhD research advisor, students examine research problems relevant to their field and engage in scholarly inquiry supporting the development of a dissertation research agenda. The course emphasizes research planning, literature analysis, methodological development, and preliminary data collection and interpretation. Students evaluate research progress and refine research questions in preparation for the doctoral candidacy examination. This course is repeatable for doctoral credit across MSEC 7103, 7203, and 7303 for a total of up to six credit hours.

MSEC 7304. Collaborative Research/Commercialization Experience.

This course allows Ph.D. level graduate students to initiate, conduct, and participate in a collaborative research or commercialization experience with graduate faculty, either internally or externally, in addition to research conducted under MSEC 7103, MSEC 7303, MSEC 7199, and MSEC 7399. This course recognizes the collaborative nature of the scientific investigation and commercialization enterprise and is designed to support meaningful research engagement under the guidance of a dissertation chair and a collaborating mentor. Repeatable for doctoral credit up to 6 hours.

MSEC 7310. Nanoscale Systems and Devices.

This course provides an in-depth examination of physical phenomena governing nanoscale systems and their implications for device performance. Topics include electronic, photonic, and mechanical behavior in nanoscale structures, as well as transport, confinement, and surface effects unique to reduced dimensions. Applications span nanoelectronic devices, biomedical systems, micro- and nanoscale manipulation, adaptive optics, and microfluidic technologies. Emphasis is placed on linking fundamental nanoscale physics to device design, functionality, and performance, and on analyzing how material properties and structure influence behavior in advanced nanoscale systems.

MSEC 7311. Materials Characterization.

This course provides a comprehensive introduction to advanced materials characterization techniques used to analyze structure, composition, and properties across multiple length scales. Topics include electron microscopy methods such as transmission electron microscopy (TEM), scanning electron microscopy (SEM), scanning probe techniques including scanning tunneling microscopy (STM) and atomic force microscopy (AFM), and optical methods such as confocal microscopy. Diffraction-based techniques, including X-ray and neutron diffraction, are also covered, with emphasis on structure determination, phase identification, texture analysis, and small-angle scattering. Emphasis is placed on interpreting characterization data and relating measurements to material structure and performance.

MSEC 7315. Quantum Mechanics for Materials Scientists.

This course provides a quantum-mechanical foundation for the study of materials at the nanometer and atomic scales. Topics include core principles of quantum physics; stationary states of one-dimensional model potentials; symmetry considerations; interactions between matter and electromagnetic radiation; scattering and reaction rate theory; spectroscopy; chemical bonding and molecular orbital theory; quantum descriptions of solids; perturbation theory; and nuclear magnetic resonance. Emphasis is placed on applying quantum-mechanical concepts to the analysis and interpretation of material structure, properties, and characterization techniques relevant to advanced materials research.

MSEC 7320. Nanocomposites.

This course examines the structure, processing, and properties of nanocomposite materials. Topics include the characteristics of nanoparticles used in nanocomposites; surface modification techniques; methods for nanoparticle dispersion and nanocomposite fabrication; major classes of nanocomposites; structure–property relationships; analytical methods for composite characterization; and representative engineering applications. Emphasis is placed on the scientific principles and theoretical models that explain the unique mechanical, thermal, electrical, and functional behaviors of nanocomposite systems. Students will evaluate processing–structure–property relationships and interpret characterization data relevant to research and development of advanced multifunctional materials.

MSEC 7325. Principles of Technical Project Management.

This course provides technical project management principles to effectively plan, lead, and manage a complex technical project. The content of the course includes understanding of project roles and responsibilities, project life cycles and processes, and project management planning, including scope, cost, quality, schedule, and risks. Students will develop a project management plan for an independent technical project. The course content is designed to prepare students for certification in project management from the Project Management Institute.

MSEC 7330. Computational Material Science.

This course introduces computational approaches used to model and predict the structure and properties of materials across multiple length scales. Topics include quantum-mechanical modeling and density functional theory; force-field-based atomistic simulations; energy minimization and molecular dynamics; mesoscale modeling methods; and prediction of thermodynamic, structural, vibrational, magnetic, and electrical properties. Students examine crystal structures, phase equilibria, and electronic structure using modern computational tools and interpret simulation results in the context of experimental observations. Emphasis is placed on applying computational methods to support materials design, characterization, and dissertation-level research.

MSEC 7340. Biomaterials and Biosensors.

This course provides an in depth examination of the design, function, and performance of biomaterials and biosensors used in biomedical applications. Students explore material properties, physiological responses, transduction mechanisms, and fabrication approaches involved in creating clinically relevant devices. The course integrates analysis of polymers, hydrogels, nanomaterials, and inorganic materials with applications in drug delivery, tissue engineering, medical diagnostics, and sensing. Through lectures, discussions, and independent research activities, students will develop the ability to evaluate biomaterial systems, interpret performance criteria, and understand regulatory, ethical, and translational considerations in biomedical device development.

MSEC 7350. Frontiers of Nanoelectronics.

This course introduces the operating principles of nanoscale electronic and optoelectronic devices, with emphasis on how reduced dimensions and quantum effects influence device behavior. Topics include quantum confinement in low-dimensional systems such as quantum wells, wires, and dots, as well as molecular and emerging nanoelectronic devices. The course examines how advanced nanofabrication techniques enable these technologies and explores their impact on device performance. Emphasis is placed on linking quantum mechanical phenomena, material properties, and fabrication approaches to the design and analysis of next-generation nanoelectronic systems.

MSEC 7355. Fluid Flow in Porous Media.

This course examines the theory and analysis of fluid transport in heterogeneous porous media. Governing equations for fluid flow and mass transport are developed and applied using analytical and numerical solution methods to predict flow behavior and transport processes. Applications include natural and engineered porous systems such as soils, rocks, concrete, and biological materials. Emphasis is placed on interpreting flow fields, permeability, and transport mechanisms and on using porous media principles to analyze, design, and optimize materials and systems relevant to materials science and engineering research.

MSEC 7360. Nanomaterials Processing.

This course examines the processing and fabrication of nanomaterials and semiconductor devices, with emphasis on nanoscale phenomena and manufacturing techniques. Topics include properties of electronic materials, thin film deposition methods, etching processes, lithography, and related device physics. Students are introduced to fabrication workflows and characterization techniques used in nanomanufacturing environments, including cleanroom practices. Emphasis is placed on understanding how processing conditions influence material structure, properties, and device performance, and on integrating fabrication and characterization approaches to support research and development of nanoscale systems. Prerequisite: MSEC 7401 with a grade of "C" or better.

MSEC 7370. Advanced Polymer Science.

This course examines advanced topics in polymer science with emphasis on polymer processing and characterization, testing, and applications. Topics include shape memory polymers, polymer lithography, nano and microfabrication, polymer additives, reactions of polymers, high-temperature polymers, polymers in biomedical applications, natural polymers, and electroactive polymers. Emphasis is placed on understanding the molecular and microstructural mechanisms that govern polymer performance and on analyzing structure–processing–property relationships relevant to advanced engineering applications.

MSEC 7375. Structure and Properties of Alloys.

This course provides an advanced examination of engineering alloys, focusing on their structures, properties, and strengthening mechanisms across ferrous, nonferrous, and emerging alloy systems. The course also examines how processing conditions influence microstructure, performance, and mechanical behavior. Emphasis is placed on the analytical evaluation of alloy systems through metallurgical principles, phase transformations, and application-driven examples. Topics include equilibrium and non-equilibrium transformation products, alloy design considerations, and relationships among composition, processing, microstructure, and material properties in advanced engineering applications.

MSEC 7380. Advanced Infrastructure Materials.

This course examines advanced infrastructure materials used in civil engineering, including cement concrete, asphalt concrete, wood, and steel. The course analyzes the composition of cement concrete with a focus on how raw ingredients influence fresh and hardened material properties. Additional infrastructure materials are evaluated through comparative discussion to highlight differences in behavior and application. Students apply analytical reasoning to infrastructure materials–related problems using advanced analytical and simulation tools. Emphasis is placed on understanding material behavior through data interpretation, modeling, and quantitative analysis.

MSEC 7395B. Thin Film Photovoltaic Devices.

This course examines the materials science and device physics underlying photovoltaic energy conversion, with emphasis on thin film solar cell technologies. Topics include the photovoltaic effect, photon absorption, carrier generation and recombination, electron and hole transport, pn-junction behavior, and charge separation mechanisms. Students study monocrystalline, thin film, and III–V photovoltaic materials and analyze performance losses and efficiency limitations. Emphasis is placed on connecting material structure and electronic properties to device performance and on interpreting experimental characterization and performance metrics relevant to modern photovoltaic research and development. Prerequisite: MSEC 7401 and MSEC 7402 with grades of "B" or better.

MSEC 7395D. Polymer Characterization and Processing.

This course examines polymeric materials which are widely used in structural, electronic, biomedical, and energy applications. Their performance depends strongly on molecular structure, processing conditions, and resulting microstructure. The course provides doctoral students with the fundamental knowledge and analytical tools required to characterize polymer structure and properties and to understand how processing methods influence material behavior. By integrating characterization techniques—such as molecular weight analysis, thermo-mechanical testing, X-ray scattering, and spectroscopy—with polymer rheology and processing methods, the course prepares students to analyze structure–processing–property relationships in polymer systems. The course supports dissertation research involving polymeric and composite materials and strengthens interdisciplinary training in advanced materials characterization and manufacturing. Prerequisite: MSEC 7370 with a grade of "B" or better.

MSEC 7395H. Environmental Chemistry.

This course provides an advanced study of environmental chemistry with emphasis on aquatic systems and applications in materials science and engineering. Topics include principles of geochemistry and atmospheric chemistry as they relate to environmental processes, pollutant behavior, and monitoring and control strategies. The course also examines the principles and applications of green chemistry in the design of sustainable materials, products, and processes. Emphasis is placed on understanding chemical transformations in natural and engineered systems and applying this knowledge to address environmental challenges relevant to materials research and development.

MSEC 7395I. Structure and Properties of Alloys.

This course in an advanced exploration of the structure and properties of engineering alloys. Strengthening mechanisms of alloys are explored with specific applications to the alloys studied. The processing, properties, and structure of ferrous and nonferrous alloys are explored including new and emerging alloys. Prerequisite: Instructor approval.

MSEC 7395J. Advanced Concrete Materials and Durability.

This course examines Portland cement concrete materials and alternative material systems used in building and transportation infrastructure. Students analyze the physical, chemical, and mechanical properties of cement, aggregates, and chemical and mineral admixtures. Topics include mixture proportioning, concrete microstructure, durability mechanisms, long-term performance, dimensional stability, and deterioration processes. The course evaluates durability prediction methods, modeling approaches, and multi-scale assessment techniques. Alternative cementitious systems are studied through comparative analysis of material behavior and performance under different exposure conditions. Emphasis is placed on understanding material selection, testing methodologies, and performance-based evaluation.

MSEC 7395M. Semiconductor Devices and Processing.

This course examines the principles and processes underlying semiconductor device fabrication, with emphasis on both silicon and compound semiconductor systems. Topics include carrier transport, doping mechanisms, and defect engineering, as well as fabrication techniques such as photolithography, etching, ion implantation, and epitaxial growth. Students study the formation of junctions and microstructures required for micro- and nanoscale devices, along with Ohmic contacts and device integration strategies. Laboratory projects and seminar presentations provide experience in applying fabrication concepts and interpreting device performance in conventional and emerging electronic and optoelectronic systems. Prerequisite: MSEC 7401 with a grade of "B" or better.

MSEC 7395O. Modern Concepts in Materials Science.

This course provides an overview of fundamental concepts used to describe and predict the structure and properties of engineering materials. Topics include atomic structure and bonding, crystallography, diffraction principles, defects, solid solutions, and phase equilibria. Emphasis is placed on understanding structure–property relationships across major classes of materials, including metals, ceramics, polymers, electronic materials, and composites. The course prepares students to apply core materials science principles to analyze material behavior and supports those without prior formal training in materials science in advancing to graduate-level coursework and research.

MSEC 7395P. Optical Properties of Solids.

This course examines the optical properties of solid materials, including electronic and vibrational transitions in inorganic and organic systems, thin films, and multilayer structures. Topics include interactions among electrons, phonons, and photons, and their influence on optical behavior. Students study optical characterization techniques such as UV/Vis spectroscopy, Raman spectroscopy, Fourier transform infrared (FTIR) spectroscopy, ellipsometry, photoluminescence, and X-ray fluorescence. Emphasis is placed on interpreting optical spectra to determine material properties and on applying spectroscopic methods to analyze and optimize materials for electronic and optoelectronic applications.

MSEC 7395Q. Scanning Probe Microscopy and Nanoscience.

This course introduces fundamental topics in nanoscience, including nanomechanics, nanoelectronics, and nano-optics, using scanning probe microscopy (SPM) as a central analytical tool for studying materials at the nanoscale. Students examine the physical principles underlying major SPM techniques and explore how these methods are applied to measure structural, electrical, and optical properties of nanostructures. The course also covers instrumentation design, signal acquisition, and data interpretation, providing students with both theoretical understanding and practical familiarity with SPM operation relevant to research in nanoscience and nanotechnology.

MSEC 7399. Dissertation.

This course supports the completion of original, independent research in materials science, engineering, and commercialization under the direct supervision of the student’s PhD research advisor. Students engage in the development, execution, and documentation of doctoral-level research that contributes new knowledge to the materials science, engineering, and commercialization discipline. Continuous enrollment during long semesters ensures sustained scholarly progress, faculty mentorship, and academic oversight throughout the dissertation research and writing process. This course is a required component of the PhD with a major in materials science, engineering, and commercialization.

MSEC 7401. Fundamentals of Material Science and Engineering.

This course provides a comprehensive foundation in the fundamental principles of materials science and engineering. Topics include atomic and electronic structure, crystallography, defects, thermodynamics and kinetics, phase diagrams, diffusion, and phase transformations. Additional topics include conservation laws, continuum mechanics, and statistical models relevant to materials behavior. Emphasis is placed on understanding the relationships among structure, processing, and properties in materials systems and on applying fundamental principles to analyze and predict material behavior in engineering applications.

MSEC 7402. Advanced Materials Science and Engineering Concepts.

This course builds on fundamental materials science principles to examine advanced concepts governing the behavior of materials. Topics include quantum mechanical foundations of solids, electronic structure, lattice vibrations, magnetism, semiconductors, nanostructures, mesoscopic phenomena, and superconductivity. The course also explores recent advances in emerging materials systems. Emphasis is placed on understanding how quantum and solid-state physics principles influence material properties and functionality, particularly in electronic, photonic, and advanced materials applications. Prerequisite: MSEC 7401 with a grade of "C" or better.

MSEC 7599. Dissertation.

This course supports the completion of original, independent research in materials science, engineering, and commercialization under the direct supervision of the student’s PhD research advisor. Students engage in the development, execution, and documentation of doctoral-level research that contributes new knowledge to the materials science, engineering, and commercialization discipline. Continuous enrollment during long semesters ensures sustained scholarly progress, faculty mentorship, and academic oversight throughout the dissertation research and writing process. This course is a required component of the PhD with a major in materials science, engineering, and commercialization.

MSEC 7699. Dissertation.

This course supports the completion of original, independent research in materials science, engineering, and commercialization under the direct supervision of the student’s PhD research advisor. Students engage in the development, execution, and documentation of doctoral-level research that contributes new knowledge to the materials science, engineering, and commercialization discipline. Continuous enrollment during long semesters ensures sustained scholarly progress, faculty mentorship, and academic oversight throughout the dissertation research and writing process. This course is a required component of the PhD with a major in materials science, engineering, and commercialization.

MSEC 7999. Dissertation.

This course supports the completion of original, independent research in materials science, engineering, and commercialization under the direct supervision of the student’s PhD research advisor. Students engage in the development, execution, and documentation of doctoral-level research that contributes new knowledge to the materials science, engineering, and commercialization discipline. Continuous enrollment during long semesters ensures sustained scholarly progress, faculty mentorship, and academic oversight throughout the dissertation research and writing process. This course is a required component of the PhD with a major in materials science, engineering, and commercialization.

BIO 7100. Professional Development.

This course develops professional skills relevant to Biology graduate training and scientific careers. It examines career pathways, professional communication practices, and competencies required for academic and non-academic contexts. Instruction is delivered through structured modules, discussions, and applied assignments that analyze professional scenarios and workforce expectations, including evaluation of professional documents, communication strategies, ethical considerations, and data presentation conventions across diverse scientific and organizational settings. The course includes analysis of qualifications, career pathways, and professional development planning based on disciplinary standards, regulatory frameworks, and employment requirements.

BIO 7102. SEMINAR IN INTEGRATIVE AND APPLIED BIOLOGY.

This course examines current topics and emerging research through focused study of primary scientific literature. Topics vary by offering and address applications relevant to biological systems at multiple levels of organization, including discipline-based education research and human dimensions of biological sciences. Instruction includes student-led discussions, presentations, and critical review of published studies, with emphasis on research design, interpretation, and significance. The course focuses on synthesis of evidence, evaluation of complex issues, and communication of scholarly analyses of contemporary research questions.

BIO 7103D. Molecular Biology of the Cell.

Interactive discussion of current literature on molecular biology of the cell. The course is designed to discuss concepts and their applications and methodology associated with the structure and function of the cell at cellular and molecular level.

BIO 7103F. Molecular Genetics of Plant Development.

The study of plant development is rapidly changing as plant genome projects discover a multitude of new genes, and their expression and interaction patterns are understood. This course is designed to discuss concepts in plant development, and developmental processes as pathways of gene regulatory activities.

BIO 7104. Marine Pollution.

This course focuses on the sources, bioaccumulation, trophic transfer, and health effects of contaminants in the marine environment. Contaminants to be reviewed include trace elements, polycyclic aromatic hydrocarbons (PAHs), oil, pesticides, radionuclides, per- and polyfluoroalkyl substances (PFAS), plastics, pharmaceuticals, illegal drugs, and personal care products. Students read and critically evaluate peer-reviewed scientific papers that address a variety of marine life including plankton, crustaceans, mollusks, fishes, marine mammals, turtles, and birds. Students lead and participate in discussions and make recommendations for future research.

BIO 7105. Environmental Issues through Documentaries.

This course will examine how environmental issues are addressed in documentaries with an emphasis on critically evaluating each for scientific content, imagery, biases, misconceptions perpetuated or depicted, and ease of understanding. Students will watch a curated list of documentaries covering topics such as overfishing, wildlife trade, habitat degradation, pollution, energy resources, climate change, sustainability, and conservation. Students will gain the skills to review and analyze documentary content for scientific content and messaging, making recommendations to improve the medium.

BIO 7106. Molecular Biology of the Cell.

This course examines current literature in molecular and cellular biology through interactive seminar discussions, emphasizing critical analysis of concepts, methodologies, and applications for understanding cell structure and function at the molecular level. Students engage with research resources and explore emerging discoveries in molecular biology. Topics vary each semester to reflect advances in the field, allowing students to repeat the course for credit.

BIO 7107. Molecular Genetics of Plant Development.

This course engages students in interactive seminar discussions of current literature on molecular genetics of plant development. Emphasis is placed on understanding developmental processes as gene regulatory pathways and exploring how plant genome projects have advanced this field. Students critically analyze research methodologies and findings, considering implications for developmental biology. Topics vary each semester to reflect emerging discoveries, and the course may be repeated for credit.

BIO 7114. Collaborative Research.

This course provides Ph.D.‑level graduate students with structured opportunities to engage in collaborative biological research with graduate faculty in the Department of Biology. Research conducted in this course may be distinct from and supplemental to research completed under BIO 7303, BIO 7399A, or BIO 7699A. Emphasis is placed on collaborative scientific inquiry, including project development, experimental or analytical work, and scholarly interpretation within a faculty‑guided research environment. The course recognizes the collaborative nature of scientific investigation and supports advanced research skill development across diverse areas of biological study. Specific research topics and methods vary by faculty mentor and semester.

BIO 7120. Population Biology Seminar.

This course provides graduate students with a comprehensive introduction to specialized topics in population and conservation biology. Topics include contemporary issues in evolution, ecology, genetics, environmental policy, and conservation. Students examine primary scientific literature related to selected topics each semester. Instruction emphasizes critical analysis of scientific literature, comparative evaluation of research approaches, and application of quantitative and conceptual frameworks. Students evaluate and synthesize contemporary research findings to construct evidence-based conclusions about specialized topics in population and conservation biology.

BIO 7199A. Dissertation.

This course represents doctoral‑level enrollment for original dissertation research and writing in Integrative and Applied Biology conducted under the direct supervision of the dissertation advisor. Students engage in independent, sustained scholarly inquiry, including research design, data collection or analysis, interpretation of findings, and preparation of the dissertation. Continuous enrollment is required during each long semester in which dissertation research or writing is conducted to ensure ongoing faculty supervision and academic oversight. This course supports the completion of advanced research that contributes to the scientific understanding of biological systems and resources and fulfills doctoral dissertation requirements.

BIO 7214. Collaborative Research.

This course provides Ph.D.‑level graduate students with structured opportunities to engage in collaborative biological research with graduate faculty in the Department of Biology. Research conducted in this course may be distinct from and supplemental to research completed under BIO 7303, BIO 7399A, or BIO 7699A. Emphasis is placed on collaborative scientific inquiry, including project development, experimental or analytical work, and scholarly interpretation within a faculty‑guided research environment. The course recognizes the collaborative nature of scientific investigation and supports advanced research skill development across diverse areas of biological study. Specific research topics and methods vary by faculty mentor and semester.

BIO 7299A. Dissertation.

This course represents doctoral‑level enrollment for original dissertation research and writing in Integrative and Applied Biology conducted under the direct supervision of the dissertation advisor. Students engage in independent, sustained scholarly inquiry, including research design, data collection or analysis, interpretation of findings, and preparation of the dissertation. Continuous enrollment is required during each long semester in which dissertation research or writing is conducted to ensure ongoing faculty supervision and academic oversight. This course supports the completion of advanced research that contributes to the scientific understanding of biological systems and resources and fulfills doctoral dissertation requirements.

BIO 7300. Communicating Science.

This course explores how to effectively disseminate scientific research through visualizations, oral presentations, and written works to multiple audience types. The course emphasizes how to alter communication strategies for sharing scientific research with non-specialists, the media, grant-giving agencies, and scientific peers. The interactive, student-centered course includes multiple interactive opportunities to present research outcomes, reflect on jargon usage, grow verbal and nonverbal communication skills, provide constructive feedback, and integrate advice from others to enhance the impact of communicating scientific research.

BIO 7301. College Science Teaching.

This course provides graduate students in the sciences with a comprehensive foundation in evidence-based pedagogical practices for teaching at the collegiate level. Moving beyond the traditional lecture model, this course explores the intersection of cognitive science and discipline-based education research to answer a central question: How do college students best learn science, and how can we design and provide environments that best facilitate that learning? Students experience, evaluate, and apply research-based approaches to college science instruction while analyzing and designing effective learning environments for different instructional contexts.

BIO 7302. Problems in Aquatic Resources.

This course provides faculty‑supervised study of selected state, national, or international aquatic resource issues. Students investigate a focused problem or topic through directed readings, data analysis, field or laboratory work, or other scholarly activities appropriate to the subject area. Emphasis is placed on independent inquiry, critical evaluation of scientific literature, and methodological rigor. The specific topic, scope, and expected products are determined collaboratively by the student and supervising faculty member and documented at the start of the term. Students may not enroll in BIO 7302 more than twice for doctoral credit without approval of the Graduate Program Director.

BIO 7303. Research.

This course provides structured research enrollment for doctoral students who have not yet passed the Candidacy Examination. Students engage in supervised research activities under the direction of their research or dissertation supervisor while preparing for admission to candidacy. Pre‑candidacy students are required to enroll in this course each semester until candidacy is achieved to maintain formal academic oversight and research continuity. The course may be taken for doctoral credit no more than three times without approval from the Graduate Program Director. This enrollment supports the development of research skills, project refinement, and scholarly progress appropriate to the pre‑candidacy stage of doctoral study.

BIO 7307. Global Change Biology.

This course explores broad patterns of biological change across ecosystems, with attention to how environmental drivers influence ecological structure and function over time and space. Major topics include shifts in atmospheric and climatic conditions, invasive species, nutrient enrichment, land use, and biodiversity change. Students engage extensively with primary research, quantitative evidence, and model-based studies to examine ecological processes at large scales. Emphasis is placed on interpretation, integration, and critical evaluation of scientific information relevant to biological responses under changing environmental conditions.

BIO 7310. Global Aquatic Resources.

This course introduces global, national, and regional aquatic resource issues through comparative analysis of scientific, environmental policy, and socioeconomic perspectives. Students examine water quantity and quality challenges and their underlying causes across diverse geographic regions, with particular emphasis on case studies. The course focuses on understanding how physical, biological, economic, and institutional factors interact to shape aquatic resource conditions and management responses. Students critically analyze empirical evidence, policy frameworks, and regional contexts to develop an informed understanding of aquatic resource variability and complexity worldwide.

BIO 7311. Ecology of Temporary Waters.

This course examines temporary waters—such as ponds, streams, and rainpools that regularly dry and the biodiversity they support of aquatic and terrestrial organisms worldwide. It explores their ecological and social significance, including their role in nutrient fluxes in river networks and for human well-being, particularly in arid and semi-arid regions. Through analysis of scientific literature, case studies, and discussion, students investigate species adaptations, population and community dynamics, and human impacts. By the end, students will be able to explain key processes, synthesize research findings, and articulate the value of temporary waters for conservation and management.

BIO 7314. Collaborative Research.

This course provides Ph.D.‑level graduate students with structured opportunities to engage in collaborative biological research with graduate faculty in the Department of Biology. Research conducted in this course may be distinct from and supplemental to research completed under other research courses. Emphasis is placed on collaborative scientific inquiry, including project development, experimental or analytical work, and scholarly interpretation within a faculty‑guided research environment. Students develop advanced research skills across diverse areas of biological study. Specific research topics and methods vary by faculty mentor and semester.

BIO 7318. Wildlife Policy and Law.

This course examines the historical, legal, and institutional foundations of wildlife policy and law in North America, with emphasis on the United States and Texas. Students analyze federal treaties, statutes, case law, and regulatory frameworks that shape wildlife management and conservation practice. Using comparative and historical approaches, the course investigates how local, national, and international policy instruments structure decision‑making and governance in wildlife conservation. Emphasis is placed on developing skills in interpreting statutory language, regulatory guidance, and policy analyses within their legal and institutional contexts. The course is intended for students in wildlife biology and related programs seeking an analytical understanding of wildlife law and policy.

BIO 7324. Natural History and Conservation of Large Mammals.

This course provides a comprehensive study of large mammals, including ecology, behavior, life history, distribution, and evolutionary relationships, with emphasis on sylvan species native to North America including both marine and terrestrial species. Content addresses distinctive anthropogenic factors influencing the management and conservation of this group of mammals, many of which are species of conservation concern. Instruction is delivered through lectures, analysis of scientific literature, and field based activities. Students examine reasons behind endangerment status, evaluate management practices, and apply conservation assessment techniques in scientific contexts.

BIO 7326. Immunobiology.

This course examines the cellular and molecular mechanisms of the immune system and its role in health and disease. Topics include innate and adaptive immunity, B and T cell activation and regulation, antigen processing and presentation, and immunological memory. Students examine immune-mediated diseases, hypersensitivities, autoimmunity, transplantation, and cancer immunology through evaluation of experimental findings and clinical evidence. By integrating molecular concepts with clinical case studies, the course highlights how immune function influences health, disease progression, and therapeutic strategies.

BIO 7327. Ecological Immunology.

This course explores the roles of immunity in natural ecosystems, focusing on central concepts in ecological immunology. It examines interactions among hosts, pathogens, and environmental factors across biological systems, including viruses, parasites, and other disease agents. Topics include foundational and emerging research in ecological immunology, with emphasis on integrating immunological and ecological perspectives. Instruction is based on analysis of primary scientific literature, with attention to research design, data interpretation, and the ecological context of immune function in natural populations.

BIO 7331. Human Dimensions of Wildlife and Fisheries Conservation.

This course examines advanced theoretical and methodological foundations of human dimensions in wildlife and fisheries conservation, emphasizing social, political, economic, and cultural drivers of management outcomes across global contexts. Students critically analyze foundational and emerging scholarship, comparing competing frameworks and evaluating their implications for conservation policy and governance. Through intensive seminar discussion and independent research, students synthesize theory and empirical evidence to generate original insights and scholarly products suitable for professional presentation or publication.

BIO 7332. Introduction to R Programming for Biologists.

This course introduces the programming language R. Emphasis is placed on best practices in programming and the use of Base-R and RStudio. Topics include navigating the R and RStudio environment, installing packages, loading, manipulating, and visualizing data, declaring variables, writing loops, and writing functions. The course will consist of lectures on various aspects of scientific programming followed by an interactive R programming session. By the end of the course, students will be comfortable and proficient in scientific programming in R.

BIO 7333. Phylogenetic Comparative Methods.

This course introduces students to modern phylogenetic comparative methods and provides in-class examples on how to perform them. Topics include constructing phylogenies, dating phylogenies, finding and using previously published phylogenetic datasets, phylogenetic data visualization, and a variety of methods to test ecological and evolutionary hypotheses in a phylogenetic framework. Instruction will consist of a lecture covering methods followed by an active coding session in which these methods are explored. By the end of the course, students will be comfortable with conducting phylogenetic comparative analyses and able to apply them to real-world datasets.

BIO 7336. Evolutionary Ecology.

This course provides a comprehensive introduction to the core concepts in evolutionary biology and applications to ecology and behavior of organisms. Major topics include quantitative methods in evolution, ecology and behavior, biotic interactions, community ecology, ecophysiology, phylogenetic inference and comparative phylogenetics. Instruction emphasizes critical analysis of scientific literature, comparative evaluation of research approaches, and application of quantitative and conceptual frameworks to specific questions in evolutionary ecology. Students examine theoretical and analytical methods and case studies from the primary scientific literature, and synthesize the research findings to construct evidence-based conclusions about specialized topics in evolutionary ecology.

BIO 7342. Virology.

This course examines the structure, replication, and genetics of bacterial and animal viruses with emphasis on molecular mechanisms of infection and disease. Topics include viral structure and assembly, genome replication strategies, host–virus interactions, immune responses, vaccines and unconventional infectious agents such as prions. Through lectures, active learning activities, case studies and literature reviews, students will apply core virology principles to analyze viral replication cycles, compare major virus families, and evaluate mechanisms of viral pathogenesis and prevention.

BIO 7346. Conservation Biology.

This course examines the principles and practice of conservation biology, emphasizing the scientific foundations and interdisciplinary approaches used to maintain biodiversity in changing environments. Students evaluate ecological, genetic, and socio-environmental factors influencing species persistence and ecosystem resilience across local and global scales. Using case studies, quantitative analyses, and current scientific literature, students assess conservation strategies, analyze drivers of biodiversity loss, and compare management approaches across ecosystems. Through these analyses, students develop the ability to evaluate conservation interventions and design evidence-based strategies for protecting biodiversity and sustaining ecological systems.

BIO 7353. Biogeography.

This course provides a comprehensive introduction to the history, core concepts, major questions and methods in biogeography. Topics include ecological biogeography, historical biogeography, the history of ideas in biogeography, earth history, experimental designs and the use of molecular genetics for hypothesis testing. Students examine case studies from the primary scientific literature. Instruction emphasizes critical analysis of scientific literature, comparative evaluation of research approaches, and application of quantitative and conceptual frameworks to specific questions in biogeography. Students evaluate and synthesize contemporary research findings to construct evidence-based conclusions about specialized topics in biogeography.

BIO 7354. Applied Analyses of Populations.

This course provides applied statistical techniques for analyzing biological populations using quantitative and computational approaches. Topics include model selection, parameter estimation, and evaluation of environmental effects on population dynamics, including abundance, occupancy, survival, recruitment, and habitat use. Instruction is delivered through coding exercises, analysis of real-world datasets, and application of statistical models to ecological questions. Students analyze population data, evaluate model performance, and apply statistical methods to estimate parameters and interpret ecological patterns in research and management contexts.

BIO 7360P. Regulation of Plant Growth and Development.

This course examines the physiological, biochemical, and molecular mechanisms that regulate plant growth and development. Topics may include hormonal control, signal transduction pathways, gene regulation, environmental influences, and developmental processes across the plant life cycle. Emphasis is placed on understanding how internal regulatory systems and external factors interact to influence plant form and function. Students engage with current scientific literature and experimental evidence to analyze regulatory mechanisms and developmental outcomes. The course develops advanced conceptual understanding relevant to research and professional work in plant biology and related fields.

BIO 7360R. Community and Ecosystem Ecology.

This course explores community and ecosystem ecology using theoretical approaches and empirical case studies. Lecture topics include biological diversity and its consequences for ecosystem functioning, biotic interactions, food webs, primary production, decomposition, nutrient cycling, and ecological succession. In-class discussions of peer-reviewed literature reinforce and extend these topics. The course emphasizes understanding ecological processes across multiple ecosystem types and spatiotemporal scales, along with critical evaluation of primary scientific literature in community and ecosystem ecology.

BIO 7360T. Karst Hydrogeology and Geomorphology.

This course provides students with both an introduction to and advanced understanding of karst hydrogeology, geology, and geomorphology, with emphasis on field and theoretical applications of this information to the study of karst systems. Central to this will be the recognition and understanding of karst landforms at the Earth’s surface and their relationships with subsurface hydrogeologic and geochemical processes in varied settings. Course materials and field experiences allow a comprehensive examination of karst hydrogeology and geomorphology, emphasizing field and theoretical approaches to understanding karst landforms, subsurface-surface hydrologic linkages, and geochemical processes across scales from pore to watershed.

BIO 7360Y. Applied Bioinformatics.

This course provides an introduction to scripting and other computational techniques used for visualizing and analyzing large biological datasets. Computational techniques include sequence and structural alignment, data mining, phylogenetic tree construction, and data clustering using UNIX, Python, and R. Students will gain a solid foundation in broadly applicable bioinformatics skills.

BIO 7361A. Discipline-Based Educational Research Methods.

This course will expose science graduate students to discipline-based educational research (DBER) in a practical setting, with a focus primarily on qualitative methods and quantitative measures commonly used in DBER involving human subjects. This interactive course will provide students with scaffolded opportunities to practice research skills using real-world data examples as they each work to generate their own rigorous DBER project proposals that comply with Institutional Review Board (IRB) guidelines for the ethical treatment of research participants.

BIO 7361C. Advanced Genomics and Bioinformatics.

This course equips students with the computational skills necessary to process and analyze data generated by contemporary genomics tools. Lectures cover basic and advanced topics in genomics and epigenomics alongside their bioinformatics frameworks, providing the theoretical context needed to interpret complex datasets. Students perform analyses using personal and cluster computing environments, gaining direct experience with workflows and tools used in modern genomic research. Students present their analyzed datasets, fostering critical evaluation and scholarly communication of genomic findings. The hands-on nature of the course ensures that acquired skills are immediately applicable to future research endeavors in genomics and related fields.

BIO 7361H. Professional Research Ethics in Life Sciences.

This course explores the application of ethical conduct in scientific research and research spaces and provides the opportunity to critically analyze and address ethical behavior and associated professional issues. Students develop problem-solving skills related to key ethical dilemmas, including parachute science, interactions with private land, conflict of interest, responsibilities (e.g., mentor/mentee, record keeping, academic integrity), data management (e.g., sharing, fabrication), AI use/misuse, authorship guidelines, protecting one's work (e.g., patents, intellectual property), research subject protections (e.g., human subjects, non-human animal welfare), and additional topics as necessary. This comprehensive approach equips students to navigate the ethical landscape of their professions effectively.

BIO 7377. Applied Bioinformatics.

This course provides an introduction to computing, scripting, and other computational techniques used to process, analyze, and visualize large biological datasets. Large data analysis is an increasingly important component of many biological fields. This course focuses on foundational concepts and skills necessary to conduct common analyses. Topics covered include command line interfaces, regular expressions, common scripting languages, and remote computing, and emphasizes the development of a solid foundation in broadly applicable bioinformatics skills.

BIO 7399A. Dissertation.

This course represents doctoral‑level enrollment for original dissertation research and writing in Integrative and Applied Biology conducted under the direct supervision of the dissertation advisor. Students engage in independent, sustained scholarly inquiry, including research design, data collection or analysis, interpretation of findings, and preparation of the dissertation. Continuous enrollment is required during each long semester in which dissertation research or writing is conducted to ensure ongoing faculty supervision and academic oversight. This course supports the completion of advanced research that contributes to the scientific understanding of biological systems and resources and fulfills doctoral dissertation requirements.

BIO 7402. Molecular Field Techniques.

This course examines the application of molecular tools for identifying, quantifying, and interpreting biological diversity in aquatic and terrestrial systems. Topics include identification of microorganisms, vertebrate genetic systems, experimental design, and integration of molecular data in field-based research. Students apply field logistics and molecular methodologies to analyze genetic datasets derived from environmental sampling. Emphasis is placed on designing rigorous field protocols, evaluating data quality, and synthesizing molecular findings within contemporary ecological and evolutionary frameworks.

BIO 7405. Statistics and Experimental Design I.

This course is an introduction to the basic types of statistical analysis routinely conducted in biological research. Students learn the fundamental philosophy behind null-hypothesis statistical testing, how to think quantitatively about data, and the proper way to make inferences from a statistical test. The course provides a solid understanding of the mechanics and motivation of proper study design, data collection, and statistical analysis. Students learn how to select an appropriate method of analysis to address a well-framed test hypothesis and to be a critical evaluator of statistical analyses applied by others.

BIO 7406. Statistics and Experimental Design II.

This course builds on foundational statistical knowledge to develop advanced analytical competency for biological research. Topics include multiple regression techniques, generalized linear models, analysis of variance designs, model selection approaches, linear mixed effects models, Bayesian statistics, and multivariate statistics. Core statistical principles of randomization, replication, and blocking are examined in the context of planning and designing rigorous scientific studies. Students will be able to conduct analyses using the R statistical computing platform with emphasis on applying appropriate methods to real biological data sets. Prerequisite: BIO 7405 with a grade of "C" or better.

BIO 7410. Aquatic Microbial Ecology.

This course explores the diversity of microbial life, microbial metabolisms, and the basis and consequences of their interactions within their environments. Students will gain the knowledge and tools to investigate the ecology, evolution, and functions of natural microbial populations. Combining theory with hands-on practice, students will apply laboratory and computational techniques to real environmental samples through a semester-long research project, while learning the current conceptual frameworks that shape our understanding of the most diverse forms of life on the planet.

BIO 7412. Environmental Hydrology.

This course examines hydrologic processes that govern the movement, storage, and quality of water in terrestrial and freshwater ecosystems. Students evaluate precipitation, infiltration, runoff, evapotranspiration, groundwater–surface water interactions using quantitative and conceptual approaches. Emphasis is placed on how hydrologic variability influences aquatic habitat, ecosystem function, and biodiversity, as well as how land use and climate change alter these relationships. The laboratory component includes hydrologic data analysis, watershed simulation, GIS-based assessment, and modeling of environmental hydrologic processes.

BIO 7414. Ecology of Infectious Diseases in Wildlife.

This course provides a comprehensive study of the ecological and evolutionary processes that drive the transmission of pathogens, the impact of diseases on host populations, and the emergence of infectious diseases in human and wildlife populations. Content addresses the integration of concepts from community ecology, epidemiology, and evolutionary biology to examine how host-pathogen relationships are shaped by environmental factors. Instruction is delivered through lectures, discussion and analysis of scientific literature, and research driven semester projects. Students examine the drivers of zoonotic diseases, wildlife conservation implications, and public health control strategies.

BIO 7419. Stream Ecology.

This course introduces the structure, function, and ecology of stream ecosystems, which are exceptionally dynamic, complex and diverse ecosystems. It explores the fundamental processes and theoretical concepts of streams and rivers, as well as monitoring approaches. The course combines lectures, scientific literature analysis, and team-based activities to apply ecological concepts in practical settings. By the end of the course, students will be able to explain fundamental stream processes, design and interpret ecological studies, critically evaluate human impacts on watersheds, and communicate ecological information effectively.

BIO 7426. Ecology and Management of Aquatic Macrophytes.

This course examines ecological processes that structure wetland ecosystems, with emphasis on interactions among hydrology, soils, and vegetation. Topics include wetland classification, plant adaptations, community dynamics, nutrient processes, and ecosystem function. It also addresses approaches to management and restoration in wetland systems. Students engage in field and laboratory investigations, quantitative analysis, and evaluation of ecological models to examine wetland processes across scales. Emphasis is placed on integrating empirical data and theory to assess vegetation patterns and ecosystem dynamics.

BIO 7427. Principles of Population Biology I.

This course provides a comprehensive introduction to the core concepts in population biology and applications in conservation biology. Major topics include population ecology, population enumeration, population genetics, molecular ecology, quantitative genetics, evolutionary biology and principles of conservation biology. Instruction emphasizes critical analysis of scientific literature, comparative evaluation of research approaches, and application of quantitative and conceptual frameworks to specific questions in population and conservation biology. Students examine theoretical and analytical methods and case studies from the primary scientific literature and synthesize contemporary research findings to construct evidence-based conclusions about specialized topics in population biology.

BIO 7428. Principles of Population Biology II.

This course provides a comprehensive introduction to the core concepts in population biology and applications in conservation biology. Major topics include community ecology, ecological and evolutionary biogeography, phylogenetic inference, comparative phylogenetics, species delimitation, molecular ecology and principles of conservation biology. Instruction emphasizes critical analysis of scientific literature, comparative evaluation of research approaches, and application of quantitative and conceptual frameworks to specific questions in population and conservation biology. Students examine theoretical and analytical methods and case studies from the primary scientific literature, and synthesize the research findings to construct evidence-based conclusions about specialized topics in population biology.

BIO 7430. Mycology.

This course provides advanced study of fungal biology with an emphasis on taxonomy, systematics, morphology, ecology, and evolution. Students examine fungal diversity and evolutionary relationships while evaluating fungi as ecological drivers, symbionts, and pathogens. Emphasis is placed on integrative approaches to fungal classification and interpretation of mycological data. The course supports advanced understanding of fungi as model systems in biological research and prepares students for professional and scholarly work in ecology, microbiology, and evolutionary biology.

BIO 7433. Population Genetics.

This course provides a comprehensive introduction to the core concepts in population genetics and applications in evolutionary biology. Major topics include principles of population genetics, quantification of genetic variation, molecular marker systems, mathematical modeling and simulations, molecular evolution and evolutionary processes. Instruction emphasizes basic computer programming, critical analysis of scientific literature, and application of quantitative and conceptual frameworks to specific questions in population genetics and genomics. Students examine theoretical and analytical methods and case studies from the primary scientific literature. Students evaluate and synthesize contemporary research findings to construct evidence-based conclusions about specialized topics in population genetics.

BIO 7434. Herpetology.

This course examines the origin and evolution of amphibians and reptiles, including their reproductive and physiological adaptations, taxonomy, systematics, and population biology. Content emphasizes North American species, especially groups inhabiting Texas. Students consider how amphibians' and reptiles' biology informs understanding of broader environmental patterns and processes. Laboratory work develops identification skills, evaluates habitat requirements, and examines conservation issues. Students analyze diagnostic visual and auditory characteristics to distinguish among amphibians and reptiles and to draw evidence-based biological inferences.

BIO 7440. Aquatic Toxicology.

This course introduces students to the principles for identifying and assessing the adverse effects of chemicals and their mixtures on freshwater and marine organisms and ecosystems. After reviewing the basic concepts of toxicology, students will investigate the toxicodynamics and environmental fate of classic pollutants, such as trace elements, pesticides, and polycyclic aromatic hydrocarbons (PAHs), alongside emerging threats such as per- and polyfluoroalkyl substances (PFAS), micro- and nanoplastics, and pharmaceuticals. Through a series of case studies, students critique historical environmental disasters and investigate current national and global regulatory frameworks.

BIO 7443. Vertebrate Endocrinology.

This course examines function and organization of the endocrine system. It describes the major endocrine glands, the synthesis and release of their hormone products, and their effect on target tissues. Endocrine control of digestion, growth, reproduction, and homeostasis will be compared between mammals and other vertebrate groups. Students will benefit from having a background in genetics and physiology.

BIO 7466. Phylogenetics.

This course examines advanced phylogenetic methodologies used to reconstruct evolutionary relationships among organisms. The main topics include principles of homology, understanding analytical assumptions, model selection, construction of molecular datasets, sequence alignment, tree-building algorithms, and statistical evaluation of phylogenetic hypotheses. Students analyze molecular data using contemporary computational tools and evaluate methodological assumptions underlying phylogenetic inference. Emphasis is placed on developing independent research questions, managing large-scale datasets, and interpreting phylogenetic results within evolutionary and comparative biological frameworks. Prerequisite: BIO 7405 with a grade of "B" or better.

BIO 7468. Groundwater Resources.

This course covers concepts related to geological, physical, chemical, and biological factors influencing sustainable groundwater resources, including hydrologic linkages and interactions with surface aquatic resources. Emphasis will be on the karst aquifer systems of Central Texas and other aquifer systems of the United States. Students will analyze groundwater processes by synthesizing field observations, laboratory measurements, and publicly available hydrologic datasets. Through these approaches, students learn to evaluate groundwater quantity, quality, and data uncertainty, construct water budgets for complex aquifer systems, and interpret groundwater data to assess resource availability and management challenges in regional and national water resource contexts.

BIO 7472. Agent-Based Modeling and Ecology.

This course introduces agent-based modeling as a method for exploring how interactions among individual agents and their environments give rise to populations and ecosystems. Students examine concepts and theories of individual-based ecology and computational modeling for research applications. Emphasis is placed on model development, guiding students from conceptualization through implementation in software used to test scientific hypotheses. Students are expected to enter the course with prior coursework in statistics, ecology, and ecological modeling.

BIO 7599A. Dissertation.

This course represents doctoral‑level enrollment for original dissertation research and writing in Integrative and Applied Biology conducted under the direct supervision of the dissertation advisor. Students engage in independent, sustained scholarly inquiry, including research design, data collection or analysis, interpretation of findings, and preparation of the dissertation. Continuous enrollment is required during each long semester in which dissertation research or writing is conducted to ensure ongoing faculty supervision and academic oversight. This course supports the completion of advanced research that contributes to the scientific understanding of biological systems and resources and fulfills doctoral dissertation requirements.

BIO 7699A. Dissertation.

This course represents doctoral‑level enrollment for original dissertation research and writing in Integrative and Applied Biology conducted under the direct supervision of the dissertation advisor. Students engage in independent, sustained scholarly inquiry, including research design, data collection or analysis, interpretation of findings, and preparation of the dissertation. Continuous enrollment is required during each long semester in which dissertation research or writing is conducted to ensure ongoing faculty supervision and academic oversight. This course supports the completion of advanced research that contributes to the scientific understanding of biological systems and resources and fulfills doctoral dissertation requirements.

BIO 7999A. Dissertation.

This course represents doctoral‑level enrollment for original dissertation research and writing in Integrative and Applied Biology conducted under the direct supervision of the dissertation advisor. Students engage in independent, sustained scholarly inquiry, including research design, data collection or analysis, interpretation of findings, and preparation of the dissertation. Continuous enrollment is required during each long semester in which dissertation research or writing is conducted to ensure ongoing faculty supervision and academic oversight. This course supports the completion of advanced research that contributes to the scientific understanding of biological systems and resources and fulfills doctoral dissertation requirements.

CJ 7199. Dissertation.

This course provides doctoral students with structured support as they complete the research and writing required for the doctoral dissertation in criminal justice or criminology under the supervision of a faculty advisor and dissertation committee. Through this process, students will design, implement, and complete an original and significant research project that advances knowledge in criminal justice. Emphasis is placed on theoretical development, methodological rigor, advanced data analysis, and the production of a high-quality scholarly manuscript suitable for publication. The course culminates in a formal oral defense of the dissertation. Students are required to maintain continuous enrollment in dissertation research each long semester until the dissertation is successfully completed and approved.

CJ 7299. Dissertation.

This course provides doctoral students with structured support as they complete the research and writing required for the doctoral dissertation in criminal justice or criminology under the supervision of a faculty advisor and dissertation committee. Through this process, students will design, implement, and complete an original and significant research project that advances knowledge in criminal justice. Emphasis is placed on theoretical development, methodological rigor, advanced data analysis, and the production of a high-quality scholarly manuscript suitable for publication. The course culminates in a formal oral defense of the dissertation. Students are required to maintain continuous enrollment in dissertation research each long semester until the dissertation is successfully completed and approved.

CJ 7301. Instructional Assistant Supervision.

This course prepares doctoral students serving as teaching assistants to work effectively in varied instructional environments. Students examine fundamental responsibilities associated with instructional support, including communication, task management, and collaboration with faculty and undergraduate learners. The course provides structured opportunities for feedback and professional development, emphasizing objective evaluation and the refinement of practical skills. All topics are approached as areas of study rather than as prescriptive directives. This course supports assistantship performance but does not apply toward graduate degree requirements.

CJ 7309. Proseminar.

This course introduces doctoral students to foundational knowledge and professional practices relevant to success in criminal justice scholarship. Students examine topics such as disciplinary perspectives, teaching responsibilities, publication processes, grant and fellowship opportunities, dissertation development, and post doctoral career pathways. The course emphasizes identifying research opportunities, understanding scholarly expectations, and building productive collaborations with faculty and peers. All topics are approached as areas of academic study rather than prescriptive models of professional conduct. Enrollment requires completion of 12 hours of doctoral coursework in Criminal Justice.

CJ 7310. Philosophy of Law, Justice, and Social Control.

This course examines major philosophical perspectives that inform the study of law, justice, and social control within contemporary criminal justice systems. Students analyze how legal institutions develop, function, and evolve over time, with attention to theoretical debates surrounding authority, punishment, rights, and social order. The course emphasizes critical evaluation of scholarly arguments rather than advocacy for particular policy positions. Activities include structured critiques, discussions of philosophical frameworks, and exploration of emerging trends in justice research. All topics are presented as objects of academic study to support independent reasoning and professional inquiry.

CJ 7311. Advanced Criminological Theory.

This course examines major criminological paradigms used to explain crime and criminal behavior, emphasizing their historical development, underlying assumptions, and areas of theoretical debate. Students evaluate theories through a philosophy of science lens, including topics such as theory construction, conceptual clarity, theoretical integration, and the systematic assessment of theoretical claims. The course also considers how criminological theories guide empirical inquiry without endorsing specific policy outcomes. Throughout the semester, students analyze arguments, compare competing perspectives, and develop skills for evaluating the strengths and limitations of theoretical frameworks. All material is presented as an object of scholarly study to support independent reasoning and doctoral level inquiry.

CJ 7313. Race and Ethnicity in Crime and Criminal Justice.

This course examines scholarly research on patterns related to race, ethnicity, crime, and criminal justice system responses. Students analyze empirical findings, theoretical explanations, and methodological approaches used to study differential experiences across racial and ethnic groups. Topics include interactions with law enforcement, sentencing patterns, and system level processes surrounding probation, pre sentencing, and post release supervision. Emphasis is placed on evaluating evidence, questioning assumptions, and understanding how researchers interpret disparities without prescribing specific policy solutions. All material is presented as an object of academic study to support independent reasoning and rigorous inquiry.

CJ 7314. Policing.

This course examines research based approaches to understanding contemporary issues in American policing. Students analyze the collection, interpretation, and limitations of crime statistics, victimization data, and measures of police performance, with an emphasis on how such information is generated and used in empirical studies. The course explores methodological debates in policing research, including data quality, measurement challenges, and evaluation strategies. All topics are framed as scholarly inquiries rather than prescriptive assessments of policing practices. Students engage critically with evidence to develop independent reasoning about patterns, explanations, and research designs relevant to policing in diverse contexts.

CJ 7315. Corrections.

This course provides an analytical overview of the history, structure, and functions of correctional systems in the United States. Students examine institutional and community based correctional models, including prisons, jails, intermediate sanctions, and reentry programs. The course also explores major theories of punishment and their relevance to correctional practices. Emphasis is placed on understanding how policies are developed, how programs operate, and how researchers study correctional outcomes. Sensitive topics such as supermax confinement and capital punishment are approached as subjects of scholarly inquiry rather than prescriptive positions. Throughout the course, students evaluate evidence and theoretical perspectives to develop informed, independent analyses of correctional systems.

CJ 7320. Quantitative Research Methods.

This course introduces doctoral students to quantitative research methodologies used in criminal justice scholarship. Students examine the philosophy of science, research ethics, and methodological principles that guide the development of empirically grounded studies. Topics include causal inference, nonexperimental and descriptive designs, sampling strategies, secondary data sources, and techniques for gathering and managing quantitative data. Emphasis is placed on evaluating methodological choices, understanding the strengths and limitations of various designs, and analyzing how researchers apply quantitative tools to study crime and justice phenomena. All content is presented as an object of scholarly inquiry, supporting independent reasoning and methodological rigor.

CJ 7321. Linear Regression for Criminal Justice Research.

This course introduces doctoral students to multivariate regression analysis as applied to criminal justice research. Topics include bivariate and multiple regression, estimation and statistical inference, model assumptions and diagnostics, factor analysis, statistical interactions, mediation, missing data techniques, and models for non-continuous dependent variables. Emphasis is placed on the applied interpretation of statistical output generated with software rather than on mathematical derivation. Students develop the skills necessary to critically evaluate quantitative research published in major criminal justice journals and to conduct independent empirical analyses.

CJ 7322. Advanced Research for Planning and Evaluation.

This course introduces doctoral students to research designs and evaluation methods used to plan and assess criminal justice programs. Topics include outcome and process evaluation, implementation science, mechanisms, mediators, and moderators of effectiveness, and the ethical and practical issues associated with evaluation research. Specific evaluation research designs and techniques are critically examined. Emphasis is placed on understanding how researchers evaluate criminal justice interventions while recognizing the methodological limitations of such evaluations. The course supports independent reasoning and evidence-based evaluation skills applicable to a variety of research and planning contexts.

CJ 7323. Applied Statistics and Quantitative Data Analysis.

This course develops doctoral students’ proficiency in applying statistical concepts to real-world data used in criminal justice and related fields. Students engage in data preparation, graphical exploration, statistical analysis, interpretation of results, and clear communication of findings. Emphasis is placed on critical thinking, methodological reasoning, and the appropriate use of statistical tools. The course uses SPSS to introduce a range of analytical techniques commonly employed in quantitative research. Students learn to evaluate the strengths and limitations of statistical methods while maintaining objectivity in interpretation. An introductory master’s level statistics course is required prior to enrollment.

CJ 7330. Qualitative Research Methods.

This course introduces doctoral students to qualitative research methodologies used in social and criminal justice inquiry. Students examine major qualitative approaches, including ethnography, focus groups, in depth interviews, and case studies, with attention to how researchers design studies, gather data, and analyze findings. The course emphasizes the logic of inductive reasoning, the evaluation of methodological choices, and the integration of qualitative and quantitative approaches when appropriate. All methods are presented as tools for scholarly investigation rather than prescriptive standards for practice. Students engage critically with research exemplars to understand how qualitative evidence is constructed and interpreted in academic contexts.

CJ 7331. Law and Behavioral Science.

This course examines how behavioral science research informs the study of criminal law and legal processes. Students explore topics such as criminal sanctions, expert testimony, and behavioral evidence in courtroom settings. The course also surveys research on media influences, emphasizing analytical evaluation rather than prescriptive conclusions. Attention is given to how scholars study legal decision making, institutional responses, and the behavioral dimensions of criminal offending. All topics are presented as objects of scholarly inquiry designed to foster independent reasoning and methodological rigor, without endorsing particular legal or policy positions.

CJ 7336. Survey Research Methods for Criminal Justice.

This course introduces doctoral students to the theory and practice of survey research as applied to criminal justice and criminology. Topics include the history of survey research, probability and nonprobability sampling, survey modes (mail, telephone, internet, and mixed-mode), question wording and questionnaire design, measurement of sensitive topics, maximizing response rates and managing nonresponse bias, survey-based experimental designs, and basic survey data management. Students gain hands-on experience designing original survey instruments and crafting research proposals. The course prepares students to both produce and critically evaluate survey-based research in the field.

CJ 7338. Qualitative Data Collection, Coding and Analysis.

This course takes a structured approach to understanding and implementing the various information collection methods used in qualitative research, including formatting the information for coding, coding schemes, and information interpretation.

CJ 7350B. Academic Scholarship and Communication.

This course examines the processes involved in conducting and communicating academic research within criminal justice and related fields. Students study approaches to developing research ideas, interpreting empirical findings, and preparing manuscripts for submission to peer reviewed journals. The course provides an overview of publication outlets, including their audiences, topical areas, and submission expectations, as well as analytical consideration of how scholars navigate the peer review and revision process. Emphasis is placed on understanding the conventions of academic communication and evaluating the factors that influence research dissemination. All topics are presented as objects of scholarly inquiry rather than prescriptive guidance for achieving specific professional outcomes.

CJ 7350C. Qualitative Data Collection, Coding and Analysis.

This course takes a structured approach to understanding and implementing the various information collection methods used in qualitative research, including formatting the information for coding, coding schemes, and information interpretation.

CJ 7350E. Discrete Multivariate Models.

This course examines statistical models designed for discrete outcome variables commonly encountered in criminal justice and social science research. Students explore the theory and application of maximum likelihood estimation, binary and multinomial logistic regression, and models for count data such as the negative binomial. Emphasis is placed on understanding model assumptions, evaluating model fit, and interpreting results within empirical research contexts. The course highlights how researchers select among discrete multivariate models based on research questions and data characteristics. All material is presented as an object of scholarly inquiry, supporting independent evaluation of methodological choices rather than prescribing particular analytic preferences. Prerequisite: CJ 7321 with a grade of "B" or better or instructor approval.

CJ 7350F. Environmental Criminology.

This course examines how opportunity explains variations in crime across space, time, and situational contexts. In contrast to theories of criminality that focus primarily on the offender, environmental criminology considers the entire crime event, emphasizing offender decision-making, target vulnerability, and situational conditions that facilitate crime. Routine activity theory, the rational choice perspective, and crime pattern theory are examined. Students also explore how these theories can be applied to prevent and investigate crime through strategies such as crime mapping, crime scripts, situational crime prevention, crime prevention through environmental design (CPTED), problem-oriented policing, and geographic profiling.

CJ 7350G. Seminar in Macro Criminology.

This course has a macro focus, examining criminological theory and research that takes cities, geographical regions, states, and nations as the units of comparison. The importance and relevance of macro criminology for understanding the causes of crime and key criminal justice issues, such as police resources, are explored in depth. Prerequisite: CJ 7311 with a grade of "B" or better or instructor approval.

CJ 7350I. Introduction to Structural Equation Modeling.

This course introduces students to the concepts and applications of structural equation modeling (SEM), an analytical framework used to study relationships among observed and latent variables. Topics include model specification, recursive and non recursive systems, path analysis, measurement models, factor analysis, and the logic of mean and covariance structure analysis. Students examine the assumptions, strengths, and limitations of SEM and learn how researchers apply these methods to evaluate theoretical models in the social sciences. The course presents SEM as an object of scholarly inquiry, emphasizing methodological reasoning and the evaluation of model fit. Prerequisite: CJ 7321 with a grade of "B" or better or instructor approval.

CJ 7350K. Criminal Justice Forecasting and Policy Analysis.

This course examines the inputs and outputs of criminal justice programs. It covers forecasting methods using statistical bootstrapping techniques including line fitting methods, moving averages, cohort propagation matrixes, and systems simulations.

CJ 7350L. Sex Offenders: Theory, Research & Policy.

This course examines theoretical, methodological, and policy-focused approaches to the study of sex crime. Students analyze how criminological theories have been used to understand sex crime and explore research design considerations relevant to studying this sensitive topic, including ethical review processes, data limitations, self report challenges, and the evaluation of available data sources. The course also investigates policy frameworks associated with sex crime, emphasizing how researchers assess the intended purposes, empirical effects, and complexities of these policies. All topics are presented as objects of academic inquiry, enabling students to critically examine evidence without endorsing particular theoretical perspectives or policy positions.

CJ 7350N. Cold Case Investigations.

This course introduces students to the concepts and issues of cold cases and their investigation. The major causes of uncleared crimes will be examined. The nature of crime and criminality will be explored with an emphasis on serial sexual crime, stranger offenders, and victim risk. Solving cold cases, evidence structure, relevant forensic methodologies, and interviewing approaches are discussed. The issue of missing persons and its relationship to cold cases is examined. Methods of crime linkage analysis, behavioral profiling, and geographic profiling are outlined and explained. The course will use a number of case studies and in-class exercises.

CJ 7350O. Survey Research Statistics in Criminal Justice.

This course addresses the techniques used in statistical analyses of social survey data, including classical test theory, item response theory statistics, and visually displaying social survey data findings for a variety of audiences. Students learn about analytic survey data strategies and procedures, and are trained in computational procedures related to survey research statistical analyses. Analyses include descriptive statistics, bivariate statistics, and multivariable regression modeling of data from probability and non-probability-based samples, accounting for weighting/clustering. Prerequisite: CJ 7320 and CJ 7321 both with a grade of “B” or better.

CJ 7350P. Criminal Justice Data Wrangling and Visualization.

This course introduces students to the principles and practices for preparing criminal justice data for analysis and visualization. Students develop automated, text-based programming workflows to acquire, clean, enrich, and restructure raw data and to produce visualizations for exploring patterns and relationships. Specific topics include data types and formats, relational database design, identification and management of missing values, variable construction and recoding, and dataset integration for data enrichment. Emphasis is placed on building reproducible workflows and documenting all data preparation procedures so that analytic results can be replicated, verified, and communicated effectively.

CJ 7350Q. Modern Methods in Biosocial Criminology.

This course examines contemporary biosocial research methods and their application to criminological questions. Students will engage foundational evolutionary and developmental perspectives on behavior, examine how biological processes and social environments interact across the life course, and explore how computational methods and molecular data can be used to study a wide range of behaviors relevant to criminology and the social sciences. By the end of the course, students will be able to critically evaluate biosocial research, apply biosocial frameworks to novel criminological questions, and identify key ethical/policy issues raised by biosocial inquiry.

CJ 7360. Independent Study.

This course provides doctoral students with the opportunity to pursue advanced, individualized study in a selected area of criminal justice under the supervision of a doctoral faculty member. The specific topic, scope, and learning activities are determined collaboratively by the student and supervising faculty member and are aligned with the student’s program of study. Emphasis is placed on independent scholarly inquiry, critical analysis of relevant literature, and the development of subject‑matter expertise. Course requirements may include readings, written work, research projects, or other academic activities appropriate to the approved topic. This course may be repeated once for credit when the subject matter differs. Prerequisite: Instructor approval.

CJ 7399. Dissertation.

This course provides doctoral students with structured support as they complete the research and writing required for the doctoral dissertation in criminal justice or criminology under the supervision of a faculty advisor and dissertation committee. Through this process, students will design, implement, and complete an original and significant research project that advances knowledge in criminal justice. Emphasis is placed on theoretical development, methodological rigor, advanced data analysis, and the production of a high-quality scholarly manuscript suitable for publication. The course culminates in a formal oral defense of the dissertation. Students are required to maintain continuous enrollment in dissertation research each long semester until the dissertation is successfully completed and approved.

CJ 7599. Dissertation.

This course provides doctoral students with structured support as they complete the research and writing required for the doctoral dissertation in criminal justice or criminology under the supervision of a faculty advisor and dissertation committee. Through this process, students will design, implement, and complete an original and significant research project that advances knowledge in criminal justice. Emphasis is placed on theoretical development, methodological rigor, advanced data analysis, and the production of a high-quality scholarly manuscript suitable for publication. The course culminates in a formal oral defense of the dissertation. Students are required to maintain continuous enrollment in dissertation research each long semester until the dissertation is successfully completed and approved.

CJ 7699. Dissertation.

This course provides doctoral students with structured support as they complete the research and writing required for the doctoral dissertation in criminal justice or criminology under the supervision of a faculty advisor and dissertation committee. Through this process, students will design, implement, and complete an original and significant research project that advances knowledge in criminal justice. Emphasis is placed on theoretical development, methodological rigor, advanced data analysis, and the production of a high-quality scholarly manuscript suitable for publication. The course culminates in a formal oral defense of the dissertation. Students are required to maintain continuous enrollment in dissertation research each long semester until the dissertation is successfully completed and approved.

CJ 7999. Dissertation.

This course provides doctoral students with structured support as they complete the research and writing required for the doctoral dissertation in criminal justice or criminology under the supervision of a faculty advisor and dissertation committee. Through this process, students will design, implement, and complete an original and significant research project that advances knowledge in criminal justice. Emphasis is placed on theoretical development, methodological rigor, advanced data analysis, and the production of a high-quality scholarly manuscript suitable for publication. The course culminates in a formal oral defense of the dissertation. Students are required to maintain continuous enrollment in dissertation research each long semester until the dissertation is successfully completed and approved.