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, most recently 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 9/10/21)

Papers (authors alphabetical unless not):

  1. Quantum Advantage in Learning From Experiments [pdf]
    Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, Jarrod R. McClean
    In submission
  2. Kalman Filtering with Adversarial Corruptions [pdf]
    Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
    In submission
  3. Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs [pdf]
    Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka
    ICLR 2022
  4. A Hierarchy for Replica Quantum Advantage [pdf]
    Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
    QIP 2022, merged with [CCHL21]
  5. Towards Instance-Optimal Quantum State Certification With Independent Measurements [pdf]
    Sitan Chen, Jerry Li, Ryan O'Donnell
    QIP 2022
    Blurb on Property Testing Review
  6. Symmetric Sparse Boolean Matrix Factorization and Applications [pdf]
    Sitan Chen, Zhao Song, Runzhou Tao, Ruizhe Zhang
    ITCS 2022
  7. Efficiently Learning One Hidden Layer ReLU Networks From Queries [pdf]
    Sitan Chen, Adam Klivans, Raghu Meka
    NeurIPS 2021
  8. Exponential Separations Between Learning With and Without Quantum Memory [pdf]
    Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
    FOCS 2021, QIP 2022
    Invited to SIAM Journal of Computing Special Issue
  9. Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination [pdf]
    Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
    FOCS 2021
  10. Learning Deep ReLU Networks Is Fixed-Parameter Tractable [pdf] [video]
    Sitan Chen, Adam R. Klivans, Raghu Meka
    FOCS 2021
  11. Algorithmic Foundations for the Diffraction Limit [pdf] [slides] [code] [video] [Ankur's Simons tutorial]
    Sitan Chen, Ankur Moitra
    STOC 2021
  12. On InstaHide, Phase Retrieval, and Sparse Matrix Factorization [pdf]
    Sitan Chen, Xiaoxiao Li, Zhao Song, Danyang Zhuo
    ICLR 2021
  13. 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
  14. Learning Structured Distributions from Untrusted Batches: Faster and Simpler [pdf] [code]
    Sitan Chen, Jerry Li, Ankur Moitra
    NeurIPS 2020
  15. Entanglement is Necessary for Optimal Quantum Property Testing [pdf] [slides] [video]
    Sebastien Bubeck, Sitan Chen, Jerry Li
    FOCS 2020
    Blurb on Property Testing Review
  16. Learning Polynomials of Few Relevant Dimensions [pdf] [slides] [video]
    Sitan Chen, Raghu Meka
    COLT 2020
  17. Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Moments [pdf] [slides] [video]
    Sitan Chen, Jerry Li, Zhao Song
    STOC 2020
  18. Efficiently Learning Structured Distributions from Untrusted Batches [pdf] [slides] [video]
    Sitan Chen, Jerry Li, Ankur Moitra
    STOC 2020
  19. 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
  20. Beyond the Low-Degree Algorithm: Mixtures of Subcubes and Their Applications [pdf] [slides]
    Sitan Chen, Ankur Moitra
    STOC 2019
  21. Basis Collapse For Holographic Algorithms over All Domain Sizes [pdf] [slides] [video]
    Sitan Chen
    STOC 2016.
  22. Pseudorandomness for Read-Once, Constant-Depth Circuits [pdf]
    Sitan Chen, Thomas Steinke, Salil Vadhan


  • 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


  • PC Member: ICALP 2022


picture of me