I hold a joint position as the Norbert Wiener fellow at the Institute for Data Science and Statistics (IDSS) and an instructor of Applied Mathematics at MIT. Before coming to MIT, I was a PhD student in the Computer Science Department at Princeton University, working under the advisement of Sanjeev Arora. Previously I received my B.S.E. degree at Princeton University as well. I work in the intersection of machine learning and theoretical computer science, with the primary goal of designing provable and practical algorithms for problems arising in machine learning. |

- RAND-WALK: a latent variable model approach to word embeddings. With Sanjeev Arora, Yuanzhi Li, Yingyu Liang and Tengyu Ma.
*Transactions of the Association for Computational Linguistics (TACL), Vol 4, 2016* - Linear algebraic structure of word senses, with applications to polysemy. With Sanjeev Arora, Yuanzhi Li, Yingyu Liang and Tengyu Ma.
*Manuscript* - Automated WordNet Construction Using Word Embeddings.* With Mikhail Khodak, Christiane Fellbaum, Sanjeev Arora.
*EACL Workshop on Sense, Concept and Entity Representations and their Applications, 2017* - Provable benefits of representation learning. With Sanjeev Arora.
*Manuscript* - Theoretical limitations of Encoder-Decoder GAN architectures. With Sanjeev Arora and Yi Zhang.
*Manuscript* - Do GANs learn the distribution? Some theory and empirics. With Sanjeev Arora and Yi Zhang.
*ICLR 2018* - Representational Power of ReLU Networks and Polynomial Kernels: Beyond Worst-Case Analysis. With Frederic Koehler.
*Manuscript.* - Approximability of Discriminators Implies Diversity in GANs. With Yu Bai and Tengyu Ma.
*ICML Workshop on Theoretical Foundations and Applications of Deep Generative Models*

- On some provably correct cases of variational inference for topic models. With Pranjal Awasthi.
*NIPS 2015, Spotlight* - Recovery guarantee of weighted low-rank approximation via alternating minimization. With Yuanzhi Li and Yingyu Liang.
*ICML 2016* - Non-negative matrix factorization using a decode-and-update approach. With Yuanzhi Li and Yingyu Liang.
*NIPS 2016* - Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo. With Rong Ge and Holden Lee.
*NIPS 2018*

- How to calculate partition functions using convex programming hierarchies: provable bounds for variational methods.
*COLT 2016, long talk* - Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods. With Yuanzhi Li.
*NIPS 2016* - Provable learning of noisy-or networks. With Sanjeev Arora, Rong Ge, and Tengyu Ma.
*STOC 2017* - New practical algorithms for learning noisy-or networks via symmetric NMF. With Sanjeev Arora, Rong Ge, and Tengyu Ma.
*Manuscript* - Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective. With Vishesh Jain and Frederik Koehler.
*Manuscript*

- Label optimal regret bounds for online local learning. With Pranjal Awasthi, Moses Charikar and Kevin A. Lai.
*COLT 2015* - Tight algorithms and lower bounds for approximately convex optimization. With Yuanzhi Li.
*NIPS 2016*

- What makes a tree a straight skeleton? With Oswin Aichholzer, Howard Cheng, Thomas Hackl, Stefan Huber, Brian Li.
*Canadian Conference on Computational Geometry 2012.* - Skeletal rigidity of phylogenetic trees. With Howard Cheng, Satyan Devadoss, Brian Li.
*Discrete Applied Mathematics 170, 2014.* - On routing disjoint paths in bounded treewidth graphs. With Alina Ene, Matthias Mnich and Marcin Pilipczuk.
*To appear in SWAT 2016* - On the ability of neural nets to express distributions.* With Holden Lee, Rong Ge, Tengyu Ma, Sanjeev Arora.
*To appear in COLT 2017*

- Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo
- MIT Algorithms and Complexity Seminar, 11/01/17
- Provable algorithms for learning noisy-OR networks
- STOC (Montreal, 2017)
- Theoretical aspects of representation learning
- Simons Institute for the Theory of Computing, 03/27/17
- New techniques for learning and inference in probabilistic graphical models
- MIT Stochastics and Statistics Seminar, 09/08/17
- Microsoft Research Redmond, 02/08/17
- How to calculate partition functions using convex programming hierarchies: provable bounds for variational methods
- Stanford Theory Seminar, 02/02/17
- Los Alamos National Laboratory, 11/07/16
- Rutgers University, 10/19/16
- COLT (New York City, 2016) [Video]
- On some provably correct cases of variational inference for topic models
- NIPS (Montreal, 2015) [Video, talk starts circa 11:45]
- Random walks on context spaces: towards an explanation of the mysteries of semantic word embeddings
- China Theory Week (Jiao Tong University, Shanghai, 2015)
- Label optimal regret bounds for online local learning
- COLT (Paris, 2015) [Video]

- School of Engineering and Applied Science Award for Excellence, 2016
- Princeton Honorific Fellowship nominee, 2016
- Member of Phi Beta Kappa, Tau Beta Pi, Sigma Xi
- Shapiro Prize for freshman year at Princeton University - 2009
- Bronze medal at Balkan Mathematical Olympiad – 2007
- Honorable Mention at International Mathematical Olympiad – 2007

- Grader for COS433 (Cryptography) at Princeton: Fall 2011/12
- Teaching assistant for COS451 (Computational Geometry) at Princeton: Fall 2013/14
- Teaching assistant for COS445 (Networks, Economics and Computing) at Princeton: Spring 2014
- Instructor for 18.200A (Principles of Discrete and Applied Mathematics) at MIT: Fall 2017/18

- The easiest way to reach me is email. My address is lastname
*at*mit.edu