Computer Science Department, Princeton University

Address: 35 Olden Street, Princeton, NJ, 08540,

E-mail: firstname at cs dot princeton dot edu

Office: 416, CS building

Hi! I am currently a fifth-year graduate student at Princeton University, advised by Professor Sanjeev Arora. My research interests broadly include topics in machine learning and algorithms, such as non-convex optimization, representation learning, deep learning, distributed optimization, and convex relaxation (e.g. sum of squares hierarchy). Before coming to U.S., I studied at Andrew Chi-Chih Yao's CS pilot class at Tsinghua University.
**Awards**
**Manuscripts (all papers' authors are in alphabetical order) **
**Publications (all papers' authors are in alphabetical order) **
**Links **
**blog posts: **
**Sanjeev's posts regarding our word embedding works**
**Talks**
CMU CSC Seminar, Mar 2017, Pittsburgh, USA
Caltech CMS Special Seminar, Mar 2017, Pasadena, USA
UW Computer Science Engineering Colloquium, April 2017, Seattle, USA
OpenAI, April 2017, San Francisco, USA
Facebook AI Research, May 2017, Menlo Park, USA
**Service**

2016 NIPS best student paper award

2017.01-05 Simons-Berkeley Research Fellowship

2016-2017 Siebel Scholarship

2016-2017
Princeton Honorific Fellowship

2015 Wu Prize for Excellence

2015-2016 IBM PhD Fellowship

2014-2016 Simons Award for Graduate Students in Theoretical Computer Science

2010.12 8th Place in Putnam Mathematical Competition (Putnam10)

2007.07 Silver Medal in 47th International Mathematical Olympiad (IMO07)

with Sanjeev Arora, Rong Ge, Yingyu Liang, and Yi Zhang

manuscript, 2017

with Sanjeev Arora, Yuanzhi Li, Yingyu Liang, and Andrej Risteski

manuscript, 2016

with Moritz Hardt and Benjamin Recht

manuscript, 2016

On the Optimization Landscape of Tensor decompositions

with Rong Ge

manuscript, 2016 (Best paper in the NIPS 2016 Workshop on Nonconvex Optimization for Machine Learning: Theory and Practice)

with Moritz Hardt

International Conference on Learning Representations (ICLR) 2017, to appear

with Sanjeev Arora and Yingyu Liang

International Conference on Learning Representations (ICLR) 2017, to appear

with Naman Agarwal, Zeyuan Allen-Zhu, Brian Bullins, and Elad Hazan

STOC 2017, to appear

with Sanjeev Arora, Rong Ge, and Andrej Risteski

STOC 2017, to appear

with Rong Ge and Jason D. Lee

NIPS (**best student paper award**), 2016

with Elad Hazan

NIPS 2016

with Jonathan Shi and David Steurer

FOCS 2016

with Sanjeev Arora, Rong Ge, Frederic Koehler, and Ankur Moitra

ICML 2016

with Sanjeev Arora, Yuanzhi Li, Yingyu Liang, and Andrej Risteski

Transactions of the Association for Computational Linguistics (TACL), 4:385-399, 2016

with Mark Braverman, Ankit Garg, Huy L. Nguyen, and David P. Woodruff

STOC 2016

with Avi Wigderson

NIPS 2015

with Sanjeev Arora and Yingyu Liang

ICLR workshop, 2016

with Rong Ge

RANDOM/APPROX 2015

with Dan Garber
and Elad Hazan

ICML 2015

with Sanjeev Arora, Rong Ge, and Ankur Moitra

COLT 2015

with Ankit Garg and Huy Nguyễn

NIPS 2014 (oral)

with Sanjeev Arora, Aditya Bhaskara, and Rong Ge

ICML 2014

with Bo Tang and Yajun Wang

Theory of Computing Systems, 2016

Proceedings of 30th Symposium on Theoretical Aspects of Computer Science (STACS 2013)

Gradient Descent Learns Linear Dynamical Systems
(with Moritz Hardt)

Back-propagation, an introduction (with Sanjeev Arora)

A Framework for Analysing Non-Convex Optimization (with Sanjeev Arora)

Generalization and Equilibrium in Generative Adversarial Nets (GANs)

OpenAI, May 2017, San Francisco, USA
Better Understanding of Non-convex Methods in Machine Learning

Stanford Statistics Department Seminar, Jan 2017, Stanford, USA
MIT EECS Special Seminar, Feb 2017, Cambridge, USA

Stanford CS Seminar, Feb 2017, Stanford, USA

Berkeley CS Seminar, Mar 2017, Berkeley, USA

Columbia CS Seminar, Mar 2017, NYC, USA
Analyzing Non-convex Optimization: Matrix Completion and Linear Residual Networks

MIT algorithms and complexity seminar, Dec 2016, Cambridge, USA

Stanford ML lunch, Nov 2016, Stanford, USA

MSR Talks Series, Nov 2016, Redmond, USA

Matrix Completion has No Spurious Local Minimum

NIPS, Dec 2016, Barcelona, Spain

Columbia theory seminar, Oct 2016, New York City, USA

Yale YNPG seminar, Oct 2016, New Haven, USA

Bekeley, Sept 2016, USA

Sum-of-squares Algorithms for Over-complete Tensor Decomposition

Stanford theory seminar, Sept 2016, Stanford, USA

IAS CSDM seminar, Mar 2016, Princeton, USA

Gradient Descent Learns Linear Dynamical Systems

IMA workshop, May 2016, Minneapolis, USA

Communication Lower Bounds For Statistical Estimation Problems via a Distributed Data Processing Inequality

STOC, Jun 2016, Boston, USA

Invited talk at CISS, Mar 2016, Princeton, USA

The Linear Algebraic Structure of Word Meanings

UW Theory seminar, Apr 2016, Madison, USA

MSR Talk Series, Nov 2015, Redmond, USA

Analyzing Non-convex Optimization for Dictionary Learning

ICML, July 2015, Lille, France

MSR Redmond, Nev 2014, Redmond, USA

Dagstuhl Seminar, Sep 2014, Dagstuhl, Germany

On Communication Cost of Distributed Statistical Estimation and Dimensionality

NIPS, Dec 2014, Montreal, Canada

Provable Bounds for Learning Some Deep Representations

ICML, Jun 2014, Beijing, China

Columbia theory lunch, Feb 2014, NYC, USA

CMU theory lunch, Feb 2014, Pittsburgh, USA

Simulate Greedy Algorithms for Several Submodular Matroid Secretary Problems

30th Symposium on Theoretical Aspects of Computer Science(STACS), Kiel, Germany, Feb 2013

A New Variation of Hat Guessing Games

17th International Computing and Combinatorics Conference(COCOON), Dallas, Texas, Aug 2011

Journal refereeing: Journal of Machine Learning Research, Mathematics of Operations Research, IEEE Transaction on Information Theory, Optimization Methods and Software, Theoretical Computer Science, Transactions on Pattern Analysis and Machine Intelligence

Conference refereeing: STOC, FOCS, ICML (with outstanding reviewer award in 2016), NIPS, COLT, AAAI(PC member), SODA, ISSAC