Sitan Chen

I am an NSF postdoc at UC Berkeley hosted by Prasad Raghavendra, and a Research Fellow at the Simons Institute. I work on designing algorithms with provable guarantees for fundamental problems in data science, especially in settings where data might be heterogeneous or contaminated. I also enjoy exploring what techniques for such problems can tell us about inverse problems in the sciences, e.g. in the context of quantum information.

Recently, I received my PhD in EECS from MIT as a member of CSAIL and the Theory of Computation group. I was very fortunate to be advised by Ankur Moitra and supported by an MIT Presidential Fellowship and a PD Soros Fellowship.

Prior to MIT, I studied mathematics and computer science as an undergraduate at Harvard, where I had the pleasure and honor of working with Salil Vadhan and Leslie Valiant.

Announcement: In Fall of 2023, I will join the Theory of Computation group at Harvard's John A. Paulson School of Engineering and Applied Sciences as an Assistant Professor of Computer Science.

Email: sitan (at) seas (dot) harvard (dot) edu
CV (last updated 1/26/21)

Papers (authors alphabetical):

  1. Efficiently Learning One Hidden Layer ReLU Networks From Queries
    Sitan Chen, Adam Klivans, Raghu Meka
    NeurIPS 2021
  2. Exponential Separations Between Learning With and Without Quantum Memory
    Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
    FOCS 2021
    Invited to SIAM Journal of Computing Special Issue
  3. Towards Instance-Optimal Quantum State Certification With Independent Measurements [pdf]
    Sitan Chen, Jerry Li, Ryan O'Donnell
    Manuscript
    Blurb on Property Testing Review
  4. Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination [pdf]
    Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
    FOCS 2021
  5. Learning Deep ReLU Networks Is Fixed-Parameter Tractable [pdf] [video]
    Sitan Chen, Adam R. Klivans, Raghu Meka
    FOCS 2021
  6. Symmetric Boolean Factor Analysis with Applications to InstaHide [pdf]
    Sitan Chen, Zhao Song, Runzhou Tao, Ruizhe Zhang
    Manuscript
  7. Algorithmic Foundations for the Diffraction Limit [pdf] [slides] [code] [video] [Ankur's Simons tutorial]
    Sitan Chen, Ankur Moitra
    STOC 2021
  8. On InstaHide, Phase Retrieval, and Sparse Matrix Factorization [pdf]
    Sitan Chen, Xiaoxiao Li, Zhao Song, Danyang Zhuo
    ICLR 2021
  9. Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability [pdf] [code] [Ankur's Simons tutorial]
    Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
    NeurIPS 2020
    Spotlight presentation
  10. Learning Structured Distributions from Untrusted Batches: Faster and Simpler [pdf] [code]
    Sitan Chen, Jerry Li, Ankur Moitra
    NeurIPS 2020
  11. Entanglement is Necessary for Optimal Quantum Property Testing [pdf] [slides] [video]
    Sebastien Bubeck, Sitan Chen, Jerry Li
    FOCS 2020
    Blurb on Property Testing Review
  12. Learning Polynomials of Few Relevant Dimensions [pdf] [slides] [video]
    Sitan Chen, Raghu Meka
    COLT 2020
  13. Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Moments [pdf] [slides] [video]
    Sitan Chen, Jerry Li, Zhao Song
    STOC 2020
  14. Efficiently Learning Structured Distributions from Untrusted Batches [pdf] [slides] [video]
    Sitan Chen, Jerry Li, Ankur Moitra
    STOC 2020
  15. Improved Bounds for Sampling Colorings via Linear Programming [pdf] [slides]
    Sitan Chen, Michelle Delcourt, Ankur Moitra, Guillem Perarnau, Luke Postle
    (merger of [CM18] and [DPP18])
    SODA 2019
  16. Beyond the Low-Degree Algorithm: Mixtures of Subcubes and Their Applications [pdf] [slides]
    Sitan Chen, Ankur Moitra
    STOC 2019
  17. Basis Collapse For Holographic Algorithms over All Domain Sizes [pdf] [slides] [video]
    Sitan Chen
    STOC 2016.
  18. Pseudorandomness for Read-Once, Constant-Depth Circuits [pdf]
    Sitan Chen, Thomas Steinke, Salil Vadhan
    Manuscript

Theses:

  • Rethinking Algorithm Design for Modern Challenges in Data Science [pdf]
    PhD Thesis, 2021
  • Geometry in Algorithms and Complexity: Holographic Algorithms and Valiant's Conjecture [pdf]
    Undergraduate Thesis, 2016
    Thomas Hoopes Prize, Captain Jonathan Fay Prize (for best Harvard undergraduate theses), New World Mathematics Award

Service:

  • PC Member: ICALP 2022

Other:

 
picture of me