Fall Term Schedule
Fall 2026
| Number | Title | Instructor | Time |
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CSC 400-01
George Ferguson
TR 4:50PM - 6:05PM
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An introduction to the technical, social, economic, and political aspects of graduate education in computer science at Rochester. Class meetings consist primarily of group discussions and presentations that focus on a broad range of topics, and are intended to improve the critical analysis, technical writing, presentation, and problem-solving skills of students. Both class discussions and written assignments are drawn from material presented in other first-year graduate courses offered within the department. The course also offers a forum for individual department faculty members to discuss their research interests and recent results. Satisfactory performance is required of all first-year PhD students.
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CSC 413-01
Daniel Nikolov
MW 2:00PM - 3:15PM
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This course provides a broad introduction to augmented and virtual reality (AR/VR) systems. The course involves lectures and workshops covering an overview of all aspects of the AR/VR domain, as well as an individual class project developing an AR/VR demonstration of an application chosen by each student. Topics covered in the lectures and class workshops include history, conceptual origins, and design/evaluation principles of AR/VR technologies; optical architectures; audio and visual perception; graphics and computation; data processing and machine intelligence; introduction to AR/VR developer tools; societal implications and ethical aspects. At the end of the course, students will have gained familiarity with the techniques, languages, and cultures of fields integral to the convergent research theme of AR/VR. This course includes workshops and project demonstrations in Studio X, as well as several guest lectures.
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CSC 420-01
Sreepathi Pai
MW 2:00PM - 3:15PM
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This advanced course is aimed at students who wish to build and use efficient large-scale AI systems. It condenses CSC 252, CSC 254, CSC 258, and CSC 255, collating material from those courses that are most relevant to modern AI systems. Topics covered include machine learning (ML) applications, ML programming models, code generation and optimization, manual and automatic parallelization, performance modeling, systems-level challenges, and specialized AI hardware. No background in AI or ML is expected, but proficiency in programming is expected. Prerequisites: CSC 173, CSC 242
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CSC 435-01
Jiaming Liang
TR 9:40AM - 10:55AM
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This course primarily focuses on algorithms for large-scale optimization problems arising in machine learning and data science applications. The first part will cover first-order methods including gradient and subgradient methods, mirror descent, proximal gradient method, accelerated gradient method, Frank-Wolfe method, and inexact proximal point methods. The second part will introduce algorithms for nonconvex optimization, stochastic optimization, distributed optimization, manifold optimization, reinforcement learning, and those beyond first-order.
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CSC 439-01
Xiangxiang Xu
MW 9:00AM - 10:15AM
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This course provides a theoretical foundation for analyzing and understanding machine learning algorithms, with a particular focus on deep learning. Our discussions cover key factors for understanding the behaviors of classical learning algorithms and deep neural networks, including: 1. Approximation: When do neural networks have enough capacity to represent certain functions? 2. Optimization: Why do gradient-based methods converge, even in highly non-convex parameter spaces? 3. Generalization: How can large, overparameterized models still avoid overfitting and achieve good test performance? The course will present students with both developments in classical statistical learning (VC theory, Rademacher complexity, uniform convergence, PAC-Bayes analysis) and more recent results in deep learning theory, such as neural tangent kernels (NTK), overparameterization, double descent, and their applications. With these theoretical viewpoints, students will develop the mathematical maturity and analytical skills needed to address research problems in deep learning analysis and design.
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CSC 440-01
Monika Polak
TR 2:00PM - 3:15PM
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This course is designed to develop your understanding of the fundamental concepts in data mining and how they can be used to answer questions about real-world problems. These methods, and the underlying models, can be used to learn from existing data to make predictions about new data. The course will help you understand the mindset of creating a data project pipeline by covering topics on model development, causal inference, and traditional data mining. The class will both emphasize individual work and also team work by helping students solidify their knowledge through a final project.
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CSC 441-01
Ralf Haefner
TR 9:40AM - 10:55AM
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IThis is a rotating topics course that includes the study of both the computations performed by the brain and of computational models of neuronal responses. Primary focus will be on the visual system. This course is taught at an introductory level in odd numbered years and an advanced level in even numbered years.
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CSC 442-01
Daniel Gildea
TR 3:25PM - 4:40PM
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Introduces fundamental principles and techniques from Artificial Intelligence, including heuristic search, automated reasoning, handling uncertainty, and machine learning, to prepare students for advanced AI courses.
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CSC 445-01
Chenliang Xu
TR 9:40AM - 10:55AM
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Much of the recent advances in machine learning and artificial intelligence have been dominated by neural network approaches broadly described as deep learning. This course provides an overview of the most important deep learning techniques covering both theoretical foundations and practical applications. The applications focus on problems in image understanding and language modeling utilizing state-of-the-art deep learning libraries and tools, which are introduced in the course.
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CSC 452-01
Yanan Guo
MW 2:00PM - 3:15PM
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Introduction to computer architecture and the layering of hardware/software systems. Topics include instruction set design; logical building blocks; computer arithmetic; processor organization; the memory hierarchy (registers, caches, main memory, and secondary storage); I/Obuses, devices, and interrupts; microcode and assembly language; virtual machines; the roles of the assembler, linker, compiler, and operating system; technological trends and the future of computing hardware. Several programming assignments required.
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CSC 453-01
Chen Ding
MW 3:25PM - 4:40PM
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Software design is a critical discipline because modern software systems are too complex for any single individual to fully comprehend, yet they must be designed to avoid causing harm to the people they serve. This course focuses on the collaborative construction of software by teams. The curriculum covers: 1. Design Principles and Practices: Information hiding, software architectures, work assignments, team organization, iterative development, and documentation. 2. Safe Programming in Rust: Generics and traits, ownership and borrowing rules, safe pointers, modules, and design patterns. 3. AI Assistance: Automated code and test generation, specialization, and coordination by coding agents. 4. Ethical Principles: Fairness and human fallibility. Assignments emphasize teamwork in software design and development. Students enrolled in CSC 453 are also required to learn Rust meta-programming
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CSC 456-01
John Criswell
TR 2:00PM - 3:15PM
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Principles of operating system design, explored within the practical context of traditional, embedded, distributed, and real-time operating systems. Topics include device management, process management, scheduling, synchronization principles, memory management and virtual memory, file management and remote files, protection and security, fault tolerance, networks, and distributed computing. CSC 456, a graduate-level course, requires additional readings and assignments.
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CSC 457-01
Adam Purtee
MW 4:50PM - 6:05PM
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Introduction to computer networks and computer communication: Architecture and Protocols:. Design of protocols for error recovery, reliable delivery, routing and congestion control. Store-and-forward networks, satellite networks, local area networks and locally distributed systems. Case studies of networks, protocols and protocol families. Emphasis on software design issues in computer communication.
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CSC 459-01
Yuhao Zhu
WF 9:00AM - 10:15AM
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This is NOT a computer vision class. Computer vision is concerned with understanding images, whereas in this class we are mostly concerned with how images are formed in the first place. In particular, we will study three fundamental forms of image formation: biological (human eye and retina), electrical (cameras), and computational (computer graphics).
Why do we care about image formation? First, it's a wonderfully rich topic that covers sciences (e.g., physics, optics, visual neuroscience), engineering (e.g., camera/display design, Augmented/Virtual Reality glass design), computation (e.g., rendering algorithms, computational photography, image/video compression algorithms), art (e.g., paintings, pentimento), and mathematics (e.g., linear systems, Fourier analysis). Second, understanding image formation allows us to better design vision systems (both computer vision and human vision) — if we don't understand how an image is formed in the first place, how can we analyze it to extract useful information?
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CSC 461-01
Eustrat Zhupa
TR 12:30PM - 1:45PM
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This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project.
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CSC 461-02
Eustrat Zhupa
T 6:15PM - 7:30PM
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This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project.
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CSC 461-03
Eustrat Zhupa
W 3:25PM - 4:40PM
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This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project.
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CSC 461-04
Eustrat Zhupa
W 4:50PM - 6:05PM
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This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project.
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CSC 461-05
Eustrat Zhupa
R 4:50PM - 6:05PM
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This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project.
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CSC 461-06
Eustrat Zhupa
R 6:15PM - 7:30PM
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This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project.
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CSC 462-01
Anson Kahng
TR 4:50PM - 6:05PM
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This course will cover foundational concepts in descriptive analyses, probability, and statistical inference. Topics to be covered include data exploration through descriptive statistics (with a heavy emphasis on using R for such analyses), elementary probability, diagnostic testing, combinatorics, random variables, elementary distribution theory, statistical inference, and statistical modeling. The inference portion of the course will focus on building and applying hypothesis tests and confidence intervals for population means, proportions, variances, and correlations. Non-parametric alternatives will also be introduced. The modeling portion of the course will include ANOVA, and simple and multiple regression and their respective computational methods. Students will be introduced to the R statistical computing environment.
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CSC 464-01
Zhiyao Duan
TR 12:30PM - 1:45PM
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Computer audition is the study of how to design a computational system that can analyze and process auditory scenes. Example problems in this field include source separation (splitting audio mixtures into individual source tracks), pitch estimation (estimating the pitches played by each instrument), timbre modeling (finding features to distinguish different kinds of instruments), and source localization (finding where the sound comes from). This course will cover both fundamentals and state-of-the-art research in this field, which applies various kinds of signal processing and machine learning techniques. Multiple programming assignments will help students practice what they learn, and a final research project will lead students through the entire research process.
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CSC 474-01
Gaurav Sharma
MW 4:50PM - 6:05PM
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Classical computation models and complexity classes, linear algebra formulation of quantum mechanics, quantum computation models, qubits, quantum circuits, and quantum computation complexity classes, Glover's search and Shor's factorization quantum algorithms, adiabatic quantum computation. Prerequisites: Linear Algebra (UR Math 165 or equivalent), College Physics (UR PHYS 122 or equivalent), or instructor permission
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CSC 476-01
Yanan Guo
MW 9:00AM - 10:15AM
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This course delves into advanced topics in computer architecture such as out-of-order execution, speculative execution, cache protocols, and advanced memory techniques. It will also introduce the security problems caused by these designs, such as side-channel attacks, Rowhammer attacks, and Spectre/Meltdown vulnerabilities. Students will learn about the design of modern processors and their impact on performance and security.
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CSC 477-01
Christopher Kanan
MW 3:25PM - 4:40PM
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Deep learning systems are now being widely productionized at large corporations and many AI-centric start-ups have been created. Productionizing AI systems requires more than just algorithmic considerations. We need to organize the data for training these systems, measure the bias present in these systems after training them, monitor them over time, and more. This course covers these topics, including, but are not limited to, deploying AI systems, MLOps, model versioning, dataset curation, data management, AI ethics/fairness, detecting and mitigation of bias, detecting out-of-distribution inputs, domain shift, data-centric AI, real-time machine learning, continual machine learning, monitoring AI systems after deployment, model/data parallelism, managing AI projects/teams, training and inference on edge-devices, and launching AI start-ups.
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CSC 480-01
Monika Polak
MW 9:00AM - 10:15AM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
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CSC 480-02
Monika Polak
M 4:50PM - 6:05PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
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CSC 480-04
Monika Polak
W 6:15PM - 7:30PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
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CSC 480-07
Monika Polak
T 6:15PM - 7:30PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
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CSC 481-01
Andrew Read-McFarland
MW 2:00PM - 3:15PM
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The modern study of cryptography investigates techniques for facilitating interactions between distrustful entities. In this course we introduce some of the fundamental concepts of this study. Emphasis will be placed on the foundations of cryptography and in particular on precise definitions and proof techniques. The main skills that will be assumed from these courses are: the ability to understand and write formal mathematical definitions and proofs and 2) comfort with reasoning about algorithms, such as proving their correctness and analyzing their running times. It is also important that you are familiar with basic probability.
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CSC 482-01
Daniel Stefankovic
TR 11:05AM - 12:20PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
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CSC 482-02
Daniel Stefankovic
T 6:15PM - 7:30PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
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CSC 482-03
Daniel Stefankovic
W 6:15PM - 7:30PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
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CSC 482-04
Daniel Stefankovic
M 6:15PM - 7:30PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
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CSC 482-05
Daniel Stefankovic
W 7:40PM - 8:55PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
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CSC 484-01
Daniel Stefankovic
MW 9:00AM - 10:15AM
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Advanced study of design and analysis of algorithms. Topics typically include: growth of functions; recurrences; probabilistic analysis and randomized algorithms; maximum flow; sorting networks; expander graphs; matrix operations; linear programming; discrete Fourier transform; number-theoretic algorithms; string matching; computational geometry; NP-completeness; approximation algorithms. Students taking this course at the 400 level may be required to complete additional tests, readings or assignments.
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CSC 489-01
Anson Kahng
MW 4:50PM - 6:05PM
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This class will provide an introduction to topics at the intersection of computer science and economics, including game theory, auctions, incentive-compatible mechanism design, matching algorithms, human computation (e.g., crowdsourcing and peer prediction), trust and reputation systems, and social choice (voting) theory. We hope to convey the fact that the relationship between computer science and economics is a two-way street: It is important to consider incentives and strategic action are important when designing computation-intensive systems, and efficient and robust algorithms enable the deployment of impactful economic ideas.
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CSC 495-01
Zhiyao Duan
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-02
John Criswell
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-03
Zhen Bai
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-04
Chen Ding
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-05
Gaurav Sharma
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-06
Daniel Gildea
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-07
Lane Hemaspaandra
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-08
Ehsan Hoque
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-09
Yukang Yan
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-10
Jiebo Luo
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-11
Fatemeh Nargesian
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-12
Yanan Guo
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-13
Sreepathi Pai
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-14
Lenhart Schubert
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-15
Zhiyao Duan
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-16
Daniel Stefankovic
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-17
Muthu Venkitasubramaniam
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-18
Chenliang Xu
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-19
Yuhao Zhu
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-20
Aaron White
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-21
Thomas Howard
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-22
Christopher Kanan
7:00PM - 7:00PM
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 577-01
Jiebo Luo
TR 12:30PM - 1:45PM
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This is a seminar course with varying topics in the area of artificial Intelligence. May not be taught every year.
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CSC 595-01
John Criswell
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-02
Jiaming Liang
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-03
Zhen Bai
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-04
Chen Ding
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-05
Xiangxiang Xu
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-06
Daniel Gildea
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-07
Lane Hemaspaandra
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-08
Ehsan Hoque
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-09
Yukang Yan
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-10
Jiebo Luo
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-11
Fatemeh Nargesian
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-13
Sreepathi Pai
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-14
Zhiyao Duan
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-16
Daniel Stefankovic
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-17
Hangfeng He
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-18
Chenliang Xu
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-19
Yuhao Zhu
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-20
Aaron White
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-21
Gonzalo Mateos Buckstein
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-22
Thomas Howard
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-23
Gregory Heyworth
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-24
Christopher Kanan
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-25
Mujdat Cetin
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-26
Anson Kahng
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-28
Kaave Hosseini
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-29
Adam Sefkow
7:00PM - 7:00PM
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 597-02
–
M 11:50AM - 1:05PM
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No description
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CSC 895-01
–
7:00PM - 7:00PM
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This course provides master's students the opportunity to work part-time on their thesis/dissertation. Students will make progress toward completing degree requirements.
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CSC 897-01
Daniel Gildea
7:00PM - 7:00PM
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This course provides master's students who are currently completing their final required coursework, or with special circumstances like an approved reduced courseload, with the opportunity to work full-time on their degrees. Students will make significant progress toward completing their degrees.
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CSC 897A-01
Daniel Gildea
7:00PM - 7:00PM
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This course provides master's students who are currently completing their final required coursework, or with special circumstances like an approved reduced courseload, with the opportunity to work full-time on their degrees while studying in absentia. Students will make significant progress toward completing their degrees.
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CSC 899-01
Daniel Gildea
7:00PM - 7:00PM
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This course provides master's students the opportunity to work full-time on their thesis/dissertation. Students will make significant progress toward completing degree requirements.
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CSC 986V-01
–
7:00PM - 7:00PM
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This course affords graduate students visiting the University of Rochester full-time student status. Visiting students will engage in research, discussion, and/or professional training in partnership with an academic department or faculty member.
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CSC 986V-02
Yukang Yan
7:00PM - 7:00PM
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This course affords graduate students visiting the University of Rochester full-time student status. Visiting students will engage in research, discussion, and/or professional training in partnership with an academic department or faculty member.
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CSC 995-01
Daniel Gildea
7:00PM - 7:00PM
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This course provides doctoral students the opportunity to work on their dissertation. Students will make progress toward completing degree requirements.
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CSC 999-01
Daniel Gildea
7:00PM - 7:00PM
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This course provides doctoral students the opportunity to work full-time on their dissertation in absentia. Students will make significant progress toward completing degree requirements.
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Fall 2026
| Number | Title | Instructor | Time |
|---|---|
| Monday | |
|
CSC 597-02
–
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No description |
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CSC 480-02
Monika Polak
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|
This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness. |
|
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CSC 482-04
Daniel Stefankovic
|
|
|
How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems. |
|
| Monday and Wednesday | |
|
CSC 439-01
Xiangxiang Xu
|
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|
This course provides a theoretical foundation for analyzing and understanding machine learning algorithms, with a particular focus on deep learning. Our discussions cover key factors for understanding the behaviors of classical learning algorithms and deep neural networks, including: 1. Approximation: When do neural networks have enough capacity to represent certain functions? 2. Optimization: Why do gradient-based methods converge, even in highly non-convex parameter spaces? 3. Generalization: How can large, overparameterized models still avoid overfitting and achieve good test performance? The course will present students with both developments in classical statistical learning (VC theory, Rademacher complexity, uniform convergence, PAC-Bayes analysis) and more recent results in deep learning theory, such as neural tangent kernels (NTK), overparameterization, double descent, and their applications. With these theoretical viewpoints, students will develop the mathematical maturity and analytical skills needed to address research problems in deep learning analysis and design. |
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CSC 476-01
Yanan Guo
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This course delves into advanced topics in computer architecture such as out-of-order execution, speculative execution, cache protocols, and advanced memory techniques. It will also introduce the security problems caused by these designs, such as side-channel attacks, Rowhammer attacks, and Spectre/Meltdown vulnerabilities. Students will learn about the design of modern processors and their impact on performance and security. |
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CSC 480-01
Monika Polak
|
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness. |
|
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CSC 484-01
Daniel Stefankovic
|
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|
Advanced study of design and analysis of algorithms. Topics typically include: growth of functions; recurrences; probabilistic analysis and randomized algorithms; maximum flow; sorting networks; expander graphs; matrix operations; linear programming; discrete Fourier transform; number-theoretic algorithms; string matching; computational geometry; NP-completeness; approximation algorithms. Students taking this course at the 400 level may be required to complete additional tests, readings or assignments. |
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CSC 413-01
Daniel Nikolov
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This course provides a broad introduction to augmented and virtual reality (AR/VR) systems. The course involves lectures and workshops covering an overview of all aspects of the AR/VR domain, as well as an individual class project developing an AR/VR demonstration of an application chosen by each student. Topics covered in the lectures and class workshops include history, conceptual origins, and design/evaluation principles of AR/VR technologies; optical architectures; audio and visual perception; graphics and computation; data processing and machine intelligence; introduction to AR/VR developer tools; societal implications and ethical aspects. At the end of the course, students will have gained familiarity with the techniques, languages, and cultures of fields integral to the convergent research theme of AR/VR. This course includes workshops and project demonstrations in Studio X, as well as several guest lectures. |
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CSC 420-01
Sreepathi Pai
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This advanced course is aimed at students who wish to build and use efficient large-scale AI systems. It condenses CSC 252, CSC 254, CSC 258, and CSC 255, collating material from those courses that are most relevant to modern AI systems. Topics covered include machine learning (ML) applications, ML programming models, code generation and optimization, manual and automatic parallelization, performance modeling, systems-level challenges, and specialized AI hardware. No background in AI or ML is expected, but proficiency in programming is expected. Prerequisites: CSC 173, CSC 242 |
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CSC 452-01
Yanan Guo
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Introduction to computer architecture and the layering of hardware/software systems. Topics include instruction set design; logical building blocks; computer arithmetic; processor organization; the memory hierarchy (registers, caches, main memory, and secondary storage); I/Obuses, devices, and interrupts; microcode and assembly language; virtual machines; the roles of the assembler, linker, compiler, and operating system; technological trends and the future of computing hardware. Several programming assignments required. |
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CSC 481-01
Andrew Read-McFarland
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The modern study of cryptography investigates techniques for facilitating interactions between distrustful entities. In this course we introduce some of the fundamental concepts of this study. Emphasis will be placed on the foundations of cryptography and in particular on precise definitions and proof techniques. The main skills that will be assumed from these courses are: the ability to understand and write formal mathematical definitions and proofs and 2) comfort with reasoning about algorithms, such as proving their correctness and analyzing their running times. It is also important that you are familiar with basic probability. |
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CSC 453-01
Chen Ding
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Software design is a critical discipline because modern software systems are too complex for any single individual to fully comprehend, yet they must be designed to avoid causing harm to the people they serve. This course focuses on the collaborative construction of software by teams. The curriculum covers: 1. Design Principles and Practices: Information hiding, software architectures, work assignments, team organization, iterative development, and documentation. 2. Safe Programming in Rust: Generics and traits, ownership and borrowing rules, safe pointers, modules, and design patterns. 3. AI Assistance: Automated code and test generation, specialization, and coordination by coding agents. 4. Ethical Principles: Fairness and human fallibility. Assignments emphasize teamwork in software design and development. Students enrolled in CSC 453 are also required to learn Rust meta-programming |
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CSC 477-01
Christopher Kanan
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Deep learning systems are now being widely productionized at large corporations and many AI-centric start-ups have been created. Productionizing AI systems requires more than just algorithmic considerations. We need to organize the data for training these systems, measure the bias present in these systems after training them, monitor them over time, and more. This course covers these topics, including, but are not limited to, deploying AI systems, MLOps, model versioning, dataset curation, data management, AI ethics/fairness, detecting and mitigation of bias, detecting out-of-distribution inputs, domain shift, data-centric AI, real-time machine learning, continual machine learning, monitoring AI systems after deployment, model/data parallelism, managing AI projects/teams, training and inference on edge-devices, and launching AI start-ups. |
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CSC 457-01
Adam Purtee
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Introduction to computer networks and computer communication: Architecture and Protocols:. Design of protocols for error recovery, reliable delivery, routing and congestion control. Store-and-forward networks, satellite networks, local area networks and locally distributed systems. Case studies of networks, protocols and protocol families. Emphasis on software design issues in computer communication. |
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CSC 474-01
Gaurav Sharma
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|
Classical computation models and complexity classes, linear algebra formulation of quantum mechanics, quantum computation models, qubits, quantum circuits, and quantum computation complexity classes, Glover's search and Shor's factorization quantum algorithms, adiabatic quantum computation. Prerequisites: Linear Algebra (UR Math 165 or equivalent), College Physics (UR PHYS 122 or equivalent), or instructor permission |
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CSC 489-01
Anson Kahng
|
|
|
This class will provide an introduction to topics at the intersection of computer science and economics, including game theory, auctions, incentive-compatible mechanism design, matching algorithms, human computation (e.g., crowdsourcing and peer prediction), trust and reputation systems, and social choice (voting) theory. We hope to convey the fact that the relationship between computer science and economics is a two-way street: It is important to consider incentives and strategic action are important when designing computation-intensive systems, and efficient and robust algorithms enable the deployment of impactful economic ideas. |
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| Tuesday | |
|
CSC 461-02
Eustrat Zhupa
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|
This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project. |
|
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CSC 480-07
Monika Polak
|
|
|
This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness. |
|
|
CSC 482-02
Daniel Stefankovic
|
|
|
How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems. |
|
| Tuesday and Thursday | |
|
CSC 435-01
Jiaming Liang
|
|
|
This course primarily focuses on algorithms for large-scale optimization problems arising in machine learning and data science applications. The first part will cover first-order methods including gradient and subgradient methods, mirror descent, proximal gradient method, accelerated gradient method, Frank-Wolfe method, and inexact proximal point methods. The second part will introduce algorithms for nonconvex optimization, stochastic optimization, distributed optimization, manifold optimization, reinforcement learning, and those beyond first-order. |
|
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CSC 441-01
Ralf Haefner
|
|
|
IThis is a rotating topics course that includes the study of both the computations performed by the brain and of computational models of neuronal responses. Primary focus will be on the visual system. This course is taught at an introductory level in odd numbered years and an advanced level in even numbered years. |
|
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CSC 445-01
Chenliang Xu
|
|
|
Much of the recent advances in machine learning and artificial intelligence have been dominated by neural network approaches broadly described as deep learning. This course provides an overview of the most important deep learning techniques covering both theoretical foundations and practical applications. The applications focus on problems in image understanding and language modeling utilizing state-of-the-art deep learning libraries and tools, which are introduced in the course. |
|
|
CSC 482-01
Daniel Stefankovic
|
|
|
How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems. |
|
|
CSC 461-01
Eustrat Zhupa
|
|
|
This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project. |
|
|
CSC 464-01
Zhiyao Duan
|
|
|
Computer audition is the study of how to design a computational system that can analyze and process auditory scenes. Example problems in this field include source separation (splitting audio mixtures into individual source tracks), pitch estimation (estimating the pitches played by each instrument), timbre modeling (finding features to distinguish different kinds of instruments), and source localization (finding where the sound comes from). This course will cover both fundamentals and state-of-the-art research in this field, which applies various kinds of signal processing and machine learning techniques. Multiple programming assignments will help students practice what they learn, and a final research project will lead students through the entire research process. |
|
|
CSC 577-01
Jiebo Luo
|
|
|
This is a seminar course with varying topics in the area of artificial Intelligence. May not be taught every year. |
|
|
CSC 440-01
Monika Polak
|
|
|
This course is designed to develop your understanding of the fundamental concepts in data mining and how they can be used to answer questions about real-world problems. These methods, and the underlying models, can be used to learn from existing data to make predictions about new data. The course will help you understand the mindset of creating a data project pipeline by covering topics on model development, causal inference, and traditional data mining. The class will both emphasize individual work and also team work by helping students solidify their knowledge through a final project. |
|
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CSC 456-01
John Criswell
|
|
|
Principles of operating system design, explored within the practical context of traditional, embedded, distributed, and real-time operating systems. Topics include device management, process management, scheduling, synchronization principles, memory management and virtual memory, file management and remote files, protection and security, fault tolerance, networks, and distributed computing. CSC 456, a graduate-level course, requires additional readings and assignments. |
|
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CSC 442-01
Daniel Gildea
|
|
|
Introduces fundamental principles and techniques from Artificial Intelligence, including heuristic search, automated reasoning, handling uncertainty, and machine learning, to prepare students for advanced AI courses. |
|
|
CSC 400-01
George Ferguson
|
|
|
An introduction to the technical, social, economic, and political aspects of graduate education in computer science at Rochester. Class meetings consist primarily of group discussions and presentations that focus on a broad range of topics, and are intended to improve the critical analysis, technical writing, presentation, and problem-solving skills of students. Both class discussions and written assignments are drawn from material presented in other first-year graduate courses offered within the department. The course also offers a forum for individual department faculty members to discuss their research interests and recent results. Satisfactory performance is required of all first-year PhD students. |
|
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CSC 462-01
Anson Kahng
|
|
|
This course will cover foundational concepts in descriptive analyses, probability, and statistical inference. Topics to be covered include data exploration through descriptive statistics (with a heavy emphasis on using R for such analyses), elementary probability, diagnostic testing, combinatorics, random variables, elementary distribution theory, statistical inference, and statistical modeling. The inference portion of the course will focus on building and applying hypothesis tests and confidence intervals for population means, proportions, variances, and correlations. Non-parametric alternatives will also be introduced. The modeling portion of the course will include ANOVA, and simple and multiple regression and their respective computational methods. Students will be introduced to the R statistical computing environment. |
|
| Wednesday | |
|
CSC 461-03
Eustrat Zhupa
|
|
|
This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project. |
|
|
CSC 461-04
Eustrat Zhupa
|
|
|
This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project. |
|
|
CSC 480-04
Monika Polak
|
|
|
This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness. |
|
|
CSC 482-03
Daniel Stefankovic
|
|
|
How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems. |
|
|
CSC 482-05
Daniel Stefankovic
|
|
|
How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems. |
|
| Wednesday and Friday | |
|
CSC 459-01
Yuhao Zhu
|
|
|
This is NOT a computer vision class. Computer vision is concerned with understanding images, whereas in this class we are mostly concerned with how images are formed in the first place. In particular, we will study three fundamental forms of image formation: biological (human eye and retina), electrical (cameras), and computational (computer graphics).
Why do we care about image formation? First, it's a wonderfully rich topic that covers sciences (e.g., physics, optics, visual neuroscience), engineering (e.g., camera/display design, Augmented/Virtual Reality glass design), computation (e.g., rendering algorithms, computational photography, image/video compression algorithms), art (e.g., paintings, pentimento), and mathematics (e.g., linear systems, Fourier analysis). Second, understanding image formation allows us to better design vision systems (both computer vision and human vision) — if we don't understand how an image is formed in the first place, how can we analyze it to extract useful information? |
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| Thursday | |
|
CSC 461-05
Eustrat Zhupa
|
|
|
This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project. |
|
|
CSC 461-06
Eustrat Zhupa
|
|
|
This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project. |
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