The new big data era has enabled revolutionary successes and synergy in data modeling, information processing, and decision making in myriad of applications. Yet, a crucial recipi of most successes lies in the intimate integration of optimization and machine learning.
A signifficant aspect of my research concentrates on assimilating techniques from optimization, machine learning, and statistics to address decision-making problems in large-scale and high-dimensional regimes as well as problems under uncertainty. Particularly, I am interested in
I am looking for talented graduate students and postdocs with strong mathematical background and interests in optimization and machine learning. Email me with your CV at firstname.lastname@example.org if you are interested.
 Niao He, Anatoli Juditsky, and Arkadi Nemirovski, “Mirror Prox Algorithm for Multi-Term Composite Minimization and Semi-Separable Problems,” Journal of Computational Optimization and Applications, 61(2), 275-319, 2015.
 Niao He, and Zaid Harchaoui, “Semi-proximal Mirror-Prox for Nonsmooth Composite Minimization,” Neural Information Processing Systems (NIPS), 2015.
 Niao He and Zaid Harchaoui, “Stochastic Semi-Proximal Mirror Prox,” NIPS 8th International Workshop on Optimization for Machine Learning, 2015.
 Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song, “Learning from Conditional Distributions via Dual Kernel Embeddings,” Artificial Intelligence and Statistics (AISTATS), 2017.
 Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Jianshu Chen, Le Song, “Smoothed Dual Embedding Control”, NIPS Deep Reinforcement Learning Symposium, 2017.
Yingxiang Yang, Bo Dai, Negar Kiyavash, and Niao He, “Predictive Approximate Bayesian Computation via Saddle Points,” Neural Information Processing Systems (NIPS), 2018.
 Bo Dai, Hanjun Dai, Arthur Gretton, Dale Schuurmans, Le Song, and Niao He, “Kernel Exponential Family Estimation via Doubly Dual Embedding,” Artificial Intelligence and Statistics (AISTATS), 2019.
 Nan Du, Yichen Wang, Niao He, and Le Song, “Time-sensitive Recommendation From Recurrent User Activities,” Neural Information Processing Systems (NIPS), 2015.
 Niao He, Zaid Harchaoui, Yichen Wang, and Le Song, “Fast Optimization for Non-Lipschitz Poisson Likelihood Models,” arXiv, 2016.
 Yingxiang Yang, Jalal Etsami, Niao He, and Negar Kiyavash, "Online Learning for Multivariate Hawkes Processes," Neural Information Processing Systems (NIPS), 2017.