Microsoft Building 99

Redmond, WA 98052

jerrl AT microsoft DOT com

My CV (last updated 2/19/2018)I am a research scientist in the Machine Learning and Optimization Group 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.

My primary research interests are in learning theory and distributed algorithms, but I am broadly interested in many other things in TCS. I particularly like applications of analysis and analytic techniques to TCS problems.

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.

In my free time I enjoy being remarkably mediocre at ultimate frisbee, chess, and piano, amongst other things.

I am teaching a course on robust machine learning at UW in Fall 2019! See the course website for more details.

I am fortunate to have supervised the following amazing junior researchers:

**Hadi Salman**(MSR AI Resident, 2018–2019).**Sitan Chen**(Research Intern, Summer 2019).

**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.*

**The SprayList: A Scalable Relaxed Priority Queue**

Jerry Li.

Master's thesis

**Efficient Algorithms for Multidimensional Segmented Regression**

Ilias Diakonikolas, Jerry Li, Anastasia Voloshinov

manuscript**The Sample Complexity of Toeplitz Covariance Estimation**

Yonina Eldar, Jerry Li, Cameron Musco, Christopher Musco

manuscript

**Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers**

Hadi Salman, Greg Yang, Jerry Li, Pengchuan Zhang, Huan Zhang, Ilya Razenshteyn, Sebastien Bubeck

to appear, NeurIPS 2019,**Spotlight Presentation****Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection**

Yihe Dong, Samuel B. Hopkins, Jerry Li

to appear, NeurIPS 2019,**Spotlight Presentation****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**How Hard is Robust Mean Estimation?**

Samuel B. Hopkins, Jerry Li

COLT 2019**On Mean Estimation For General Norms with Statistical Queries**

Jerry Li, Aleksandar Nikolov, Ilya Razenshteyn, Erik Waingarten

COLT 2019**Privately Learning High-Dimensional Distributions**

Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan Ullman

preliminary version in TPDP 2018

COLT 2019**Spectral Signatures for Backdoor Attacks**

Brandon Tran, Jerry Li, Aleksander Mądry

NeurIPS 2018**Byzantine Stochastic Gradient Descent**

Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li

NeurIPS 2018**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**Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms**

Ilias Diakonikolas, Jerry Li, Ludwig Schmidt

COLT 2018**Distributionally Linearizable Data Structures**

Dan Alistarh, Trevor Brown, Justin Kopinsky, Jerry Li, Giorgi Nadiradze

SPAA 2018

**Mixture Models, Robustness, and Sum of Squares Proofs**

Samuel B. Hopkins, Jerry Li

STOC 2018

**Robustly Learning a Gaussian: Getting Optimal Error, Efficiently**

Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, Alistair Stewart

SODA 2018

**Communication-Efficient Distributed Learning of Discrete Distributions**

Ilias Diakonikolas, Elena Grigorescu, Jerry Li, Abhiram Natarajan, Krzysztof Onak, Ludwig Schmidt

NIPS 2017,**Oral Presentation****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]**Being Robust (in High Dimensions) can be Practical**

Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, Alistair Stewart

ICML 2017

[code]

**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

**The Power of Choice in Priority Scheduling**

Dan Alistarh, Justin Kopinsky, Jerry Li, Giorgi Nadiradze

PODC 2017**Robust Sparse Estimation Tasks in High Dimensions**

Jerry Li

COLT 2017

merged with this paper**Robust Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities**

Jerry Li, Ludwig Schmidt.

COLT 2017

**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

**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**Fast Algorithms for Segmented Regression**

Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt

ICML 2016

**Replacing Mark Bits with Randomness in Fibonacci Heaps**

Jerry Li, John Peebles.

ICALP 2015

**Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms**

Jayadev Acharya, Ilias Diakonikolas, Chinmay Hegde, Jerry Li, Ludwig Schmidt.

PODS 2015

**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**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 **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.****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.

**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.

**Efficient training of neural networks**

Dan Alistarh, Jerry Li, Ryota Tomioka, Milan Vojnovic

in submission

**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).

**Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection**

Columbia Theory Seminar, July 2019

**Efficiently Learning from Untrusted Batches**

NYU Computation, Information, and Mathematics Seminar, July 2019

**The Sample Complexity of Toeplitz Covariance Estimation**

MIT Algorithms and Complexity Seminar, May 2019

**Efficient Algorithms for High Dimensional Robust Learning**

MSR AI Seminar, April 2019

**Nearly Optimal Algorithms for Robust Mean Estimation**

MIT Algorithms and Complexity Seminar, February 2019

TTIC Machine Learning Seminar, February 2019

MSR MLO Lunch, January 2019

UW Theory Seminar, January 2019

**"Explicitly" Learning Mixtures of Gaussians**

Simons Fellows Reading Group, September 2018

**Robustly Learning a Gaussian in High Dimensions: Getting Optimal Error, Efficiently**

SODA 2018, January 2018

**Mixture Models, Robustness, and Sum-of-Squares Proofs**

Google Algorithms Reading Group, July 2018

Microsoft Research Redmond, December 2017

MIT Algorithms and Complexity Semniar, November 2017

**QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding**

NIPS 2017, December 2017

**Being Robust (in High Dimensions) can be Practical**

ICML 2017, August 2017

**Robust Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities**

COLT 2017, July 2017

**Efficient Robust Sparse Estimation in High Dimensions**

COLT 2017, July 2017. Joint with Simon Du

**Robust Estimators In High Dimensions without the Computational Intractability**[slides]

**Quantized Stochastic Gradient Descent**

MIT ML Tea, October 2016

**Fast Algorithms for Segmented Regression**[slides]

ICML 2016 [video]

**Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms**[slides]

PODS 2015

**Model Counting of Query Expressions: Limitations of Propositional Methods**[slides]

ICDT 2015

MIT Theory Lunch, 2014

I'm participating in Algorithms Office Hours. If you're affiliated with MIT, and have algorithmic questions, please contact us!

I am on the steering committee for SLOGN*

I organized the Great Ideas in Theoretical Computer Science (aka theory lunch) in the 2013-2014 academic year.

I stole the boombox from the Glorious Office 3 times, then promptly lost it back each time.