Ashish Goel

About

Ashish Goel is a Professor of Management Science and Engineering at Stanford University, holding the Fortinet Founders Chair of Management Science and Engineering. He is also a Professor, by courtesy, of Computer Science. He received his PhD in Computer Science from Stanford University in 1999. His research interests lie in the design, analysis, and applications of algorithms. Prior to his current position, he was an Assistant Professor of Computer Science at the University of Southern California from 1999 to 2002. His work has garnered recognition, including being named an ACM Fellow for contributions to computing that are transforming science and society. He has been involved in various research projects and initiatives, including efforts related to AI, climate resilience, and democratic deliberation, and has contributed to the academic and broader community through his leadership and research activities.

Research topics

• Computer Science
• Sociology
• Artificial Intelligence
• Business
• Economics
• Engineering
• Physics
• Geology
• Psychology
• Remote sensing

Selected publications

• The impossibility of low-rank representations for triangle-rich complex networks

  Proceedings of the National Academy of Sciences · 2020 · 49 citations

  Senior authorCorresponding

  The study of complex networks is a significant development in modern science, and has enriched the social sciences, biology, physics, and computer science. Models and algorithms for such networks are pervasive in our society, and impact human behavior via social networks, search engines, and recommender systems, to name a few. A widely used algorithmic technique for modeling such complex networks is to construct a low-dimensional Euclidean embedding of the vertices of the network, where proximit…

  DOI

• Finding the Right Curve: Optimal Design of Constant Function Market Makers

  2023-07-07 · 21 citations

  articleOpen access

  Constant Function Market Makers (CFMMs) are a tool for creating exchange markets, have been deployed effectively in prediction markets, and are now especially prominent in the Decentralized Finance ecosystem. We show that for any set of beliefs about future asset prices, an optimal CFMM trading function exists that maximizes the fraction of trades that a CFMM can settle. We formulate a convex program to compute this optimal trading function. This program, therefore, gives a tractable framework f…

  PublisherOA PDFDOI

• JUE insights: Does mobility explain why slums were hit harder by COVID-19 in Mumbai, India?

  Journal of Urban Economics · 2021 · 15 citations

  DOI

• Advertising for Demographically Fair Outcomes

  arXiv (Cornell University) · 2020 · 13 citations

  Online advertising on platforms such as Google or Facebook has become an indispensable outreach tool, including for applications where it is desirable to engage different demographics in an equitable fashion, such as hiring, housing, civic processes, and public health outreach efforts. Somewhat surprisingly, the existing online advertising ecosystem provides very little support for advertising to (and recruiting) a demographically representative cohort. We study the problem of advertising for de…

  DOI

• Achieving parity with human moderators

  2023-06-08 · 4 citations

  book-chapterOpen access

  We describe the design of a video-conferencing platform for online deliberation that is self-moderating in the sense that it works without a human moderator. The platform includes an audio and video conferencing system and incorporates automated and user-assisted moderation, queues, nudges, speaker management, and agenda management. It can be configured to mirror the moderation practices of the Deliberative Polling framework (Fishkin, Luskin, and Jowell 2000), and has also been used in other del…

  PublisherOA PDFDOI

Recent grants

• DC: Small: The Use of Ternary Associative Memories in Data Intensive Computing

  NSF · $430k · 2009–2013

• CAREER: Algorithms for Services - Oriented Communications Networks

  NSF · $280k · 2003–2008

• BIGDATA: F: DKA: Collaborative Research: Dealing Efficiently with Big Social Network Data

  NSF · $300k · 2014–2018

Frequent coauthors

• Sudipto Guha

  University of Pennsylvania

  33 shared

• Moses Charikar

  Stanford University

  32 shared

• Sanjeev Khanna

  30 shared

• Amit Chakrabarti

  Centre for Mental Health

  27 shared

• Pascal Koiran

  25 shared

• Gruia Călinescu

  25 shared

• Howard Karloff

  New York Proton Center

  25 shared

• Nicolas Schabanel

  École Normale Supérieure de Lyon

  25 shared

Education

• Ph.D., Computer Science

  Stanford University

  1990

• M.S., Computer Science

  Stanford University

  1987

• B.S., Electrical Engineering and Computer Science

  Massachusetts Institute of Technology (MIT)

  1983

Awards & honors

• ACM Fellow (2025)

Similar researchers at Stanford University

• Amin Saberih-53
• Benjamin Van Royh-51
• Renyuan Xuh-51