William Brown

w.brown@columbia.edu

About

I'm a fifth-year Computer Science PhD candidate at Columbia, where I'm incredibly fortunate to be advised by Christos Papadimitriou and Tim Roughgarden in the Theory group. My research interests lie at the intersection of machine learning and game theory, with a focus on learning in the presence of strategic, adversarial, or otherwise dynamic behavior. Previously, I graduated from Penn in 2019 with an MS in Data Science and a BS in Computer Science and Philosophy.

Publications

  • Online Recommendations for Agents with Discounted Adaptive Preferences
    Arpit Agarwal, William Brown
    ALT 2024. [arXiv]
  • Is Learning in Games Good for the Learners?
    William Brown, Jon Schneider, Kiran Vodrahalli
    NeurIPS 2023 (Spotlight). [arXiv] [talk]
  • Diversified Recommendations for Agents with Adaptive Preferences
    Arpit Agarwal, William Brown
    NeurIPS 2022. [arXiv] [talk]
  • Private Synthetic Data for Multitask Learning and Marginal Queries
    Giuseppe Vietri, Cedric Archambeau, Sergul Aydore, William Brown, Michael Kearns, Aaron Roth, Ankit Siva, Shuai Tang, Steven Wu
    NeurIPS 2022. [arXiv]
  • Learning in Multi-Player Stochastic Games
    William Brown
    UAI 2021. [arXiv]
  • Differentially Private Query Release Through Adaptive Projection
    Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Ankit Siva
    ICML 2021 (Long presentation). [arXiv] [talk]
  • Targeted Intervention in Random Graphs
    William Brown, Utkarsh Patange
    SAGT 2020. [arXiv]
  • Change Point Detection in Software Performance Testing
    David Daly, William Brown, Henrik Ingo, Jim O'Leary, David Bradford
    ICPE 2020. [arXiv] [blog]

Preprints

  • Online Stackelberg Optimization via Nonlinear Control
    William Brown, Christos Papadimitriou, Tim Roughgarden
    In submission.
  • Online Portfolio Selection with Adversarial Transaction Costs
    William Brown, Yikai Zhang
    In submission.

Experience

  • Machine Learning Research - Morgan Stanley (2023 - )
  • Applied Science - Amazon Web Services (2020 - 2022)
  • Applied Research - Two Sigma Labs (Summer 2019)
  • Data Engineering - Two Sigma (Summer 2018)
  • Software Engineering - MongoDB (Summer 2017)
  • Data Science - American Family Insurance (Summer 2016)

Teaching

Columbia:
  • Teaching Assistant - COMS 6998-001: Foundations of Blockchains (Fall 2020)
Penn: