CS Special Topics Courses for Fall 2026
Last Updated: Thursday, March 26
CS Special Topics Courses
CS 4501 – Special Topics (Undergraduate)
Software Engineering with LLMs (Section 001)
Instructor: Sebastian Elbaum
This course explores the transformative impact of Large Language Models (LLMs) on modern software engineering. Students will learn how to leverage LLMs across the software development lifecycle—from coding and testing to debugging and maintenance—while also considering ethical and safety implications.
Usability Engineering (Section 002)
Instructor: Panagiotis Apostolellis
Why are Google products—mostly—a pleasure to use while Microsoft is still struggling to satisfy its customers? Why did the newest Boeing Max 737 have two fatal accidents on its first flights? What is stimulus salience and inattentional blindness and how they can help (or hinder) the interfaces you design? Most importantly, why might the software you develop with so much technical rigor drive people crazy? In most cases, responses to these questions can be found in the field of usability engineering, a sub-domain of Human-Computer Interaction, which explores the best ways to design interactive systems that are “easy and intuitive to use.” Although this phrase has been widely used, or abused, by software engineers and other stakeholders in software development, there is actually a foolproof path to designing interactive systems that ensure usability. We will embark on an exciting journey to not only cross this path learning about the usability factors and the cognitive psychology principles that inform them, but also use them to redesign and test broadly used software. The ethical use of AI prototyping- development tools will be discussed and encouraged, as long as they serve the intended purpose of design.
Data Privacy (Section 003)
Instructor: Tianhao Wang
This course introduces the foundations and practice of data privacy in modern computing systems. Topics include privacy attacks on machine learning and data release, differential privacy, privacy-preserving ML, and technologies such as secure multi-party computation, homomorphic encryption, trusted execution environments, and network privacy tools. Includes hands-on labs and a final project.
Privacy in the Internet Age (Section 004)
Instructor: Yixin Sun
An in-depth look at privacy issues on the Internet, including anonymous communications, traffic analysis, and online tracking, along with privacy-enhancing technologies.
AI-Powered Cybersecurity (Section 005)
Instructor: Jack W. Davidson
CS 6501 – Special Topics (Graduate)
3D Computer Vision (Section 001)
Instructor: Zezhou Cheng
Focuses on foundational concepts and recent advances in 3D computer vision, including multiview geometry and deep learning approaches, with applications in robotics, graphics, and AR/VR.
Economics of Distributed Systems (Section 002)
Instructors: Matheus Venturyne Xavier Ferreira
Examines how economic incentives impact distributed systems. Topics include auction design, cryptocurrency, decentralized finance, fairness, and security in decentralized environments.
Networking Infrastructure (Section 003)
Instructor: Qizhe Cai
Covers modern data center networking, including high-bandwidth systems, programmable hardware, SmartNICs, RDMA, SDN, and infrastructure for machine learning.
Learning for Interactive Robots (Section 004)
Instructor: Yen-Ling Kuo
Explores AI/ML techniques for enabling robots to interact effectively with humans, including natural language grounding, intent inference, and human-robot collaboration.
Wireless Sensing for IoT (Section 005)
Instructor: Kun Qian
Introduces IoT sensing using wireless signals such as Wi-Fi and radar, covering sensing modalities, AI and signal processing, and applications like smart homes and transportation.
Probabilistic Machine Learning (Section 006)
Instructor: Zhe Zeng
Covers probabilistic foundations and modern approaches including autoregressive models, VAEs, normalizing flows, diffusion models, and tractable probabilistic models.
Graph Machine Learning (Section 007)
Instructor: Chen Chen
Focuses on analyzing large-scale graphs, including network measures, community detection, recommendation systems, and graph neural networks.
Online Optimization and Learning in Games (Section 008)
Instructor: Chen Yu Wei
A theoretical course on optimization and learning algorithms in dynamic, multi-agent environments.
Trustworthy Machine Learning (Section 009)
Instructor: Xi Peng
Covers methods for building reliable, robust, interpretable, fair, and safe ML systems under real-world constraints.
Software Engineering Logic (Section 010)
Instructor: Kevin J. Sullivan