CS Graduate Courses for Fall 2024

Last Updated: Monday, March 25

Please see Lou’s List for the most up-to-date information regarding course offerings.

Special Topics Courses

CS 6501-001: Human-Computer Interaction w/ Prof. Seongkook Heo - “This course will focus on research methods for Human-Computer Interaction, including the research contributions in HCI and qualitative and quantitative study methods.”

CS 6501-002: Wireless Sensing in IoT w/ Prof. Kun Qian - “Wireless sensing technologies repurpose wireless signals for sensing physical environment and gaining situational awareness. Formed by pervasive wirelessly connected devices, the IoT can be turned into a universal sensor network with wireless sensing, enabling the vision of ambient intelligence. This course covers the wireless sensing basics (e.g., radar, Wi-Fi) and cutting-edge applications (e.g., motion tracking, activity recognition, environmental sensing). The evaluation will be based on assignments, a course project, a mid-term exam, and a final presentation.”

CS 6501-003: Smart and Healthy Buildings w/ Prof. Brad Campbell - “This class focuses the next generation of buildings where smart devices, Internet of Things (IoT) systems, machine learning applications, and simulations platforms will help us understand the changes in indoor environments and occupants’ needs, allowing building systems to dynamically enhance the indoor environmental conditions from health, comfort, and energy perspectives. This course will familiarize students with smart building concepts, terminology, tools and techniques needed to make our future buildings adjust to our needs and energy demands. Specifically, students will gain hands-on experience with real test beds to build and deploy smart building applications that will better support occupants.”

CS 6501-004: TBD w/ TBD

CS 6501-005: Machine Learning in Image Analysis w/ Prof. Miaomiao Zhang - “This course focuses on an in-depth study of advanced topics and interests in image data analysis. Students will learn practical image techniques and gain mathematical fundamentals in machine learning needed to build their own models for effective problem solving. Topics of image denoising/reconstruction, deformable image registration, data dimensionality reduction, generative models, and deep neural networks for image segmentation/classification will be covered. The main focus might change from semester to semester. The graduate students (CS 6501 / ECE 6782) will be given additional programming tasks and more advanced theoretical questions.”

CS 6501-006: TBD w/ Prof. Dave Evans

CS 6501-007: Learning for Interactive Robots w/ Prof. Yen-Ling Kuo

CS 6501-008: 3D Computer Vision w/ Prof. Zezhou Cheng - “The ability to perceive the world in 3D is critically important for humans and has extensive applications in robotics, graphics, virtual/augmented reality, and more. This course will delve into the foundational concepts and recent advancements at the intersection of machine learning and 3D computer vision. Topics include classical multi-view geometry, explicit and implicit 3D representations, differentiable rendering, 3D perception, and motion understanding.”

CS 6501-009: Convex Optimization for Engineering & Data Science w/ Prof. Jundong Li

CS 6501-010: Autonomous Mobile Robots w/ Prof. Nicola Bezzo

CS 6501-011: Intro NLP w/ Prof. Yangfeng Ji - “Natural language processing (NLP) seeks to provide computers with the ability to process and understand human language intelligently. Examples of NLP techniques include (i) automatically translating from one natural language to another, (ii) analyzing documents to answer related questions or make related predictions, and (iii) generating texts to help story writing or build conversational agents. This course, consisting of one fundamental part and one advanced part, will give an overview of modern NLP techniques.”

CS 6501-012: Cloud System Reliability w/ Prof. Chang Lou - “In this course we discuss reliability challenges in cloud systems and software techniques to address them. This is a reading-based seminar course with a course project. No exam.”

CS 6501-013: Geometry of Data w/ Prof. Tom Fletcher

CS 6501-014: Computational Behavior Modelng w/ Prof. Afsaneh Doryab - “This course aims to provide students with a comprehensive understanding of the principles, methods, and applications of computational behavior modeling. The course content focuses on the techniques used to model the behavior of complex systems such as humans, animals, nature, robots, or other computer systems. By the end of the course, students will have gained a thorough knowledge of computational techniques and their relevance to modeling complex systems.”

CS 6501-015: TBD w/ TBD