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 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.