Spring 2026 Special Topics Courses
CSCI 5980 - Game Engine Architecture
MW 02:30 PM‑03:45 PM
Instructor: Evan Suma Rosenberg
Course Description: This course covers the software architecture and systems programming of modern real-time game engines. Topics include engine modularity and game loops; data-oriented design and memory management; entity–component systems; asset import/cooking and hot-reload; rendering architecture and material systems; audio, animation, and physics integration; tooling and profiling; and serialization, saving/loading, and streaming. Students will implement a minimal-yet-functional game engine in C++ over five programming assignments. This is a programming-heavy course that assumes prior familiarity with object-oriented programming and introductory computer graphics.
Prerequisites for Registration: CSCI 4611 or 5607 or instructor consent. Students may email the instructor to obtain permission numbers for registration.
CSCI 5980 - Linux Kernel Development
MW 09:45 AM‑11:00 AM
Instructor: Jack Kolb
Course Description: This course will introduce students to Linux kernel development. We will explore the internals of the Linux operating system by directly reading, modifying, and extending the existing body of kernel source code. We do not seek to replicate the breadth of theory covered by existing operating systems courses, but we do plan to address OS topics as illustrated by Linux, motivated by hands-on experiences with real kernel source code. The course will also cover the software development process practiced by the Linux kernel community, particularly the construction, submission, and review of software patches. Throughout the course, students will be instructed in best practices for OS kernel development.
Prerequisites for Registration: CSci 3081w and CSci 4061 or instructor consent. Students may email the instructor to obtain permission numbers for registration.
CSCI 5980 - Cloud Computing
TTh 04:00 PM‑05:15 PM
Instructor: Ali Anwar
Course Description: This course will cover the fundamentals of cloud services management and cloud software development, including but not limited to design patterns, application programming interfaces, and underlying middleware technologies. More specifically, we will cover the topics of cloud computing service models, data centers resource management, task scheduling, resource virtualization, SLAs, cloud security, software defined networks and storage, cloud storage, and programming models. We will also discuss data center design and management strategies, which enable the economic and technological benefits of cloud computing. Lastly, we will study cloud storage concepts like data distribution, durability, consistency, and redundancy.
Prerequisites for Registration: CSCI 3061 or CSCI 4061 or Grad Standing
CSCI 5980/8980 - Behavioral Data Mining
TTh 01:00 PM‑02:15 PM
Instructor: Jaideep Srivastava
Course Description: Doing things online, including socialization, education, work, entertainment, shopping and healthcare consults, has been increasing dramatically, and is expected to continue to increase. It is also creating huge amounts of behavioral data about people at a very fine granularity. This creates a unique opportunity to analyze this data to (i) gain a better understanding of human behavior and thus add nuance to existing social and behavioral science models, (ii) use results from the analysis to improve the effectiveness of various applications, and (iii) create new applications. This course will focus on all these aspects of behavioral data mining, and draw upon a number of applications mentioned above.
Prerequisites for Registration: None.
CSCI 8980 - ML for Programmable Wireless Networks
MW 01:00 PM‑02:15 PM
Instructor: Zhangyu Guan
Course Description: This course covers the state of the art in optimization and machine learning (ML) for programmable wireless networks, with applications to 5G, 6G, and next-generation Internet of Things (IoT). The course begins with a brief overview of the fundamental principles of wireless networks, control, and optimization, followed by an introduction to key optimization methods and ML paradigms relevant to wireless systems.
The main focus is on the integration of optimization and ML into programmable wireless networking Topics include:
i) Optimization techniques for wireless resource allocation, scheduling, and control;
ii) Supervised and unsupervised learning methods for network prediction, monitoring, and anomaly detection;
iii) Reinforcement learning and multi-agent learning for adaptive wireless network control; iv) Software-defined and virtualized architectures, e.g., software-defined radio (SDR), software defined networking (SDN), and network slicing, as programmable platforms for optimization and ML; and
v) Class projects based on SDR platform.
The course is designed for Master’s and Ph.D. students. Evaluation will be based on homework and projects, with a strong emphasis on hands-on experimentation. By the end of the semester, students will present projects that apply optimization and ML to programmable wireless networking problems, demonstrating both theoretical understanding and practical implementation.
Prerequisites for Registration: Graduate Student Status
CSCI 8980 - Emerging Topics in Large Language Models
TTh 04:00 PM‑05:15 PM
Instructor: Dongyeop Kang
Course Description: This graduate-level special topics course examines emerging frontiers in large language models (LLMs) and their expanding roles across cognitive science, human-AI interaction, and the social sciences. Students will explore state-of-the-art research in areas such as cognitive architectures, reasoning and planning, compositionality, social cognition, and test-time scaling, as well as applications of LLMs in domains including law, medicine, journalism, and scientific discovery.
Each student (or team) will select a focused topic, conduct a comprehensive literature review, and lead a seminar-style lecture and discussion. The course culminates in a semester-long research or implementation project, presented both as a final paper and in-class presentation.
Emphasizing creative inquiry, scholarly communication, and critical analysis, the course is designed for students eager to engage with the rapidly evolving landscape of LLM research.
Prerequisites for Registration: Students may complete the following form (https://forms.gle/6GtkWThz5dVxL1kB6) to request a permission number for registration.
CSCI 8980 - Advanced Graph Algorithms
TTh 11:15 AM‑12:30 PM
Instructor: Zihan Tan
Course Description: Graphs are among the most powerful and versatile tools in algorithm design, and graph algorithms continue to be a central theme in modern algorithmic research. This course will explore key results from modern approaches to graph algorithms, with a particular emphasis on graph sparsification for preserving cut/flow/distance information, and its role in resolving recent open problems. A tentative list of topics: multi-way cut, multi-cut, 0-Extension, multi-commodity flow, sparsest cut, flow-cut gap, expanders and expander decomposition, sampling-based sparsification, spanner, Steiner Point Removal, Travelling Salesperson problem, Steiner Tree problem, emulators, metric realization, graph minor theory.
Prerequisites for Registration: CSci 3041 or CSci 4041 or CSci 5421 or instructor consent. Students may email the instructor to obtain permission numbers for registration.