Hongyi Zhang (张宏毅)

I am a sixth-year Ph.D. student in the Department of Brain and Cognitive Sciences at MIT working with Prof. Suvrit Sra.

I plan to graduate in February 2019. Feel free to contact me for job opportunities!

I am also affiliated with LIDS and the Machine Learning Group at MIT.

I aim to utilize mathematical insights from high-dimensional statistics and high-dimensional geometry to advance machine learning and nonlinear optimization. I am interested in machine learning and, more broadly, modeling and solving real world computational problems.

Address: 32D-572
77 Massachusetts Avenue,
Cambridge MA, 02139
Email: hongyiz (at) mit (dot) edu


2013 - Present Massachusetts Institute of Technology
Ph.D. candidate in Cognitive Science
2008 - 2013 Peking University
Bachelor in Machine Intelligence


2017/2018 Summer Research Intern,
at Facebook AI Research with Yann Dauphin
2015/2016 Fall Teaching Assistant,
MIT 9.520/6.860 -- Statistical Learning Theory and Applications
2014 Fall Teaching Assistant,
MIT 9.66/6.804/9.660 -- Computational Cognitive Science
2012 Summer Research Intern,
at TTI Chicago with Prof. Raquel Urtasun


An Estimate Sequence for Geodesically Convex Optimization. [arXiv]
Hongyi Zhang, Suvrit Sra.
Proceedings of the 31st Conference On Learning Theory, PMLR 75:1703-1723, 2018..

mixup: Beyond Empirical Risk Minimization. [paper][code]
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz.
6th International Conference on Learning Representations (ICLR 2018)

Matrix Completion from O(n) Samples in Linear Time. [Paper][Long version]
David Gamarnik, Quan Li, Hongyi Zhang;
Proceedings of the 30th Conference on Learning Theory, PMLR 65:940-947, 2017.

Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds. [Paper]
Hongyi Zhang, Sashank J. Reddi, Suvrit Sra.
30th Conference on Neural Information Processing Systems (NIPS 2016)

First-order Methods for Geodesically Convex Optimization. [Paper]
Hongyi Zhang, Suvrit Sra.
Proceedings of the 29th Conference on Learning Theory, PMLR 49:1617-1638, 2016.

Physics 101: Learning Physical Object Properties from Unlabeled Videos. [Paper][Project]
Jiajun Wu, Joseph Lim, Hongyi Zhang, Joshua Tenenbaum and William Freeman.
Proceedings of the British Machine Vision Conference 2016 (BMVC 2016)

Writing Customized Proposals for Probabilistic Programs as Probabilistic Programs.
Hongyi Zhang, Vikash Mansinghka.
3rd NIPS Workshop on Probabilistic Programming. 2014.

Understanding High-Level Semantics by Modeling Traffic Patterns. [Paper][Supplemental][Video]
Hongyi Zhang, Andreas Geiger, Raquel Urtasun.
International Conference on Computer Vision (ICCV'13)