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.
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|
PublicationsAn Estimate Sequence for Geodesically Convex Optimization. [arXiv]
Proceedings of the 31st Conference On Learning Theory, PMLR 75:1703-1723, 2018..
mixup: Beyond Empirical Risk Minimization. [paper][code]
6th International Conference on Learning Representations (ICLR 2018)
Matrix Completion from O(n) Samples in Linear Time. [Paper][Long version]
Proceedings of the 30th Conference on Learning Theory, PMLR 65:940-947, 2017.
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds. [Paper]
30th Conference on Neural Information Processing Systems (NIPS 2016)
First-order Methods for Geodesically Convex Optimization. [Paper]
Proceedings of the 29th Conference on Learning Theory, PMLR 49:1617-1638, 2016.
Physics 101: Learning Physical Object Properties from Unlabeled Videos. [Paper][Project]
Proceedings of the British Machine Vision Conference 2016 (BMVC 2016)
Writing Customized Proposals for Probabilistic Programs as Probabilistic Programs.
3rd NIPS Workshop on Probabilistic Programming. 2014.
Understanding High-Level Semantics by Modeling Traffic Patterns. [Paper][Supplemental][Video]
International Conference on Computer Vision (ICCV'13)