I am an Assistant Professor of Computer Science at Harvard's John A. Paulson School of Engineering and Applied Sciences, where I am an Associate Faculty at the Kempner Institute and a member of the Theory of Computation group, the ML Foundations group, and the Harvard Quantum Initiative.

I am broadly interested in algorithmic questions about learning from data. In recent years my work has revolved around two threads: developing the mathematical and scientific foundations of diffusion-based generative modeling, and building a theory of learning in quantum systems.

My work has been generously supported by an NSF CAREER award CCF-2441635, an NSF Small (joint with Anurag Anshu) CCF-2430375, an NSF SLES (joint with Boaz Barak and Sham Kakade) IIS-2331831, and the Harvard Dean's Competitive Fund for Promising Scholarship.

Previously I was an NSF postdoc at UC Berkeley under the wise guidance of Prasad Raghavendra. 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.

Email: sitan (at) seas (dot) harvard (dot) edu
Sitan Chen

Current Group

Former Group Members

Teaching

Recent Papers

Selected Papers

Show All Papers
  1. Sublinear Iterations Can Suffice Even for DDPMs [pdf]
    Matthew S. Zhang, Stephen Huan, Jerry Huang, Nicholas M. Boffi, Sitan Chen, Sinho Chewi
    Manuscript
  2. Quantum Probe Tomography [pdf]
    Sitan Chen, Jordan Cotler, Hsin-Yuan Huang
    Manuscript
  3. Selective Underfitting in Diffusion Models [pdf] [website]
    Kiwhan Song, Jaeyeon Kim, Sitan Chen, Yilun Du, Sham Kakade, Vincent Sitzmann
    Manuscript
  4. ReGuidance: A Simple Diffusion Wrapper for Boosting Sample Quality on Hard Inverse Problems [pdf]
    Aayush Karan, Kulin Shah, Sitan Chen
    Manuscript
  5. A Provably Efficient Method for Tensor Ring Decomposition and Its Applications [pdf]
    Han Chen, Sitan Chen, Anru R. Zhang
    SIAM Journal on Mathematics of Data Science
  6. Information-Computation Gaps in Quantum Learning via Low-Degree Likelihood [pdf]
    Sitan Chen, Weiyuan Gong, Jonas Haferkamp, Yihui Quek
    COLT 2026, QIP 2026
  7. Optimal Inference Schedules for Masked Diffusion Models [pdf]
    Sitan Chen, Kevin Cong, Jerry Li
    COLT 2026
  8. Stop Training for the Worst: Progressive Unmasking Accelerates Masked Diffusion Training [pdf]
    Jaeyeon Kim, Jonathan Geuter, David Alvarez-Melis, Sham Kakade, Sitan Chen
    ICML 2026
  9. High-Accuracy and Dimension-Free Sampling with Diffusions [pdf]
    Khashayar Gatmiry, Sitan Chen, Adil Salim
    ICML 2026
    Spotlight paper
  10. Fine-Tuning Masked Diffusion for Provable Self-Correction [pdf]
    Jaeyeon Kim, Seunggeun Kim, Taekyun Lee, David Z. Pan, Hyeji Kim, Sham Kakade, Sitan Chen
    ICML 2026
  11. Computation-Utility-Privacy Tradeoffs in Bayesian Estimation [pdf]
    Sitan Chen, Jingqiu Ding, Mahbod Majid, Walter McKelvie
    STOC 2026
  12. Any-Order Flexible Length Masked Diffusion [pdf] [website] [code]
    Jaeyeon Kim, Lee Cheuk-Kit, Carles Domingo-Enrich, Yilun Du, Sham Kakade, Timothy Ngotiaoco, Sitan Chen, Michael Albergo
    ICLR 2026
  13. S4S: Solving for a Diffusion Model Solver [pdf]
    Eric Frankel, Sitan Chen, Jerry Li, Pang Wei Koh, Lillian J. Ratliff, Sewoong Oh
    ICML 2025
  14. Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions [pdf]
    Jaeyeon Kim, Kulin Shah, Vasilis Kontonis, Sham Kakade, Sitan Chen
    ICML 2025
    Outstanding Paper Award
  15. Blink of an Eye: A Simple Theory for Feature Localization in Generative Models [pdf]
    Marvin Li, Aayush Karan, Sitan Chen
    ICML 2025
    Oral presentation
  16. Gradient Dynamics for Low-Rank Fine-Tuning Beyond Kernels [pdf] [slides]
    Arif Kerem Dayi, Sitan Chen
    COLT 2025
  17. Predicting Quantum Channels Over General Product Distributions [pdf]
    Sitan Chen, Jaume de Dios Pont, Jun-Ting Hsieh, Hsin-Yuan Huang, Jane Lange, Jerry Li
    COLT 2025
  18. Learning General Gaussian Mixtures with Efficient Score Matching [pdf]
    Sitan Chen, Vasilis Kontonis, Kulin Shah
    COLT 2025
  19. Provably Learning a Multi-Head Attention Layer [pdf] [slides]
    Sitan Chen, Yuanzhi Li
    STOC 2025
  20. Stabilizer Bootstrapping: A Recipe for Agnostic Tomography and Magic Estimation [pdf]
    Sitan Chen, Weiyuan Gong, Qi Ye, Zhihan Zhang
    STOC 2025, QIP 2025
    Short plenary talk
  21. Faster Diffusion-Based Sampling with Randomized Midpoints: Sequential and Parallel [pdf]
    Shivam Gupta, Linda Cai, Sitan Chen
    ICLR 2025
  22. Optimal High-Precision Shadow Estimation [pdf]
    Sitan Chen, Jerry Li, Allen Liu
    QIP 2025, merged with [CLL24a]
  23. Efficient Pauli Channel Estimation with Logarithmic Quantum Memory [pdf] [journal]
    Sitan Chen, Weiyuan Gong
    QIP 2025, PRX Quantum
  24. What Does Guidance Do? A Fine-Grained Analysis in a Simple Setting [pdf]
    Muthu Chidambaram, Khashayar Gatmiry, Sitan Chen, Holden Lee, Jianfeng Lu
    NeurIPS 2024
  25. Unrolled Denoising Networks Provably Learn Optimal Bayesian Inference [pdf]
    Aayush Karan, Kulin Shah, Sitan Chen, Yonina C. Eldar
    NeurIPS 2024
  26. Optimal Tradeoffs for Estimating Pauli Observables [pdf]
    Sitan Chen, Weiyuan Gong, Qi Ye
    FOCS 2024, QIP 2025
    Quanta Magazine, Wired Magazine
  27. A Faster and Simpler Algorithm for Learning Shallow Networks [pdf]
    Sitan Chen, Shyam Narayanan
    COLT 2024
  28. Critical Windows: Non-Asymptotic Theory for Feature Emergence in Diffusion Models [pdf]
    Marvin Li, Sitan Chen
    ICML 2024
  29. An Optimal Tradeoff Between Entanglement and Copy Complexity for State Tomography [pdf]
    Sitan Chen, Jerry Li, Allen Liu
    STOC 2024, QIP 2025
  30. Learning Mixtures of Gaussians Using the DDPM Objective [pdf]
    Kulin Shah, Sitan Chen, Adam R. Klivans
    NeurIPS 2023
  31. The Probability Flow ODE Is Provably Fast [pdf]
    Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, Adil Salim
    NeurIPS 2023
  32. When Does Adaptivity Help for Quantum State Learning? [pdf] [slides] [video]
    Sitan Chen, Brice Huang, Jerry Li, Allen Liu, Mark Sellke
    FOCS 2023, QIP 2023
  33. Learning Narrow One-Hidden-Layer ReLU Networks [pdf]
    Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka
    COLT 2023
  34. Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-Type Samplers [pdf]
    Sitan Chen, Giannis Daras, Alexandros G. Dimakis
    ICML 2023
  35. Learning Polynomial Transformations [pdf] [video]
    Sitan Chen, Jerry Li, Yuanzhi Li, Anru R. Zhang
    STOC 2023
  36. Sampling Is as Easy as Learning the Score: Theory for Diffusion Models With Minimal Data Assumptions [pdf] [slides]
    Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru R. Zhang
    ICLR 2023
    Oral presentation
  37. Learning to Predict Arbitrary Quantum Processes [pdf] [slides] [journal]
    Hsin-Yuan Huang, Sitan Chen, John Preskill
    QIP 2023, PRX Quantum
  38. The Complexity of NISQ [pdf] [slides] [video] [journal]
    Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
    QIP 2023, Nature Communications
  39. Learning (Very) Simple Generative Models Is Hard [pdf]
    Sitan Chen, Jerry Li, Yuanzhi Li
    NeurIPS 2022
    Oral presentation
  40. Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks [pdf]
    Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka
    NeurIPS 2022
    Oral presentation
  41. Tight Bounds for Quantum State Certification with Incoherent Measurements [pdf] [slides] [video]
    Sitan Chen, Brice Huang, Jerry Li, Allen Liu
    FOCS 2022, QIP 2023
  42. Quantum Advantage in Learning From Experiments [pdf] [journal]
    Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, Jarrod R. McClean
    Science
  43. Kalman Filtering with Adversarial Corruptions [pdf]
    Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
    STOC 2022
  44. Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs [pdf]
    Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka
    ICLR 2022
  45. A Hierarchy for Replica Quantum Advantage [pdf]
    Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
    QIP 2022, merged with [CCHL21]
  46. Towards Instance-Optimal Quantum State Certification With Independent Measurements [pdf]
    Sitan Chen, Jerry Li, Ryan O'Donnell
    QIP 2022, COLT 2022
    Blurb on Property Testing Review
  47. Symmetric Sparse Boolean Matrix Factorization and Applications [pdf]
    Sitan Chen, Zhao Song, Runzhou Tao, Ruizhe Zhang
    ITCS 2022
  48. Efficiently Learning One Hidden Layer ReLU Networks From Queries [pdf]
    Sitan Chen, Adam R. Klivans, Raghu Meka
    NeurIPS 2021
  49. 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
  50. Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination [pdf]
    Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
    FOCS 2021
  51. Learning Deep ReLU Networks Is Fixed-Parameter Tractable [pdf] [video]
    Sitan Chen, Adam R. Klivans, Raghu Meka
    FOCS 2021
  52. Algorithmic Foundations for the Diffraction Limit [pdf] [slides] [code] [video] [Ankur's Simons tutorial]
    Sitan Chen, Ankur Moitra
    STOC 2021
  53. On InstaHide, Phase Retrieval, and Sparse Matrix Factorization [pdf]
    Sitan Chen, Xiaoxiao Li, Zhao Song, Danyang Zhuo
    ICLR 2021
  54. 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 paper
  55. Learning Structured Distributions from Untrusted Batches: Faster and Simpler [pdf] [code]
    Sitan Chen, Jerry Li, Ankur Moitra
    NeurIPS 2020
  56. Entanglement is Necessary for Optimal Quantum Property Testing [pdf] [slides] [video]
    Sebastien Bubeck, Sitan Chen, Jerry Li
    FOCS 2020
    Blurb on Property Testing Review
  57. Learning Polynomials of Few Relevant Dimensions [pdf] [slides] [video]
    Sitan Chen, Raghu Meka
    COLT 2020
  58. Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Moments [pdf] [slides] [video]
    Sitan Chen, Jerry Li, Zhao Song
    STOC 2020
  59. Efficiently Learning Structured Distributions from Untrusted Batches [pdf] [slides] [video]
    Sitan Chen, Jerry Li, Ankur Moitra
    STOC 2020
  60. 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
  61. Beyond the Low-Degree Algorithm: Mixtures of Subcubes and Their Applications [pdf] [slides]
    Sitan Chen, Ankur Moitra
    STOC 2019
  62. Basis Collapse For Holographic Algorithms over All Domain Sizes [pdf] [slides] [video]
    Sitan Chen
    STOC 2016
  63. Pseudorandomness for Read-Once, Constant-Depth Circuits [pdf]
    Sitan Chen, Thomas Steinke, Salil Vadhan
    Manuscript

Thesis

Service

Other