Qi Lei (雷琦)

Qi Lei UT Austin 

Email: leiqi at ices.utexas.edu

Website: http://users.ices.utexas.edu/~leiqi/

I am a Ph.D student at Institute for Computational Engineering and Sciences, University of Texas at Austin, working with Professor Inderjit S. Dhillon and Alexandros G. Dimakis. I am also a member of Center for Big Data Analytics.

My research interests are machine learning, optimization and linear algebra. (Resume, Github, Google Scholar)


Unversity of Texas at Austin, Austin, TX

Ph.D student in Institute for Computational Engineering and Sciences       August 2014 - Present

Zhejiang University, Zhejiang, China

B.S. in Mathematics        August 2010 - May 2014

Industry Experience

IBM Thomas J. Watson Research Center, Yorktown Heights, NY

Research Summer Intern        March 2016 - October 2016

Amazon Web Services (AWS) Deep Learning Team, Palo Alto, CA

Applied Scientist Intern        January 2017 - April 2017

Amazon/A9 Product Search, Palo Alto, CA

Software Development Intern, Search Technologies       May 2017 - August 2017

Seleted Talk

“Coordinate Descent Methods for Matrix Factorization”, July/11/2016: Minisymposium on Recent Advances in Nonnegative Matrix Factorization, SIAM Annual Meeting, Boston, USA


Qi Lei, Jinfeng Yi, Roman Vaculin, Lingfei Wu, Inderjit Dhillon. “Similarity Preserving Representation Learning for Time Series Analysis”. (code) (results)

Jinfeng Yi, Qi Lei, Wesley Gifford, Ji Liu. “Negative-Unlabeled Tensor Factorization for Location Category Inference from Inaccurate Mobility Data” (code)


Jiong Zhang, Qi Lei, Inderjit Dhillon, “Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization”, Deep Learning at Supercomputer scale Workshop at NIPS, Dec. 2017

Hsiang-fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit Dhillon, “A Greedy Approach for Budgeted Maximum Inner Product Search”, Proc. of Neural Information Processing Systems (NIPS) 2017

Qi Lei, Enxu Yan, Chao-yuan Wu, Pradeep Ravikumar, Inderjit Dhillon, “Doubly Greedy Primal-Dual Coordinate Methods for Sparse Empirical Risk Minimization”, Proc. of International Conference of Machine Learning (ICML), 2017 (code)

Rashish Tandon, Qi Lei, Alexandros G. Dimakis, Nikos Karampatziakis, “Gradient Coding: Avoiding Stragglers in Distributed Learning”, Proc. of International Conference of Machine Learning (ICML), 2017 (code)

Qi Lei, Kai Zhong, Inderjit. Dhillon, “Coordinate-wise Power Method”, Proc. of Neural Information Processing Systems (NIPS), Dec. 2016 (code,poster)

Rashish Tandon, Qi Lei, Alexandros G. Dimakis, Nikos Karampatziakis, “Gradient Coding”, ML Systems Workshop at NIPS, Dec. 2016

Arnaud Vandaele, Nicolas Gillis, Qi Lei, Kai Zhong, Inderjit Dhillon, “Efficient and Non-Convex Coordinate Descent Methods for Symmetric Nonnegative Matrix Factorization”, IEEE Transactions on Signal Processing 64.21 (2016): 5571-5584 (code)

Maria R. D'Orsogna, Qi Lei, Tom Chou, “First assembly times and equilibration in stochastic coagulation-fragmentation”, The Journal of Chemical Physics, 2015: 143.1, 014112

Jiazhou Chen, Qi Lei, Yongwei Miao, Qunsheng Peng, “Vectorization of Line Drawing Image based on Junction Analysis”, Science China Information Sciences, 2014:1-14 (code)

Jiazhou Chen, Qi Lei, Fan Zhong, Qunsheng Peng, “Interactive Tensor Field Design Based on Line Singularities”, Proceedings of the 13th International CAD/Graphics, 2013 (code)


“Method and System for General and Efficient Time Series Representation Learning via Dynamic Time Warping”
with J. Yi, R. Vaculin, W. Sun

“Real-Time Cold Start Recommendation and Rationale within a Dialog System”
with J. Yi, R. Vaculin, M. Pietro