Zebang Shen

I am currently a post-doctoral researcher at ETH Zürich, supervised by Prof. Niao He, since the end of 2022. Prior to this role, I was a post-doctoral researcher at the University of Pennsylvania from 2019 to 2022, working under the guidance of Professors Alejandro Ribeiro and Hamed Hassani. I obtained his Bachelor’s degree and Ph.D. in 2014 and 2019, respectively, from Zhejiang University, under the supervision of Prof. Hui Qian.

I am particularly intrigued by the connection between physics and machine learning, and my current research focuses on developing neural network-based methods for solving partial differential equations using entropy dissipation principles. I am also actively involved in optimization in the probability space and stochastic optimization techniques for addressing machine learning problems.

selected publications

Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEs

Zebang Shen , and Zhenfu Wang

Thirty-seventh Conference on Neural Information Processing Systems, 2023
Self-Consistency of the Fokker Planck Equation

Zebang Shen , Zhenfu Wang , Satyen Kale , Alejandro Ribeiro , Amin Karbasi , and Hamed Hassani

In Proceedings of Thirty Fifth Conference on Learning Theory , 2022
Sinkhorn barycenter via functional gradient descent

Zebang Shen , Zhenfu Wang , Alejandro Ribeiro , and Hamed Hassani

In Thirty-fifth Conference on Neural Information Processing Systems , 2020
Sinkhorn natural gradient for generative models

Zebang Shen , Zhenfu Wang , Alejandro Ribeiro , and Hamed Hassani

(Spotlight) Advances in Neural Information Processing Systems, 2020
Stochastic conditional gradient++:(non) convex minimization and continuous submodular maximization

Hamed Hassani , Amin Karbasi , Aryan Mokhtari , and Zebang Shen

SIAM Journal on Optimization, 2020
Hessian aided policy gradient

Zebang Shen , Hamed Hassani , Chao Mi , Hui Qian , and Alejandro Ribeiro

In International conference on machine learning , 2019