printer friendly pageCS 278: Social Computing (SOC 174, SOC 274)
Today we interact with our friends and enemies, our team partners and romantic partners, and our organizations and societies, all through computational systems. How do we design these social computing systems - platforms for social media, online communities, and collaboration - to be effective and responsible? This course covers design patterns for social computing systems and the foundational ideas that underpin them.
Terms: Spr
| Units: 3-4
Instructors:
Bernstein, M. (PI)
;
Doby, S. (TA)
;
Miller, G. (TA)
;
Phatak, U. (TA)
;
Rodriguez, S. (TA)
;
Verma, S. (TA)
;
Zhou, G. (TA)
;
Zuna Largo, W. (TA)
CS 281: Ethics of Artificial Intelligence
Machine learning has become an indispensable tool for creating intelligent applications, accelerating scientific discoveries, and making better data-driven decisions. Yet, the automation and scaling of such tasks can have troubling negative societal impacts. Through practical case studies, you will identify issues of fairness, justice and truth in AI applications. You will then apply recent techniques to detect and mitigate such algorithmic biases, along with methods to provide more transparency and explainability to state-of-the-art ML models. Finally, you will derive fundamental formal results on the limits of such techniques, along with tradeoffs that must be made for their practical application. CS229 or equivalent classes or experience.
Terms: Spr
| Units: 3-4
Instructors:
Guestrin, C. (PI)
CS 323: The AI Awakening: Implications for the Economy and Society
Intelligent computer agents must reason about complex, uncertain, and dynamic environments. This course is a graduate level introduction to automated reasoning techniques and their applications, covering logical and probabilistic approaches. Topics include: logical and probabilistic foundations, backtracking strategies and algorithms behind modern SAT solvers, stochastic local search and Markov Chain Monte Carlo algorithms, variational techniques, classes of reasoning tasks and reductions, and applications. Enrollment by application:
https://digitaleconomy.stanford.edu/about/the-ai-awakening-implications-for-the-economy-and-society/
Terms: Spr
| Units: 3-4
Instructors:
Brynjolfsson, E. (PI)
CS 336: Language Modeling from Scratch
Language models serve as the cornerstone of modern natural language processing (NLP) applications and open up a new paradigm of having a single general purpose system address a range of downstream tasks. As the field of artificial intelligence (AI), machine learning (ML), and NLP continues to grow, possessing a deep understanding of language models becomes essential for scientists and engineers alike. This course is designed to provide students with a comprehensive understanding of language models by walking them through the entire process of developing their own. Drawing inspiration from operating systems courses that create an entire operating system from scratch, we will lead students through every aspect of language model creation, including data collection and cleansing for pre-training, transformer model construction, model training, and evaluation before deployment. Application required, apply at
https://docs.google.com/forms/d/e/1FAIpQLSdW0HgT8MHzdM8cgapLWqX2ZPP1yHSX52R_r5JzF52poqXsHg/viewform
Terms: Spr
| Units: 3-5
CS 340R: Rusty Systems
Language shapes thought; for 40 years, software systems and some of their research challenges have been defined by the C language. In the past 5 years, this has begun to change, with new languages (Rust, Go, coq) becoming competitors to C in large classes of systems. CS340R is a project-centric course that examines how the Rust programming language changes software systems, solving some problems while presenting new ones. This course seeks to ask and start to answer a simple question: "What are the most important open research challenges for software systems written in Rust?"
Terms: Spr
| Units: 3
Instructors:
Levis, P. (PI)
CS 343S: Domain-Specific Language Design Studio
This is a design-studio course for the creation of domain-specific languages (DSLs). We will start with lectures teaching fundamental skills for designing and implementing DSLs, followed by a long term project designing and implementing a DSL of the student's choice. The course will particularly emphasize the role that languages can play in tasks that we do not usually think of as programming, such as DSLs for knitting patterns or geometric constructions.
Terms: Spr
| Units: 3
CS 347: Human-Computer Interaction: Foundations and Frontiers
(Previously numbered
CS376.) How will the future of human-computer interaction evolve? This course equips students with the major animating theories of human-computer interaction, and connects those theories to modern innovations in research. Major theories are drawn from interaction (e.g., tangible and ubiquitous computing), social computing (e.g., Johansen matrix), and design (e.g., reflective practitioner, wicked problems), and span domains such as AI+HCI (e.g., mixed initiative interaction), accessibility (e.g., ability based design), and interface software tools (e.g., threshold/ceiling diagrams). Students read and comment on multiple research papers per week, and perform a quarter-long research project. Prerequisites: For CS and Symbolic Systems undergraduates/masters students, CS147 or
CS247. No prerequisite for PhD students or students outside of CS and Symbolic Systems.
Terms: Spr
| Units: 3-4
| Repeatable
for credit
CS 348K: Visual Computing Systems
Visual computing tasks such as computational photography, image/video understanding, and real-time 3D graphics are key responsibilities of modern computer systems ranging from sensor-rich smart phones, autonomous robots, and large data centers. These workloads demand exceptional system efficiency and this course examines the key ideas, techniques, and challenges associated with the design of parallel, heterogeneous systems that execute and accelerate visual computing applications. This course is intended for graduate and advanced undergraduate-level students interested in architecting efficient graphics, image processing, and computer vision systems (both new hardware architectures and domain-optimized programming frameworks) and for students in graphics, vision, and ML that seek to understand throughput computing concepts so they can develop scalable algorithms for these platforms. Students will perform daily research paper readings, complete simple programming assignments, and compet
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Visual computing tasks such as computational photography, image/video understanding, and real-time 3D graphics are key responsibilities of modern computer systems ranging from sensor-rich smart phones, autonomous robots, and large data centers. These workloads demand exceptional system efficiency and this course examines the key ideas, techniques, and challenges associated with the design of parallel, heterogeneous systems that execute and accelerate visual computing applications. This course is intended for graduate and advanced undergraduate-level students interested in architecting efficient graphics, image processing, and computer vision systems (both new hardware architectures and domain-optimized programming frameworks) and for students in graphics, vision, and ML that seek to understand throughput computing concepts so they can develop scalable algorithms for these platforms. Students will perform daily research paper readings, complete simple programming assignments, and compete a self-selected term project. Prerequisites:
CS 107 or equivalent. Highly recommended: Parallel Computing (
CS149) or Computer Architecture (
EE 282). Students will benefit from some background in deep learning (
CS 230,
CS 231N), computer vision (
CS 231A), digital image processing (
CS 232) or computer graphics (
CS248).
Terms: Spr
| Units: 3-4
Instructors:
Fatahalian, K. (PI)
CS 349D: Cloud Computing Technology
The largest change in the computer industry over the past twenty years has arguably been the emergence of cloud computing: organizations are increasingly developing their workloads to be cloud native, using global-scale compute, storage, and communication services that were simply not possible with private infrastructure. This research seminar covers the latest technical advances and open issues in cloud computing, including cloud infrastructure for AI inference and training, cloud databases and data lakes, resource management, serverless computing, confidential computing, multi-cloud computing, and AI for cloud management. Students will propose and develop an original research project in cloud computing. Prerequisites: Background in computer systems recommended but not required (
CS 111/240, 144/244, 244B
or 245).
Terms: Spr
| Units: 3
Instructors:
Kozyrakis, C. (PI)
CS 352B: Blockchain Governance
This course offers an overview of blockchain governance and Decentralized Autonomous Organizations (DAOs), with topics including DAO tooling, on-chain and off-chain voting, delegation, constitutional design, alternative governance mechanisms, identity, and privacy. We will cover these topics and others from technical, social science, and legal perspectives, and we will include a range of guests from the web3 space as well as several speakers who are on the frontiers of DAO research. The course presumes some basic familiarity with blockchain and cryptocurrencies, but deep technical facility is not required, i.e., successful completion of
CS 251 or
LAW 1043 is more than enough. Elements used in grading: Homework and papers. There are no examinations. Grading elements and the course itself are designed so that students with diverse expertise and backgrounds (law, technical, business, etc.) have an equal opportunity to do well and have a powerful learning experience. Cross-listed with
L
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This course offers an overview of blockchain governance and Decentralized Autonomous Organizations (DAOs), with topics including DAO tooling, on-chain and off-chain voting, delegation, constitutional design, alternative governance mechanisms, identity, and privacy. We will cover these topics and others from technical, social science, and legal perspectives, and we will include a range of guests from the web3 space as well as several speakers who are on the frontiers of DAO research. The course presumes some basic familiarity with blockchain and cryptocurrencies, but deep technical facility is not required, i.e., successful completion of
CS 251 or
LAW 1043 is more than enough. Elements used in grading: Homework and papers. There are no examinations. Grading elements and the course itself are designed so that students with diverse expertise and backgrounds (law, technical, business, etc.) have an equal opportunity to do well and have a powerful learning experience. Cross-listed with
LAW 1078. The course will be taught in law school classrooms. In addition to the listed Stanford faculty instructors and the various guest speakers, Silke Noa Elrifai, a crypto lawyer and mathematician with a deep background in actual DAO projects and currently a Visiting Scholar at Stanford, will be the primary instructor for several classes and will play an integral role in the course.
Terms: Spr
| Units: 3
Instructors:
Mazieres, D. (PI)
;
Strnad, J. (PI)
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