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 Time and Teaching Schedule page under "Course Enrollment Information for Non-majors".

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 icon is the mailing list for the course. You're welcome to use it to contact the instructors for questions.

    • Title: Women In Entrepreneurial Leadership (D)
    • Instructor: Christy Johnson
    • TBA
    • Quarter Offered: Autumn 2022
    MyPlan: ENTRE 579 (D)
    Description: Contact mbaregis@uw.edu for registration information.
    • Title: Directed Research Group: AI Bias Cycle and the Implicit Social Cognition of Machines
    • Instructor: Aylin Caliskan
    • N/A
    • Quarter Offered: Autumn 2022, Winter 2023, Spring 2023
    MyPlan: INSC 578 (A)
    Description:

    We have open research questions on how implicit AI associations and biases propagate from society to models and their decisions. How do biased AI outputs affect humans-in-the-loop in critical decision-making processes as humans interact with AI? How does implicit AI bias impact individuals, society, and equity at scale? What are the ethical implications? How do the compounding effects of these mechanisms shape society and the future generations of AI models? Finally, how do we develop strategies to mitigate bias? Social cognition of machines: We will research why AI automatically learns implicit bias from sociocultural data by developing statistical methods and algorithms, collecting data, training models in controlled experimental settings, and analyzing machine learning models. We will focus on unsupervised and self-supervised AI models such as static word embeddings and dynamic word embeddings of language models (English or multilingual), multi-modal language-vision models, or speech models. Biases of interest are on age, all representations of gender, body-weight, disability, immigration, intersectionality, language, nationality, race or ethnicity, political orientation, sexual orientation, and social class. I’d love to hear about any other concept or social group associations you might be interested in studying.

    Human-AI interaction: The DRG aims to advance our understanding of the processes underpinning AI’s biased information acquisition, propagation and evolution of bias and associations, and AI’s impact on individuals, society, and AI. Approaches to HAI research might involve domain-based bias analyses, human subjects, humans-in-the-loop, and the replication of real-world AI applications.

    Structure: Students will form groups to write research papers to answer scientific research questions we identify together. We will meet weekly to present and discuss research progress, new results, and research goals. Students will revise and improve their paper based on detailed reviews, then submit the paper to an appropriate conference in AI, AI ethics, fairness in machine learning, machine learning, natural language processing, or a broad audience venue. Who is this experience for? The DRG is an opportunity for students to gain research experience in AI, AI ethics, computational bias analysis, multi-modal models, and natural language processing. Levels of investment might vary, with those seeking graduate degrees in computer and information science, HCDE, or related disciplines being able to commit more time. Researchers with experience on these topics could lead projects. Students will earn 2 credits each quarter, and are expected to spend (>)8 hours a week.

    Hint: When brainstorming about research questions, go over Heilmeier’s catechism. Even though it might not be ideal or complete for ethical scientific evaluation, it is a proxy for analyzing the potential impact of a research question.

    Registration: If you are interested in participating, contact aylin@uw.edu with the subject line “DRG registration inquiry: Your name, major, and expected graduation date.” Please send your CV, cover letter, and a bulleted list of three concise research questions you are inspired to investigate based on the research directions outlined above. Why are you interested in conducting research with me? What motivates you to make original contributions to this research area? Why do you think you will be successful in answering these research questions in less than a year? In response, you will be sent a reading list and tailored evaluation tasks. There could also be a meetings to discuss which research questions excite you and what motivates you to participate in a DRG.
    • Title: The structural (and counterfactual) theory of causation
    • Instructor: Carlos Cinelli
    • TBA
    • Quarter Offered: Winter 2023
    MyPlan: STAT 591
    Description:

    This course presents a general structural (and counterfactual) theory of causation that grounds and unifies many of the recent developments in causal inference. We will study from first principles many topics of causal inference research, such as the (partial) identification of causal effects, (partial) identification of causes of effects, mediation analysis, generalization of experimental results, and causal discovery. After this course, students should have a solid base for reading cutting-edge papers, and pursuing further theoretical research in causal inference. This is not an introductory course, thus having taken STAT 566 (Causal Modeling) or equivalent is advisable (but not required).

    • Title: Topics in Gaussian and Empirical Processes
    • Instructor: Alexander Giessing
    • TBA
    • Quarter Offered: Spring 2023
    MyPlan: STAT 591

    Description: In this course we develop elements of the theory of Gaussian and empirical processes that have proved useful for statistical inference in high-dimensional models, i.e. statistical models in which the number of parameters is much larger than the sample size. The course consists of three parts, with the first two parts laying the foundation for the third one: an introduction to modern techniques in Gaussian processes (concentration, comparison, anti-concentration, and super-concentration inequalities, Talagrand’s Generic chaining bounds), a recap of classical empirical process theory (convergence of laws on separable metric spaces, Glivenko-Cantelli and Donsker theorems under metric and bracketing entropy conditions, applications to bootstrap), and lastly, a discussion of Gaussian approximation, high-dimensional CLTs, and the conditional multiplier bootstrap when function classes are not Donsker. Typed lecture notes of all three parts will be provided.

    Prerequisites: The course assumes that the students have taken PhD level classes in advanced theoretical statistics comparable to STAT 581, 582, 583 at University of Washington. Knowledge of measure theoretic probability will be helpful, but is not required.

    Textbooks for the first and second part:

    • Dudley, R. M. (2014). "Uniform Central Limit Theorems". CUP.
    • Giné, E. and Nickl, R. (2016). "Mathematical Foundations of Infinite-Dimensional Statistical Models". CUP.

    • Title: Technology for Conservation (C)
    • Instructor: Kurtis Heimerl
    • Non-major Enrollment: UW Graduate Students can self-register;Standard Petition for everyone else
    • TBD
    • Description: Increasingly, wildlife conservancies and environmental institutions are experimenting with digital technologies for research, public outreach, tracking at-risk wildlife, designing mechanisms for sustainable systems, aggregating data to support management decisions, and encouraging the public to take political and personal actions. In this class, we will broadly explore the area of Conservation Technology by reading about national- and global-scale challenges and more specific subproblems with relevant technology projects. This class will build from foundational understandings of conservation to a set of active research agendas conducted throughout the world. We will encourage a critical lens; one of our aims will be to differentiate between nice-sounding-but-ineffective tech-for-good solutions, nice-sounding-but-actually-quite-harmful tech-for-good-solutions, and answers that have a chance for real impact.

      All students will complete a project and end up with an artifact; potentially a tool (designed and/or built) for solving a real-world problem that they bring to the class.

      This is a graduate-level computer science class but particularly motivated and experienced students (including undergrads) from other disciplines can reach out if they'd like to participate.

      Some potential areas we plan to cover include:
      • History of environmental movements
      • Processes for conservation and environmental governance
      • Animal tracking
      • Behavior change
      • Computer vision / camera traps
      • Biodiversity informatics
      • Environmental communication
      • Big data applications / mechanism design"
  • Description: Despite the ubiquity of online, computational media, scholarly communication remains rooted in centuries-old models of publication. In this course, we will research and prototype novel forms of reading, writing, reviewing, reusing, and disseminating academic work. Topics include computational media, interactive articles, augmented reading and writing, collaboration, mining the literature, and applications of NLP, computer vision, and other methods for content generation and extraction. In addition to ideation and prototyping exercises, students will complete a project developing and/or assessing alternative forms of scholarly communication. The class is open to all interested students with a computer science background roughly equivalent to upper-level undergraduate courses. Depending on individual student interests, familiarity with human-computer interaction, data visualization, web programming, natural language processing, and/or computer vision may be particularly relevant.
  • Description: The last few years have seen the development of a number of privacy-preserving systems for a wide range of goals that include telemetry and analytics, digital credentials, contact tracing, ad-click measurements, private/federated machine learning, abuse reporting, etc. These systems rely on fairly advanced and elegant cryptographic tools.

    The goal of this class is to cover recent developments in this space, with the goal of developing a grounded understanding of the underlying cryptographic tools. We will cover both foundations and applications, and discuss societal implications of the resulting systems.

    The class is aimed both at cryptographers wanting to learn about a new set of primitives and non-cryptographers alike. The core of the class will mostly not cover proofs of security - only models and actual algorithms. Additional reading materials, and pointers to proofs of security, will be provided for those wanting to go deeper into the foundations of these systems.

    While some basics will be reviewed (digital signatures, zero-knowledge proofs, etc), a minimal understanding of cryptography is assumed, roughly equivalent to either our undergraduate cryptographic class (or a graduate counterpart). However, if in doubt, contact the instructor.

  • Description: TBD
    • Title: Quantum Computing (Q)
    • Instructor: J. Lee
    • Non-major Enrollment: Standard Petition;Exception for QISE students who can self-register
    • TBD
    Description: An introduction to the field of quantum computing, which explores approaches to leveraging the revolutionary potential of computers that exploit the parallelism of the quantum mechanical laws of the universe. Topics include quantum algorithms, quantum error correction, and quantum information. No prior knowledge of quantum theory is required. Prerequisites: A background in undergraduate level linear algebra and probability theory
  • Description: TBD
    • Title: Submodular Optimization
    • Instructor: Jeffrey Bilmes
    • Non-major Enrollment: Counts as a CSE++ course (formerly known as a post-quals course). Email Jeff at bilmes@uw.edu for an add code.
    • EE563 Homepage
    Description: Submodularity, a branch of discrete mathematics, is a natural model for cooperation, complexity, and attractiveness as well as for diversity, coverage, and information. Many problems in machine learning are discrete, so there is a need to study such expressive, applicable, and mathematically motivated frameworks that still offer computationally practical and scalable algorithms. There are many machine learning uses for submodular functions, such as structured convex norms, generalized independence, tractable global probabilistic models, and data management (e.g. summarization, partitioning, sketching, core sets, and clustering). Submodularity has been useful in computer vision, natural language processing, computational biology, game theory, social networks, economics, and information theory, and is growing in popularity in artificial intelligence and machine learning.

    The course will be an introduction to submodular and supermodular optimization and how it can be applied in machine learning. Topics will include the following: (1) Introduction to submodular and supermodular functions, including definitions, examples, properties, closure operations, variants and special cases, computational properties, relationships to graph theory, and generalizations; (2) A background on the theory of matroids and lattices. Generalizations of submodular functions to integer lattices and continuous spaces (e.g., non-convex DR functions); (3) Polyhedral properties, including matroid and polymatroidal polyhedra, and the semidifferential structure of submodular and supermodular functions; (4) The Lovasz extension, the Choquet integral, and other convex and concave extensions; (5) Optimization, including submodular maximization algorithms under constraints, cover problems, greedy algorithms, and other centralized and parallel/distributed algorithms, submodular minimization algorithms, including both numerical and combinatorial algorithms, the constrained case, computational properties, and recent results and open problems; (6) Applications including those mentioned above as well as traditional applications in combinatorics, human learning, convex cores and the Shapley value, graphical models, and learning submodularity.

    The course will have regular homework assignments (due approximately every two weeks), and a final project. All lectures will be recorded and sent to youtube via private links. We will use canvas and discord for class announcements and discussions.
    • Title: Experimental Methods
    • Instructor: René Just
    • Non-major Enrollment: All UW Students can register in Period II
    • TBD
    Description: Valid experimental designs, sound data analyses, and reproducibility of empirical results are core tenets of the scientific method -- crucial not only for specific domains in computer science but rather any field that seeks empirical evidence.

    This course covers qualitative and quantitative research methods and focuses on properly designing experiments and observational studies, choosing appropriate statistical methods and models, and reasoning about the validity of experimental designs (in terms of internal, external, and construct validity). This course involves lectures, paper discussions, as well as a hands-on experience for data analysis and visualization with R.
  • Description: A lattice is discrete subgroup of R^n. Lattices are fundamental objects in discrete math with a rich set of applications to theoretical computer science, optimization and cryptography. We will see the following in this course:
    • Introduction to lattices
    • Algorithms for the Closest Vector problem
    • The Transference Theorems of Banaszczyk
    • Khintchine's Flatness Theorem
    • Lenstra's algorithm for Integer programming in fixed dimension
    • Lattice problems in NP intersected coNP
    • Lattice-based cryptography and Learning with Errors
    Prerequisites: A good understanding of convex geometry, probability and algorithms will be very helpful.
  • Description: This is an advanced graduate-level theory course about tools from analysis and geometry that have interesting applications in algorithms, complexity theory, and theoretical ML. Evaluation will be based on a small number of homeworks and student presentations. One topic per week, choice of topics will be guide by class interest.
  • Description: TBA
  • Description: TBA
  • Description: Brains are remarkably complex, massive networks of interconnected neurons that underly 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 deep 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 focus around variational autoencoders and recurrent neural networks, along with their probabilistic foundations from classical machine learning. Coursework will include readings from the deep learning and computational neuroscience literature, programming assignments, and a final modeling project applied to neural population data.

    Prerequisites: vector calculus, probability & statistics, linear algebra, and some exposure to machine learning. Programming assignments will be completed in Python. No prior knowledge of neuroscience is needed.
  • Description: This is a seminar class to explore the frontier research questions centered around language, knowledge, and reasoning. The class requires familiarity with basic NLP and deep learning literature.
  • Description: This course is open to Allen School undergraduates, combined BS/MS students, Professional Masters Program students, and Ph.D. students, as well as to Foster School MBA and EMBA students, students in Interaction Design, graduate students in Human Centered Design & Engineering, and students in the Master of Human-Computer Interaction and Design program - all by permission of the instructors in order to ensure balance among the participants. There will be no auditing - everyone needs to be all-in. And project teams will form early - if you hang on for a week or two and then bail, you'll be letting others down, so please don't do this.
    • Title: Computational Genomics (C)
    • Instructor: Sara Mostafavi
    • Non-major Enrollment: Open to all UW Students to self-register in Period II
    • TBD
    Description: Computational and statistical approaches and practices for deriving robust and rigorous insights from modern genomics datasets. Lectures alternate between genomics-inspired problem formulation and foundational statistical and computational approaches for addressing them. In foundational lectures, we will cover basics of statistical inference, hidden confounding factors, causality and causal inference, deep neural networks and interpretation approaches to deep learning models.
  • Description: This is what the students will read when trying to access whether the course is appropriate for them or not. Please include a suitable description. There is an example at the top of this form. 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 makes predictions. We'll cover many active research areas in the field, including feature attribution, counterfactual explanations, instance explanations and human-AI collaboration.
    • Title: ML for Systems (M)
    • Instructor: Luis Ceze
    • Non-major Enrollment: Open to all UW Students to self-register in Period II
    • CSE599m Webpage
    Description: ML models are quickly become an integral component of how applications are built. Yet they are a different thing than most software — performance hungry, bandwidth hungry, and very fast-evolving. This lead to the need to build systems to support them — abstractions and frameworks to tame complexity and quickly adapt, compilers, programming languages and runtime systems to make efficient use of hardware resources, better communication approaches for distributed systems, etc. One important twist to this fast systems development is that optimization spaces for ML systems themselves (codegen fo ML models, systems parameter tuning, resource allocation, etc) are very large, so these systems use machine learning itself to provide effective solutions — so you read the name of the class right, it is “ML for ML systems” ;).

    In this special topics class we will explore the state-of-the-art and research on ML systems, including: ML model compilers, ML training systems, ML serving systems, support for large language models serving, ML systems that span cloud and edge, resource management for ML, among others. The format is a participatory focused on paper reading, presenting and discussion, and a class project scoped and chosen by the participants.
  • Description: The goal of this class is to lay the foundations for graduate research in machine learning, both on the theoretical and empirical side. We will cover fundamentals of generalization theory such as Rademacher complexity and core results in optimization with a focus on first-order methods, building up to widely used variants of stochastic gradient descent. On the empirical side, we will focus on recent advances leveraging large models and datasets, covering both multimodal models (CLIP) and language models (GPT). We will pay particular attention to the techniques behind models such as ChatGPT and the experimental methodology behind their development (datasets, benchmarks, and scaling trends).
  • Description:This course provides an introduction to the concept of accessibility including its importance in a variety of computational systems. We ask students to explore how computing can enable new solutions to accessibility, including both access to the world and access to computers. Similarly, students explore how a disability studies perspective can guide us in developing empowering and relevant solutions to accessibility problems.

    This course explores both of those questions through a combination of discussions, reading, and building. The class will focus on a combination of practical skills such as how to assess accessibility of documents, websites and apps and how to do disability based UX; advanced skills such as how to address accessibility in visualization, AR/VR and AI/ML; and forward looking topics such as intersectional concerns, accessible healthcare, and accessibility in disaster response. The largest project in the class will be an open ended opportunity to explore access technology in more depth.
MyPlan

    • 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: Interdisciplinary-focused, email grad-advising@cs.washington.edu for add code
    • TBD
    • 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: Interdisciplinary-focused, email grad-advising@cs.washington.edu for add code
    • http://changemm.cs.washington.edu/mailman/listinfo/change
    • Description: TBA
    • Non-major Enrollment: N/A
    • TBD
    • 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: Grad students and Postdocs welcome, email grad-advising@cs.washington.edu for add code. Undergraduate students are also very welcome, but will need to fill out this form to receive an add code: https://tinyurl.com/590e-interest.
    • https://mailman.cs.washington.edu/mailman/listinfo/cs-ed
    • Title: Computing and the Developing World (F)
    • Instructor: Richard Anderson + ICTD Lab Students
    • TBD
    • 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: Ph.D. students taking the course from appropriate departments only, email grad-advising@cs.washington.edu for add code.
    • N/A
    • 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: Research-active with participating CSE faculty (i.e., with CSE HCI faculty), email grad-advising@cs.washington.edu for add code.
    • N/A
    • 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: Research-active with participating CSE faculty (i.e., with CSE HCI faculty), email grad-advising@cs.washington.edu for add code.
    • N/A
    • Title: Networks (L)
    • Instructor: Tom Anderson
    • Non-major Enrollment: N/A
    • TBD
    • Description: TBA
    • Non-major Enrollment: Ph.D. students taking the course from appropriate departments only, email grad-advising@cs.washington.edu for add code.
    • N/A
    • Title: Software Engineering (N)
    • Instructor: Michael Ernst
    • Non-major Enrollment: N/A
    • TBD
    • Description: TBA
    • Non-major Enrollment: N/A
    • TBD
    • Description: [Cancelled for Autumn '21] We choose a different topic each quarter. Recent examples of topics: “Responsible Data Management”, “Learned Database Systems”, “Deep Learning Meets DB”, “Transactions!”. We present 1-2 papers per seminar. Presentations are either by one or two students. If you sign up for more than 1 credit then you must present a paper.
    • Non-major Enrollment: Not Accepted
    • 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 Selest Nashef for add codes.
    • https://mailman.cs.washington.edu/mailman/listinfo/robotics
    • Description: Discussion of recent research in operating systems, distributed systems, and networks.
    • Non-major Enrollment: N/A
    • TBD
    • Title: Computing Cultural Heritage (T)
    • Instructor: Ben Lee (bcgl@cs.washington.edu)
    • Non-major Enrollment: Instructor Form;This seminar is accessible to those without any formal training in computer science. Ben welcomes students across a range of disciplines, including library and information science, the humanities, and computer science & engineering
    • GDoc Syllabus
    • Description: From large-scale systems for searching the web to the datasets that machine learning practitioners utilize to train their models, our collective cultural heritage is in many ways the substrate of computer science. Indeed, cultural heritage practitioners including humanists, librarians, and archivists have been influential in shaping the discourse surrounding the sociotechnical implications of computing. This course explores various topics within computer science through the lens of cultural heritage: data visualization, human-AI interaction, search & discovery, crowdsourcing, web archiving, design & UX, and classification. The goals of this course are two-fold: first, to survey these topics in computer science, and second, to explore how they manifest within the context of cultural heritage. We will cover one topic every week, with the first meeting devoted to the CS-oriented literature for the topic, and the second meeting devoted to the sociotechnical implications of the topic in practice. During these second meetings, we will speak with cultural heritage practitioners at institutions across the country to learn about the roles of computing in their work, research, and stewardship.
    • Non-major Enrollment: N/A
    • TBD
    • Title: Ubicomp (U)
    • Instructor: Taught by Allen School Students - Autumn '21: Richard Li (Ph.D.) and Jerry Cao (Ugrad)
    • TBD
    Description: Discussion of recent research in operating systems, distributed systems, and networks.
    • Description: Topics include recent advances in computer graphics and vision. One or two speakers will present each time, talking about their researches or recent papers. There will also be several guest lectures introducing popular topics in this area.
    • Non-major Enrollment: N/A
    • TBD
    • 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 Franziska Roesner , Tadayoshi Kohno , David Kohlbrenner , and Elle Brown for access.
    • https://mailman.cs.washington.edu/mailman/listinfo/uw-security-research
    • Description: Talks from a mix of local and external speakers on the latest and greatest in CS theory; from algorithms to complexity theory, from optimization to cryptography, from information theory to game theory. All your theory needs under one roof.
    • Non-major Enrollment: N/A
    • TBD
    • MyPlan

  • This is the regular weekly meeting of the database research group. Individual seminars range from invited speakers, to internal talks, to group discussions about specific issues faced by the group.
    • Title: MOLECULAR INFORMATION SYSTEMS SEMINAR (I)
    • Instructor: Luis Ceze
    • Non-major Enrollment:
    • TBD
    • Title: SYSTEMS AND ARCHITECTURE FOR MACHINE LEARNING SEMINAR (J)
    • Instructor: Luis Ceze
    • Non-major Enrollment:
    • Title: XLAB SEMINAR (K)
    • Instructor: Yejin Choi
    • Non-major Enrollment:
    • TBD
    • Title: NLP SEMINAR (N)
    • Instructor: Yejin Choi
    • Non-major Enrollment:
    • TBD
    • Title: SAMPA SEMINAR (O)
    • Instructor: Luis Ceze
    • Non-major Enrollment:
    • TBD
    • Title: MULTIMEDIA SEMINAR (P)
    • Instructor: Linda Shapiro
    • Non-major Enrollment:
    • TBD
    Description: CSE, ECE, and BIME do not require add codes.
    • Title: LANGUAGE, INTERACTION, AND LEARNING SEMINAR (T)
    • Instructor: Luke Zettlemoyer
    • Non-major Enrollment:
    • TBD
    • Title: CLAW BOOTSTRAP SEMINAR (U)
    • Instructor: Michael Taylor
    • Non-major Enrollment:
    • TBD
    • MyPlan