If you have any concerns or questions, feel free to reach out to the Instructor listed (for course content questions) or grad-advising@cs (for general registration troubleshooting). You can also schedule an appointment with a grad adviser if needed. If you are interested in taking a CSE 500-level course and need an add code (any non-major), then please review the enrollment petition information on the CSE Non-Major Registration page under "Ph.D./Doctoral Courses (CSE 500 level)".

Each section has a field designated Non-major Enrollment: that provides information on if the course allows for students outside the Allen School Ph.D. program, and how to apply if it does. If you are an Allen School student (Ph.D. or 5th-year), you typically will not need an add code or to apply for enrollment. You can contact grad-advising@cs for registration assistance if needed.

The section marked with an Mailing List: is the mailing list for the course. You're welcome to use it to contact the instructors for questions.

    • Description: Consider a function f on R^n that can be written as a sum of a large number of functions f = f_1 + f_2 + ... from some class. When can this sum be sparsified in the sense that f can be approximated by a (nonnegative) weighted combination of a small number of the {f_i}? For many classes of functions, the answer is that, surprisingly, only about O(n) summands suffice (up to logarithmic factors). When this is possible, one can address the algorithmic questions: How to efficiently construct the sparsifier, and how to use sparse representations to optimize f efficiently.

      This simple question has a wealth of applications in CS (especially in big data analysis), as well as in many areas of mathematics (especially in functional analysis and convex geometry). The course will focus on the (often deep) mathematical structures underlying sparsification problems, covering things like: Leverage scores, Lewis weights, generalized linear regression, spectral sparsification of graphs, matrix concentration, generic chaining theory, subspace embeddings and dimension reduction, submodularity, and low-rank approximation in numerical linear algebra.

    • Description: An introduction to quantum information and computation aimed at graduate students. Qubits, quantum gates, and measurements. Entanglement and non-locality. Density matrix formalism. Quantum algorithms: Simon’s, Grover search, Shor’s factoring, and Hamiltonian Simulation.

      Ph.D. Quals Breadth Course approval pending faculty review.

    • Description: From Excel to Tableau to Jupyter and beyond, we interact with a variety of analysis systems on a regular basis to advance our educational and professional goals. However, as we continue to accumulate unprecedented amounts of data in our personal and professional lives, we can easily lose traction and even access to understanding and interpreting this data in an intuitive way. How do we design easy-to-use systems for exploring and understanding large, complex datasets? What factors influence how we design these systems? In this course, we will consider qualitative and quantitative methods as well as system performance and user experience-driven perspectives in the design and evaluation of interactive data analysis systems. We will review foundational and recent research as well as a wide range of developed systems in this space. Students will read, present on, and discuss relevant research papers and complete a team-based final project. Prior exposure to data visualization research, HCI research, or data management research (such as through a previous course) is encouraged but not required.
    • Non-major Enrollment: Open to UW Graduate Students starting Period II; Ugrads may use the CSE Doctoral Course Enrollment Request.
    • Description:Recent advances in AI (including the versatile transformer neural network architecture, generative pretraining, and multimodal modeling) hint at the potential for advances on longstanding music processing problems as well as new applications. This seminar/project course aims to explore the recent research literature contributing toward AI tools serving musicians. Participants will work in teams to motivate, plan, and implement new projects. Throughout the course, the focus will be on musicians as users of the technology, not general consumers as users. Applications under discussion may include: automatic transcription (converting performance audio to symbolic notation), rendering (converting symbolic notation to visual formats), score-performance synchronization (in real time or not), performance analysis, audio transformations (e.g., source separation for music), and other kinds of music audio analysis (e.g., chord identification, beat/tempo tracking). Discussion of evaluation methodology and dataset development is also encouraged.
    • Non-major Enrollment: Open to all UW students starting Period II.
    • Description:Computing Education Research (CER) is the study of how people learn to use programmable and/or trainable technologies. It includes computer science education, as well as other contexts where people learn how to boss computers around to pursue their own interests. Accountants learning to program macros in Excel, biologists learning Python to crunch their data, and children creating interactive stories in Scratch are all people and activities that CER attends to. So are experienced software engineers learning to weave machine learning into their applications, CS graduate students getting their heads around dependent types, and teachers figuring out how to integrate computing into their courses. CER studies learning, and how to support it, using a variety of methods, including design.

      This graduate seminar will provide students with a broad understanding of the history and state of the field, including classic systems and research as well as emerging areas of inquiry. We will read and discuss papers from CER, statistics education, science education, and the learning sciences. Students will write a research proposal that charts how we could deepen our collective knowledge about how people learn computing.
    • Non-major Enrollment: All interested students should use the CSE Doctoral Course Enrollment Request.
    • Description:Only open to Allen School BS/MS students.
    • CSE 599 (I): Open Problems in Communication Complexity
    • Instructor: Anup Rao (he/him)
    • Course Website: TBA
    • Description:If two or more parties want to compute some joint function of their inputs, how long does their conversation need to be? Communication complexity is the mathematical study of this question. Because every computational process involves communication between components, this area has proven to be.a "master method" for proving lower bounds on computational processes. Many of the best lower bounds on families of circuits, data structures, linear programs, all rely on communication complexity.

      In this class, we shall introduce the basic concepts of communication complexity and explore some of the best applications. The focus will be on talking about the big open problems in the area. The course will involve several long lectures where I discuss my current attempts to make progress on showing that communication is almost-linear in communication, the logrank conjecture, and proving lower bounds in the number-on-forehead model. Along the way, we shall learn about advanced topics from convex geometry, information theory and Fourier analysis that are relevant to these topics.
    • Non-major Enrollment: Open to all UW students starting Period II.
    • Description:Many advances in machine learning over the past decade have been powered by the increasing availability of larger and more diverse datasets. Where do these datasets come from? What issues are present in these datasets, and how might we deal with them? This course will study questions around how we can better use our available data for training, at inference, and for evaluation. Potential topics include: dataset construction and curation; dataset biases; data augmentation and generation; distribution shifts; data poisoning; data attribution; privacy and copyright; and retrieval models that use an external datastore for inference. The course will be primarily based around paper reading and discussions, with an open-ended course project.
    • Non-major Enrollment: All interested students should use the CSE Doctoral Course Enrollment Request.
    • Description: This class is about taking derivatives of programs (automatic differentiation) and interpreting programs as probabilistic models: change and chance. We will take a broad and eclectic view on these subjects: what does it mean to take derivatives of discrete programs? How do you conditionally sample from a program? Is the derivative of a computable function always computable? How can you verify a randomized program? We will cover a range of interesting applications including but not limited to physical simulation, rendering, 3d reconstruction problems, cognitive science models, inverse design problems, etc.
    • Non-major Enrollment: Open to UW Graduate Students starting Period II; Ugrads may use the CSE Doctoral Course Enrollment Request.
    • Description: Foundations of modern reinforcement learning. Topics may include Markov Decision Processes, Value Iteration, Policy Iteration, Approximate Dynamic Programming, Temporal Difference Learning, Q-Learning, Policy Gradients, and Imitation Learning.
    • Non-major Enrollment: All interested students should use the CSE Doctoral Course Enrollment Request.
    • Description: This course is about explainable artificial intelligence (XAI), a subfield of machine learning that provides transparency for complex models. Modern machine learning relies heavily on black-box models like tree ensembles and deep neural networks; these models provide state-of-the-art accuracy, but they make it difficult to understand the features, concepts, and data examples that drive their predictions. As a consequence, it's difficult for users, experts, and organizations to trust such models, and it's challenging to learn about the underlying processes we're modeling.

      In response, some argue that we should rely on inherently interpretable models in high-stakes applications, such as medicine and consumer finance. Others advocate for post hoc explanation tools that provide a degree of transparency even for complex models. This course explores both perspectives, and we'll discuss a wide range of tools that address different questions about how models make predictions. We'll cover many active research areas in the field, including feature attribution, counterfactual explanations, instance explanations, and human-AI collaboration.
    • Non-major Enrollment: All interested students should use the CSE Doctoral Course Enrollment Request.
    • CSE 599 (I): Exponential time hypotheses, fine-grain complexity, and lifting
    • Instructor: Paul Beame (he/him)
    • Course Website: TBA
    • Description: This course will focus on two subjects in computational complexity that have particularly come to the fore in recent years.
      Exponential time hypotheses and fine-grain complexity: Beyond P vs NP
      Despite the best efforts of researchers for over 50 years since the P vs NP question was formulated, the best algorithms we have for SAT and other NP-complete problems are still exponential in the worst case and barely improve on brute force. If these are the correct level of complexity, which seems a reasonable stronger conjecture than P != NP, our usual ways of proving relationships between NP problems need to be rethought, both from a theoretical and practical point of view, and radically new relationships between problems emerge, a subject termed ""fine-grain complexity"". This yields surprising connections that have produced a web of problem-solving relationships well beyond the usual resource-focused complexity classes, for example, showing how improving existing polynomial-time algorithms for well-known problems is tied to improving exponential-time algorithms for SAT.
      Lifting
      A number of longstanding problems in computational complexity have been resolved in the last decade by showing how simple forms of function composition let us convert hardness results proven in weak models of computation into hardness results for more powerful models of computation, a methodology that has been termed "lifting". We revisit lifting techniques and the some of the longstanding problems resolved using them. We then focus on a number of open problems and approaches in resolving them.
    • Non-major Enrollment: All interested students should use the CSE Doctoral Course Enrollment Request.
    • Description: How can we accelerate AI when learning in an environment with other intelligent agents? This course focuses on Social Reinforcement Learning in multi-agent and human-AI interactions. From studying the natural world, we know that social learning is an incredibly powerful mechanism that helps both humans and animals rapidly adapt to new circumstances, coordinate with others, and drives the emergence of complex learned behaviors. From recent advances in AI, we know that reinforcement learning from human feedback (RLHF) is an incredibly powerful mechanism for improving the capabilities and alignment of large models. This course will link these two perspectives, examining the complexities of modeling, learning from, and coordinating with other agents, whether those agents are humans or other RL agents in a simulation. We will study how social learning can address fundamental issues in AI like learning and generalization, as well as improving the ability of AI to coordinate with and interact with people.

      The course will tentatively cover the following topics: multi-agent RL, coordinating with other agents (including zero-shot coordination with humans), emergent complexity, social learning, learning from human feedback across multiple domains, and RLHF for language models. Although we will cover a brief introduction to reinforcement learning (RL), familiarity with RL and deep learning is encouraged. The course is a project course; in addition to reading and discussing relevant research papers, students will submit a team-based final project in the form of a research paper.
    • Non-major Enrollment: All interested students should use the CSE Doctoral Course Enrollment Request.
    • Description: Brains are remarkably complex, massive networks of interconnected neurons that underlie our abilities to intelligently sense, reason, learn, and interact with our world. Technologies for monitoring neural activity in the brain are revealing rich structure within the coordinated activity of these interconnected populations of neurons. In this course, we will discuss machine learning models that can be applied toward 1) understanding how neural activity in the brain gives rise to intelligent behavior and 2) designing algorithms for brain-interfacing biomedical devices. Topics will include basic neurobiology, classical probabilistic machine learning foundations, and modern deep learning approaches, including variational autoencoders and recurrent neural networks. Coursework will include readings from the machine learning and computational neuroscience literature, programming assignments, and a final modeling project applied to neural population data.
    • Non-major Enrollment: All interested students should use the CSE Doctoral Course Enrollment Request. Allen School undergrads who have taken CSE446/546, anyone from the UW Neuroscience Graduate Program, and Allen School Ph.D. and MS students are can contact grad-advising@cs directly for an add code.
MyPlan

  • Collaborative course offerings for 23-24 will be updated as information becomes available.

    • Description: A taste of current research in Computational Biology (local and non-) + critical reading of literature + presentation skills. Students, with faculty advice, pick and present CompBio papers from recent journals/conferences. Students & faculty also present their own research (mostly in Spring, but may be sprinkled throughout, depending on schedules). Background knowledge of biology is not assumed; come learn!
    • Non-major Enrollment: The seminar is interdisciplinary, and non-majors are welcome. Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.
    • Mailing List: See Webpage
    • Description: Change is a group of faculty, students, and staff at the UW who are exploring the role of information and communication technologies (ICT) in improving the lives of underserved populations, particularly in the developing world (though domestically as well). We cover topics such as global health, education, micro finance, agricultural development, and general communication, and look at how technology can be used to improve each of these areas.
    • Non-major Enrollment: The seminar is interdisciplinary, and non-majors are welcome. Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.
    • Mailing List: http://changemm.cs.washington.edu/mailman/listinfo/change
    • Description: Computer scientists ask what it means for a work or process to be "creative" seriously, seeking formal models and tools to help humans and computers define, explore, and augment solution spaces together. We will cover foundational work on computational approaches to creativity as well as modern applications of this work to fields including the visual arts, music, mathematics, and the sciences.

      The seminar will consist of weekly discussions of readings that help us understand creative processes more formally and computationally. Participants are asked to take one hour a week for reading preparation, and to co-lead one discussion. We have background readings prepared, and are looking forward to selecting additional readings based on everyone's research and hobbies. It should be a fun time!
    • Non-major Enrollment: The seminar is available for all UW students and the content is designed to be widely accessible. Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.
    • Mailing List: N/A
    • Description: Are you interested in discussing different approaches to teaching Computer Science? Are you wondering what kind of research people do in CS education? Are you thinking about a career that involves a lot of CS teaching?A seminar for people interested in discussing topics related to Computer Science education. The format for this quarter will be a weekly discussion of readings from a variety of sources such as CS education conferences (e.g. SIGCSE, ITiCSE, ICER), journal articles on teaching approaches, or excerpts from books on teaching. Participants will be expected to do the readings, participate in weekly discussions, and co-lead one of the discussions.
    • Non-major Enrollment: Complete the 590E enrollment request form.
    • Description: The seminar will consist of weekly discussions of readings that help us understand creative processes more formally and computationally. Participants are asked to take one hour a week for reading preparation, and to co-lead one discussion. We have background readings prepared, and are looking forward to selecting additional readings based on everyone's research and hobbies. It should be a fun time!
    • Non-major Enrollment: Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.
    • Description: A weekly seminar held on Fridays at noon, run by HCI PhD students, where we gather informally to discuss new and foundational HCI literature, get to know one another, and learn together. Typically, it's a mixture of guest speakers, paper discussions, and informal conversations about HCI research. HCI Seminar is intended primarily for graduate students who do HCI research with CSE faculty members.
    • Non-major Enrollment: Open to students who are research-active with relevant CSE faculty. Email grad-advising@cs.washington.edu with your request, specifying who you are research-active with, in order to receive an add code.
    • Description: DUB is a grassroots alliance of faculty, students, researchers, and industry partners interested in Human Computer Interaction & Design at the University of Washington.

      Our mission is to bring together an interdisciplinary group of people to share ideas, collaborate on research, and advance teaching related to the interaction between design, people, and technology.
    • Non-major Enrollment: Student researchers can email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.
    • Mailing List: http://dub.uw.edu/mailman/listinfo/dub
    • Description:
    • Description:
    • Non-major Enrollment: Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.
    • Description:
    • Non-major Enrollment: Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.
    • Description: Weekly seminar organized by database faculty and students where we read papers on exciting topics related to data management.
    • Non-major Enrollment: The seminar is available for all UW students and the content is designed to be widely accessible. Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.
    • Mailing List: data-science-seminars@cs.washington.edu
    • Description: The Robotics Colloquium features talks by invited and local researchers on all aspects of robotics, including control, perception, machine learning, mechanical design, and interaction. The colloquium is held Fridays between 1:30-2:30pm.
    • Non-major Enrollment: Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.
    • Description: Only for Linda Shapiro's research students in CSE, ECE and BIME.
    • Non-major Enrollment: Only students who are doing research with Linda Shapiro.
    • Description: The seminar is for students and faculty members to explore research in accessible computing for people with disabilities in the context of human-computer interaction (HCI). The seminar consists of short student presentations of current research results, followed by discussion and critical evaluations the research.
    • Description: A seminar for first year PhD students focused on enriching students' sense of belonging, camaraderie and purpose in their first year in the program. The seminar also addresses advising, choosing research problems, and the core skills needed to thrive in the Ph.D. program and beyond, including making engaging presentations and writing clearly, time management and work-life balance. Meets 3-4 times per quarter.
    • Description: The focus of this quarter is on papers (to be selected by participants) appearing in recent computer security & privacy or security & privacy-adjacent venues. All enrolled participants are expected to present at least one paper and to attend the rest of the presentations.
    • Non-major Enrollment: Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.
    • Mailing List: https://mailman.cs.washington.edu/mailman/listinfo/uw-security-research
    • Description:
    • MyPlan

    • Description:TBA
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    • CSE 590 (D): Database
    • Instructor: Dan Suciu ()
    • Course Website: TBA
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    • CSE 590 (O): TBA
    • Instructor: Luis Ceze ()
    • Course Website: TBA
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    • MyPlan