Fall 2026

CSE 510 Agentic AI Engineering (Lecture)
Section: AE
Instructor: David Doermann
Description: Agentic AI Engineering is a project-based course on designing, building, and responsibly deploying AI-powered agents. Unlike standard chatbots, agents are engineered systems that sense bounded environments, choose actions through well-designed interfaces, and use tools to achieve explicit objectives. The curriculum focuses on the orchestration of foundation models with memory, planning, and structured workflows. Students will evaluate systems against safety constraints and performance benchmarks while building autonomous applications that solve multi-step tasks.
Notes: The class meets once weekly, utilizing a hybrid format that alternates between conceptual lectures and intensive, hands-on technical practice. Students will work extensively with Python-based frameworks (such as LlamaIndex or LangChain), local LLM orchestration, and containerized deployment using Docker. Students enrolled at the 500-level will be held to a higher standard of technical rigor.
Prereqs: CSE 250 Data Structures or equivalent programming maturity. Students should be comfortable writing Python programs, using Git/GitHub, reading technical documentation, and debugging code. Useful background: CSE 331 Algorithms and Complexity, CSE 368 Introduction to Artificial Intelligence, CSE 442 Software Engineering, CSE 435 Information Retrieval, CSE 474 Introduction to Machine Learning, or equivalent experience. These are recommended, not required. Mathematical maturity will help.
Instruction Mode: In person
Class #: 24591
Dates: 08/24/2026 - 12/07/2026
Days, Time: W, 5:00PM-7:40PM
Location: Clemen 120, North Campus
Credit Hours: 1-3
Enrollment: 15/15 (0/15 seats reserved: force registration only) (Active)
Info:
CSE 610 Quantum Networks for Distributed Computing and Sensing (Lecture)
Section: QIAO
Instructor: Chunming Qiao
Description: This course will first introduce basic quantum computing, communication, sensing and networking concepts, and then delve into research issues in distributed quantum computing (DQC), including but not limited to those related to distributed quantum sensing (DQS) and Quantum Error Correction (QEC). Given that these are all emerging research topics, the students are expected to possess a strong interest in research, and the ability to read and present research papers. Although no prior knowledge of quantum physics or quantum mechanics is needed, the students should have a background in linear algebra, algorithms, and networks. The students may be asked to take CSE439/539 (Quantum Computation), CSE489/589 (Modern Networking Concepts), or similar courses concurrently. Knowledge in distributed systems, datacenters, optimization, and algorithms would be a plus.
Notes: Although this is a graduate-level course in CSE, students with EE and Physics backgrounds are welcome. Also, Junior and Senior UGs with some exposure to optics/photonics, and/or quantum mechanics are also welcome. Students are expected to have a sufficient background in Math (complex numbers, vectors, linear algebra, and probability) and basic computer knowledge (e.g., python programming, and networking) as well.
Prereqs: See descriptions above.
Instruction Mode: In person
Class #: 24262
Dates: 08/24/2026 - 12/07/2026
Days, Time: TR, 2:00PM-3:20PM
Location: Davis 113A, North Campus
Credit Hours: 3
Enrollment: 1/20 (Active)
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CSE 610 Human–Computer Interaction: Foundations for Design and Research (Lecture)
Section: XI
Instructor: Xi Lu
Description: This graduate-level course provides an introduction to Human–Computer Interaction (HCI), combining practical design methods with an overview of core research in the field. We will cover user needs and task analysis, user interface design guidelines, and methods for prototype evaluation and interface testing, while also examining major research topics and forms of contribution in HCI. Over the past few decades, HCI has expanded from improving computer use in traditional workplaces to investigating and critiquing the many technologies embedded in everyday life. This course will help you understand and engage with that evolution. The course is designed for Master’s and PhD students who may be new to HCI but are interested in practically applying HCI skills and methods: whether to design and evaluate technology or to conduct HCI research.
Prereqs: No
Instruction Mode: In person
Class #: 24118
Dates: 08/24/2026 - 12/07/2026
Days, Time: TR, 2:00PM-3:20PM
Location: Baldy 105, North Campus
Credit Hours: 3
Enrollment: 3/20 (3/20 seats reserved for computer science & engineering majors only) (Active)
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CSE 610 AI-assisted System Programming Practice: Build a Query Optimizer (Lecture)
Section: ZHAO
Instructor: Zhuoyue Zhao
Description: Query optimizer is a key component in a Relational DataBase Management System (RDBMS). It converts an unoptimized relational algebra expression into an optimized physical plan with lower cost. However, query optimizer is a hard problem in general cases because of the intractable search space of equivalent plans and difficulty to accurately estimate cardinality and cost. In this course, we will survey the recent literature in optimizer design build your own query optimizer for a simple query execution engine. Given the large design space and the countless related works, you may use Large Language Model based AI software to help you in learning and coding. Prior experience in database system development is preferred but not required, such as CSE 562 or equivalent project experience.
Notes: This course does not have written assignments, midterm or final exams. There is a semester long project consisting of multiple checkpoints, which will be a mix of coding, oral presentation and written reports.
Instruction Mode: In person
Class #: 24676
Dates: 08/24/2026 - 12/07/2026
Days, Time: W, 12:00PM-2:50PM
Location: Academ 322, North Campus
Credit Hours: 3
Enrollment: 1/15 (1/15 seats reserved for computer science & engineering majors only) (Active)
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CSE 750 AI Seminar (Seminar)
Section: AI
Instructor: Rohini Srihari
Description: The AI seminar surveys the frontiers of current artificial intelligence research with a special emphasis on conversational AI and large language model-powered chatbots. Students will explore how modern dialogue systems draw on the full breadth of AI: natural language processing, deep generative models and Bayesian inference, foundation models and deep learning optimization, and multimodal understanding and computer vision. We will discuss the use of LLMs for scientific discovery and LLMs for creativity in the arts. The seminar also addresses applications such as media forensics and information integrity and biomedical applications. Weekly presentations by students, CSE faculty and invited speakers will ground these topics in current research.
Prereqs: CSE 574
Instruction Mode: In person
Class #: 24695
Dates: 08/24/2026 - 12/07/2026
Days, Time: W, 11:00AM-1:50PM
Location: On Campus - TBD (CSS), North Campus
Credit Hours: 1-3
Enrollment: 0/12 (0/12 seats reserved for computer science & engineering majors only) (Active)
Info:
CSE 750 Databases and Programming Languages (Seminar)
Section: DBPL
Instructor: Andrew Hirsch
Description: The UB Databases and Programming Languages seminar brings together a diverse group of students and faculty interested in addressing challenges at the intersection of data management and formal methods. Specific areas of interest include: * Correct-by-construction distributed programs * Logic programming and Datalog * Incremental view maintenance * Languages for safe, correct computation * Data structures for approximate computation * Data structures and languages for embedded and portable systems In order to sign up for this class, you must be working with a faculty member associated with the DBPL group. See https://cse.buffalo.edu/dbpl.
URL: https://cse.buffalo.edu/dbpl
Instruction Mode: In person
Class #: 24184
Dates: 08/24/2026 - 12/07/2026
Days, Time: T, 12:30PM-1:50PM
Location: Baldy 112, North Campus
Credit Hours: 1-3
Enrollment: 4/12 (3/12 seats reserved for computer science & engineering majors only) (Active)
Info:
CSE 750 Software Analysis and Applications (Seminar)
Section: SEC
Instructor: Haipeng Cai
Description: Modern software systems—from web services and mobile platforms to distributed microservices and AI-enabled applications—demand rigorous methods to ensure security, reliability, privacy, and performance. This seminar explores the cutting-edge foundations, techniques, and emerging research directions in software analysis: static, dynamic, hybrid, and AI-augmented approaches. Students will engage deeply with seminal papers and state-of-the-art research, including work at top venues such as ICSE, FSE, ISSTA, PLDI, OOPSLA, USENIX Security, S&P, and CCS. The course is intentionally broad: we study not only program analysis but also system-level, cross-layer, and AI-integrated software analysis spanning the full lifecycle of software artifacts. Special emphasis is given to security and privacy applications, but we also investigate analysis for functional correctness, robustness, and performance optimization. Students will learn both classical analysis techniques and how modern advancements (e.g., LLM-based analysis, agentic AI systems, hybrid symbolic-neural analysis, fuzzing with AI guidance) are reshaping software analysis research and practice.
Instruction Mode: In person
Class #: 24056
Dates: 08/24/2026 - 12/07/2026
Days, Time: T, 10:30AM-12:00PM
Location: Baldy 112, North Campus
Credit Hours: 1-3
Enrollment: 5/12 (5/12 seats reserved for computer science & engineering majors only) (Active)
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CSE 750 Area Seminar: Theory (Seminar)
Section: THEO
Instructor: Charlie Carlson
Description: This course provides research-focused students with a forum to discuss active research in their field, practice presentation skills, and meet important speakers. Each section specializes in a particular area. This area will primarily focus on topics in theoretical computer science, including combinatorial optimization, complexity theory, algorithm design, randomness, and graph theory. To sign up, consult the cognizant faculty member from the area.
Notes: Students will be expected to attend regularly and either present their own relevant research or prepare a lecture/presentation based on a new or historically important result in the area.
Instruction Mode: In person
Class #: 24592
Dates: 08/24/2026 - 12/07/2026
Days, Time: UNKWN, Unknown
Location: Unknown, North Campus
Credit Hours: 1-3
Enrollment: 0/ 5 (0/5 seats reserved for computer science & engineering majors only) (Active)
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