Fall 2026 Courses

This is a graduate course in operating systems. The course will consist of a reading/lecture/discussion component and a project component. The class will cover approximately 40 research papers. Some of these papers will introduce students to the basic principles on which modern operating systems and distributed systems are based. Others will cover more recent work to explore the state of the art and observe the evolution of these systems over time.

This course surveys analytic tools that are applicable within several areas of modern theoretical computer science, including pseudorandomness, learning theory, algorithms, and complexity.. Topics may include additive combinatorics and Fourier-analytic methods, Boolean function analysis, concentration and high-dimensional probability, etc.

Many problems in machine learning, visual computing, robotics, and mechanics lack exact analytical solutions. Numerical computing provides powerful techniques to approximate these solutions using computational methods, combining applied math and programming to solve real-world problems such as simulation, optimization, and data analysis. The course begins with a review on calculus and linear algebra, followed by an introduction to how numbers are represented on computers and error analysis. It then moves on to core topics, including matrix factorization, solving linear and nonlinear systems, optimization, and numerical solution of differential equations. Students will gain hands-on experience developing numerical algorithms and learn to balance accuracy, stability, and efficiency in problem-specific contexts, such as image processing, physics-based animation, motion planning and control, structural analysis, etc.

In AI and beyond, systems of multiple agents are naturally modeled using game theory. From game theory, we know that sometimes, when each agent pursues its own objectives, the outcome may be one that is bad for all agents (e.g., the Prisoner's Dilemma). Learning algorithms can indeed converge to such bad equilibria. What can be done to prevent such bad outcomes, and how should we think about designing agents in such contexts? In this course, we will approach this question from a variety of angles, ranging from traditional approaches in game theory to novel ones that fit AI better than humans.

This number is used internally by the department to transfer approved courses for elective credit.

This course number is used for internal course transfer for approved electives outside CSD

The course studies the theory of type systems, with a focus on applications of type systems to practical programming languages. The emphasis is on the mathematical foundations underlying type systems and operational semantics. The course includes a broad survey of the components that make up existing type systems, and also teaches the methodology behind the design of new type systems.

Automated reasoning has become a powerful technology with applications ranging from verification of hardware and software to solving long-standing open problems in mathematics. This course covers several state-of-the-art automated reasoning techniques and provides hands-on experience with research questions in this area.

This is a course exploring research issues in mobile computing and its close relative, pervasive computing (aka "Internet of Things (IoT)"). Many traditional areas of computer science and computer engineering are impacted by the constraints and demands of mobile and pervasive computing. The course will offer significant hands-on experience: students will work in small groups under the guidance of a mentor on a project. Each student will present a research paper from the literature in a conference-style 30-minute talk. In teams of two, students will present a short (30 minutes) overview of the commercial landscape for one of the topics covered in class. There will a brief quiz at the start of each class, based on the readings for that class. Prerequisites Knowledge of operating systems, distributed systems, and computer architecture. If in doubt, check with one of the instructors before registering.

The course covers advanced algorithms for learning, analysis, data management and visualization of large datasets. Topics include indexing for text and DNA databases, searching medical and multimedia databases by content, fundamental signal processing methods, compression, fractals in databases, data mining, privacy and security issues, rule discovery, data visualization, graph mining, stream mining.

Potential topics: Models and Time Hierarchy Theorem. Nondeterminism, padding, Hopcroft-Paul-Valiant Theorem. Circuits and advice. Randomized classes. Cook-Levin Theorem and SAT. Nondeterministic Time Hierarchy Theorem, and nondeterministic models. Oracles, alternation, and the Polynomial Time Hierarchy. Kannan's Theorem, Karp-Lipton, and PH vs. constant-depth circuits. Time-Space tradeoffs for SAT. Randomized classes vs. PH. Interactive proofs and the AM hierarchy. NP in BPP implies PH in BPP, and Boppana-Hastad-Zachos. BCGKT Theorem and Cai's Theorem. Counting classes and the permanent. Valiant's Theorem. Algebraic Complexity. IP = PSPACE and interactive proofs. Instance checkers and Santhanam's Theorem. Random restrictions and AC0 lower bounds for parity. Monotone circuit lower bounds. Razborov-Smolensky lower bounds for AC0[p]. Valiant-Vazirani and Toda Theorems. Beigel-Tarui Theorem. Hardness vs. Randomness and Nisan-Wigderson. Hardness amplification and derandomization. Williams's Theorem. Natural proofs and barriers.

This course is aimed as an introduction to modern cryptography. This course will be a mix of applied and theoretical cryptography.

This course aims to provide an introduction to computational geometry and numerical computation to those with some background in algorithms and geometry. Focus will be in low, especially 2, dimensions. Main topics are plane sweeps, convexity, geometric construction/optimization, optimization algorithms in low to intermediate dimensions, and geometric data structures. Evaluations will consist of 5 homework and a final exam, and will be flexible to allow both theoretical and implementation perspectives.

Computational photography is the convergence of computer graphics, computer vision and imaging. Its role is to overcome the limitations of the traditional camera, by combining imaging and computation to enable new and enhanced ways of capturing, representing, and interacting with the physical world. This advanced undergraduate course provides a comprehensive overview of the state of the art in computational photography. At the start of the course, we will study modern image processing pipelines, including those encountered on mobile phone and DSLR cameras, and advanced image and video editing algorithms. Then we will proceed to learn about the physical and computational aspects of tasks such as 3D scanning, coded photography, lightfield imaging, time-of-flight imaging, VR/AR displays, and computational light transport. Near the end of the course, we will discuss active research topics, such as creating cameras that capture video at the speed of light, cameras that look around walls, or cameras that can see through tissue. The course has a strong hands-on component, in the form of seven homework assignments and a final project. In the homework assignments, students will have the opportunity to implement many of the techniques covered in the class, by both acquiring their own images of indoor and outdoor scenes and developing the computational tools needed to extract information from them. For their final projects, students will have the choice to use modern sensors provided by the instructors (lightfield cameras, time-of-flight cameras, depth sensors, structured light systems, etc.).

The course will focus on multi-step imperfect-information games because most real-world strategic settings are such games. Such games beget additional issues beyond perfect-information games like chess and Go, such as signaling, deception, and understanding deception by others. There has been tremendous progress in the AI community on solving such games since around 2003. This course covers the fundamentals and the state of the art of solving such games.

This course will cover the fundamental concepts quantum computation and quantum computer systems, including recent progress on research topics. Specific topics include the following quantum fundamentals, quantum gates and circuits, quantum circuit synthesis/compilation (including Solovay Kitaev algorithm) quantum circuit optimization (search algorithms and local optimality) quantum simulation (dense and sparse approaches) quantum algorithms In addition to lectures, the course will include a project on a topic chosen by the student (or a group of students). The course is open to all students and especially to students with background is ""classical"" computer science, including algorithms, programming languages, systems (in no particular order).

This course number is used for scheduling distinguished lectures offered throughout the semester.

This orientation course is required for incoming Computer Science Department doctoral students.

This course number is used for doctoral students to register the appropriate units for research associated with work toward on or on their dissertation.

Individualized Experience - Internships