About

I am an associate professor at the University of Washington. Previously, I was a principal research scientist at Microsoft Research Redmond.

In Fall 2018 I was the VMware Research Fellow at the Simons Institute. I did my Ph.D at MIT, where I was fortunate to work with Ankur Moitra. I also did my masters at MIT under the wonderful supervision of Nir Shavit.

As an undergrad at the University of Washington, I worked on complexity of branching programs, and how we could prove hardness of techniques used for naturally arising learning problems in database theory and AI.

My primary research interests are in learning theory, (very) broadly defined, including quantum information theory, the science of large foundation models, and high-dimensional statistics. I particularly like applications of analysis and analytic techniques to TCS problems.

Teaching

• Autum 2025 -- CSE 599K: Algorithmic Robust Statistics
• Winter 2025 -- CSE 422: Toolkit for Modern Algorithms
• Fall 2019 -- CSE 599-M: Robustness in Machine Learning
  
  • See also these video lectures I made.

Advising

• Dutch Hansen (Ph.D, 2025—),
• Ziyun Chen (Ph.D, 2024—), co-advised with Shayan Oveis Gharan
• Jaume de Dios Pont (Research Intern, Summer 2023), co-advised with Adil Salim
• Jane Lange (Research Intern, Summer 2023)
• Sidhanth Mohanty (Research Intern, Summer 2022)
• Allen Liu (Research Intern, Summer 2021, Summer 2022)
• Huiying Li (Research Intern, Summer 2020), co-advised with Ece Kamar and Emre Kıcıman.
• Kai Xiao (Research Intern, Summer 2020), co-advised with Sébastien Bubeck.
• Kevin Tian (Research Intern, Summer 2020).
• Ivan Evtimov (Research Intern, Spring 2020), co-advised with Weidong Cui, Ece Kamar, and Emre Kıcıman.
• Tony Duan (MSR AI Resident, 2019–2020).
• Hadi Salman (MSR AI Resident, 2018–2019).
• Sitan Chen (Research Intern, Summer 2019)

Service

• FOCS 2024 Workshop: Recent Advances in Quantum Learning co-organizer.
• PC member STOC 22, SODA 2022, SODA 2024, ITCS 2024.

Papers

Theses

• Principled Approaches to Robust Machine Learning and Beyond
  Jerry Li.
  Ph.D thesis
  George M. Sprowls Award for outstanding Ph.D. theses in EECS at MIT
  Note: any stupid jokes in the thesis are the author's own. Please excuse them. Or don't.
  

• The SprayList: A Scalable Relaxed Priority Queue
  Jerry Li.
  Master's thesis
  

Essays

• Robustness Meets Algorithms
  Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart.
  Communications of the ACM May 2021, Research Highlights
  Technical Perspective: Jacob Steinhardt
  

Preprints

• Agnostic Product Mixed State Tomography via Robust Statistics
  Alvan Arulandu, Ilias Diakonikolas, Daniel Kane, Jerry Li
  in submission
  

• Lower Bounds for Learning Hamiltonians from Time Evolution
  Ziyun Chen, Jerry Li
  in submission
  

Journal Papers

• Query lower bounds for log-concave sampling
  Sinho Chewi, Jaume de Dios Pont, Jerry Li, Chen Lu, Shyam Narayanan
  Journal of the ACM 71 (4), 1-42
  preliminary version in FOCS 2023
  

• The Complexity of NISQ
  Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
  Nature Communications 14 (1), 6001
  preliminary version in QIP 2023
  

• Quantum Advantage in Learning from Experiments
  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, 376 (6598), 2022.

• Robust Estimators in High Dimensions without the Computational Intractability
  Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart.
  SIAM Journal on Computing, 48(2), 2019. Special Issue for FOCS 2016.

• Exact Model Counting of Query Expressions: Limitations of Propositional Methods
  Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu.
  ACM Transactions on Database Systems (TODS), Vol. 42, Issue 1, pages 1:1-1:46, March 2017.

Conference and Workshop Papers

1. Robust Estimation Under Heterogeneous Corruption Rates
   Syomantak Chaudhuri, Jerry Li, Thomas A. Courtade
   to appear, NeurIPS 2025
   

2. The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains
   Scott Geng, Hamish Ivison, Chun-Liang Li, Maarten Sap, Jerry Li, Ranjay Krishna, Pang Wei Koh
   to appear, COLM 2025
   

3. S4S: Solving for a Diffusion Model Solver
   Eric Frankel, Sitan Chen, Jerry Li, Pang Wei Koh, Lillian J. Ratliff, Sewoong Oh
   to appear, ICML 2025
   

4. Predicting quantum channels over general product distributions
   Sitan Chen, Jaume de Dios Pont, Jun-Ting Hsieh, Hsin-Yuan Huang, Jane Lange, Jerry Li
   to appear, COLT 2025
   

5. Learning the closest product state
   Ainesh Bakshi, John Bostanci, William Kretschmer, Zeph Landau, Jerry Li, Allen Liu, Ryan O'Donnell, Ewin Tang
   QIP 2025, plenary presentation
   STOC 2025

6. Optimal High-Precision Shadow Estimation
   Sitan Chen, Jerry Li, Allen Liu
   QIP 2025, merged with [CLL24]
   

7. Semi-Random Matrix Completion via Flow-Based Adaptive Reweighting
   Jonathan Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian
   NeurIPS 2024

8. Black-Box k -to-1 PCA Reductions: Theory and Applications
   Arun Jambulapati, Syamantak Kumar, Jerry Li, Shourya Pandey, Ankit Pensia, Kevin Tian
   COLT 2024

9. An optimal tradeoff between entanglement and copy complexity for state tomography
   Sitan Chen, Jerry Li, Allen Liu
   STOC 2024
   QIP 2025

10. KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval
    Marah I Abdin, Suriya Gunasekar, Varun Chandrasekaran, Jerry Li, Mert Yuksekgonul, Rahee Ghosh Peshawaria, Ranjita Naik, Besmira Nushi
    ICLR 2024

11. Automatic Prompt Optimization with "Gradient Descent" and Beam Search
    Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng
    EMNLP 2023

12. Structured Semidefinite Programming for Recovering Structured Preconditioners
    Arun Jambulapati, Jerry Li, Christopher Musco, Kirankumar Shiragur, Aaron Sidford, Kevin Tian
    NeurIPS 2023
    preliminary version in OPT 2022

13. The Full Landscape of Robust Mean Testing: Sharp Separations between Oblivious and Adaptive Contamination
    Clément L. Canonne, Samuel B. Hopkins, Jerry Li, Allen Liu, Shyam Narayanan
    FOCS 2023
    Invited to appear in special issue of SIAM Journal on Computing for FOCS 2023
    

14. Matrix Completion in Almost-Verification Time
    Jon Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian
    FOCS 2023

15. When Does Adaptivity Help for Quantum State Learning?
    Sitan Chen, Brice Huang, Jerry Li, Allen Liu, Mark Sellke
    FOCS 2023
    preliminary version in QIP 2023, merged with this paper

16. Semi-Random Sparse Recovery in Nearly-Linear Time
    Jon Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian
    COLT 2023
    

17. Sampling Is as Easy as Learning the Score: Theory for Diffusion Models With Minimal Data Assumptions
    Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru R. Zhang
    ICLR 2023, Notable top 5%

18. Learning Polynomial Transformations
    Sitan Chen, Jerry Li, Yuanzhi Li, Anru R. Zhang
    STOC 2023

19. REAP: A Large-Scale Realistic Adversarial Patch Benchmark
    Nabeel Hingun, Chawin Sitawarin, Jerry Li, David Wagner
    ICCV 2023

20. Learning (Very) Simple Generative Models Is Hard
    Sitan Chen, Jerry Li, Yuanzhi Li
    NeurIPS 2022
    

21. Robust Model Selection and Nearly-Proper Learning for GMMs
    Jerry Li, Allen Liu, Ankur Moitra
    NeurIPS 2022

22. Tight Bounds for Quantum State Certification with Incoherent Measurements
    Sitan Chen, Brice Huang, Jerry Li, Allen Liu
    FOCS 2022
    QIP 2023, merged with this paper

23. The Price of Tolerance in Distribution Testing
    Clément Canonne, Gautam Kamath, Ayush Jain, Jerry Li
    COLT 2022

24. Clustering Mixtures with Almost Optimal Separation in Polynomial Time
    Jerry Li, Allen Liu
    STOC 2022
    Invited to appear in special issue of SIAM Journal on Computing for STOC 2022
    

25. Clustering Mixture Models in Almost-Linear Time via List-Decodable Mean Estimation
    Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian
    STOC 2022

26. Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs
    Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka
    ICLR 2022

27. Toward Instance-Optimal State Certification With Incoherent Measurements
    Sitan Chen, Jerry Li, Ryan O'Donnell
    preliminary version in QIP 2022
    COLT 2022

28. Robust Regression Revisited: Acceleration and Improved Estimation Rates
    Arun Jambulapati, Jerry Li, Tselil Schramm, Kevin Tian
    NeurIPS 2021

29. List-Decodable Mean Estimation in Nearly-PCA Time
    Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian
    NeurIPS 2021, Spotlight Presentation

30. A Hierarchy for Replica Quantum Advantage
    Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
    QIP 2022, merged with [CCHL21]
    

31. Exponential Separations between Learning With and Without Quantum Memory
    Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
    FOCS 2021
    QIP 2022
    Invited to appear in special issue of SIAM Journal on Computing for FOCS 2021
    

32. Finding the Mode of a Kernel Density Estimate
    Jasper C.H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, Wai Ming Tai
    ESA 2021

33. Statistical Query Algorithms and Low-Degree Tests Are Almost Equivalent
    Matthew Brennan, Guy Bresler, Samuel B. Hopkins, Jerry Li, Tselil Schramm
    COLT 2021, Best Paper Runner Up

34. Aligning AI With Shared Human Values
    Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, Jacob Steinhardt
    ICLR 2021

35. Byzantine-Resilient Non-Convex Stochastic Gradient Descent
    Dan Alistarh, Zeyuan Allen-Zhu, Faeze Ebrahimianghazani, Jerry Li
    ICLR 2021

36. Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization
    Samuel B. Hopkins, Jerry Li, Fred Zhang
    NeurIPS 2020

37. Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing
    Arun Jambulapati, Jerry Li, Kevin Tian
    NeurIPS 2020, Spotlight Presentation

38. Learning Structured Distributions From Untrusted Batches: Faster and Simpler
    Sitan Chen, Jerry Li, Ankur Moitra
    NeurIPS 2020

39. Robust Covariance Estimation in Nearly-Matrix Multiplication Time
    Jerry Li, Guanghao Ye
    NeurIPS 2020

40. Entanglement is Necessary for Optimal Quantum Property Testing
    Sébastien Bubeck, Sitan Chen, Jerry Li
    FOCS 2020

41. Randomized Smoothing of All Shapes and Sizes
    Greg Yang, Tony Duan, Edward Hu, Hadi Salman, Ilya Razenshteyn, Jerry Li
    ICML 2020

42. Positive Semidefinite Programming: Mixed, Parallel, and Width-Independent
    Arun Jambulapati, Yin Tat Lee, Jerry Li, Swati Padmanabhan, Kevin Tian
    STOC 2020

43. Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Moments
    Sitan Chen, Jerry Li, Zhao Song
    STOC 2020

44. Efficiently Learning Structured Distributions from Untrusted Batches
    Sitan Chen, Jerry Li, Ankur Moitra
    STOC 2020

45. Low-rank Toeplitz Matrix Estimation via Random Ultra-Sparse Rulers
    Hannah Lawrence, Jerry Li, Cameron Musco, Christopher Musco
    ICASSP 2020

46. The Sample Complexity of Toeplitz Covariance Estimation
    Yonina Eldar, Jerry Li, Cameron Musco, Christopher Musco
    SODA 2020

47. Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
    Hadi Salman, Greg Yang, Jerry Li, Pengchuan Zhang, Huan Zhang, Ilya Razenshteyn, Sébastien Bubeck
    NeurIPS 2019, Spotlight Presentation

48. Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
    Yihe Dong, Samuel B. Hopkins, Jerry Li
    NeurIPS 2019, Spotlight Presentation

49. SEVER: A Robust Meta-Algorithm for Stochastic Optimization
    Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart
    preliminary version in SecML 2018, Oral Presentation
    ICML 2019

50. How Hard is Robust Mean Estimation?
    Samuel B. Hopkins, Jerry Li
    COLT 2019

51. On Mean Estimation For General Norms with Statistical Queries
    Jerry Li, Aleksandar Nikolov, Ilya Razenshteyn, Erik Waingarten
    COLT 2019

52. Privately Learning High-Dimensional Distributions
    Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan Ullman
    preliminary version in TPDP 2018
    COLT 2019

53. Spectral Signatures for Backdoor Attacks
    Brandon Tran, Jerry Li, Aleksander Mądry
    NeurIPS 2018

54. Byzantine Stochastic Gradient Descent
    Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li
    NeurIPS 2018

55. On the limitations of first order approximation in GAN dynamics
    Jerry Li, Aleksander Mądry, John Peebles, Ludwig Schmidt
    preliminary version in PADL 2017 as Towards Understanding the Dynamics of Generative Adversarial Networks
    ICML 2018

56. Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms
    Ilias Diakonikolas, Jerry Li, Ludwig Schmidt
    COLT 2018

57. Distributionally Linearizable Data Structures
    Dan Alistarh, Trevor Brown, Justin Kopinsky, Jerry Li, Giorgi Nadiradze
    SPAA 2018
    

58. Mixture Models, Robustness, and Sum of Squares Proofs
    Samuel B. Hopkins, Jerry Li
    STOC 2018
    

59. Robustly Learning a Gaussian: Getting Optimal Error, Efficiently
    Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, Alistair Stewart
    SODA 2018
    

60. QSGD: Communication-Optimal Stochastic Gradient Descent, with Applications to Training Neural Networks
    Dan Alistarh, Demjan Grubić, Jerry Li, Ryota Tomioka, Milan Vojnovic
    preliminary version in OPT 2016
    NIPS 2017, Spotlight Presentation
    Invited for presentation at NVIDIA GTC
    [code] [poster] [video]

61. Being Robust (in High Dimensions) can be Practical
    Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, Alistair Stewart
    ICML 2017
    [code]
    

62. ZipML: An End-to-end Bitwise Framework for Dense Generalized Linear Models
    Hantian Zhang*, Jerry Li*, Kaan Kara, Dan Alistarh, Ji Liu, Ce Zhang
    *equal contribution
    ICML 2017
    

63. The Power of Choice in Priority Scheduling
    Dan Alistarh, Justin Kopinsky, Jerry Li, Giorgi Nadiradze
    PODC 2017

64. Robust Sparse Estimation Tasks in High Dimensions
    Jerry Li
    COLT 2017
    merged with this paper

65. Robust Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities
    Jerry Li, Ludwig Schmidt.
    COLT 2017
    

66. Sample Optimal Density Estimation in Nearly-Linear Time
    Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt.
    SODA 2017
    TCS+ talk by Ilias, which discussed the piecewise polynomial framework and our results at a high level
    

67. Robust Estimators in High Dimensions, without the Computational Intractability
    Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, Alistair Stewart
    FOCS 2016
    Invited to Highlights of Algorithms 2017
    Invited to appear in special issue of SIAM Journal on Computing for FOCS 2016
    Invited to appear in Communications of the ACM Research Highlights
    MIT News, USC Viterbi News

68. Fast Algorithms for Segmented Regression
    Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt
    ICML 2016
    

69. Replacing Mark Bits with Randomness in Fibonacci Heaps
    Jerry Li, John Peebles.
    ICALP 2015
    

70. Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms
    Jayadev Acharya, Ilias Diakonikolas, Chinmay Hegde, Jerry Li, Ludwig Schmidt.
    PODS 2015
    

71. The SprayList: A Scalable Relaxed Priority Queue
    Dan Alistarh, Justin Kopinsky, Jerry Li, Nir Shavit.
    PPoPP 2015, Best Artifact Award
    See also the full version
    [code]
    Slashdot, MIT News

72. On the Importance of Registers for Computability
    Rati Gelashvili, Mohsen Ghaffari, Jerry Li, Nir Shavit.
    OPODIS 2014
    

The following two papers are subsumed by the journal paper Exact Model Counting of Query Expressions: Limitations of Propositional Methods

73. Model Counting of Query Expressions: Limitations of Propositional Methods
    Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu.
    ICDT 2014
    Invited to appear in special issue of ACM Transactions on Database Systems for ICDT 2014.

74. Lower bounds for exact model counting and applications in probabilistic databases
    Paul Beame, Jerry Li, Sudeepa Roy, and Dan Suciu.
    UAI 2013, selected for plenary presentation.
    

Patents

• Efficient training of neural networks
  Dan Alistarh, Jerry Li, Ryota Tomioka, Milan Vojnovic
  

Other Writing

• On Distinctive Properties of Universal Perturbations
  Sung Min Park, Kuo-An Wei, Kai Xiao, Jerry Li, Aleksander Mądry
  manuscript

• Security and Machine Learning in the Real World
  Ivan Evtimov, Weidong Cui, Ece Kamar, Emre Kıcıman, Tadayoshi Kohno, Jerry Li
  manuscript

• Efficient Algorithms for Multidimensional Segmented Regression
  Ilias Diakonikolas, Jerry Li, Anastasia Voloshinov
  manuscript

• Tracking Serial Criminals with a Road Metric
  Mark Bun, Jerry Li, Ian Zemke.
  Our 2010 MCM submission, which was awarded an Outstanding Winner prize (the top prize).
  

Misc

• I reached Challenger in Set 4.5 TFT. This is by far my proudest life accomplishment.