Scholar
Zebang Shen 沈泽邦
Google Scholar ID: klqzFvgAAAAJ
ETH Zürich
Citations & Impact
Citations
823
H-index
16
i10-index
23
Publications
20
Co-authors
0
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEs, 2023, Thirty-seventh Conference on Neural Information Processing Systems
- Self-Consistency of the Fokker Planck Equation, 2022, Proceedings of Thirty Fifth Conference on Learning Theory
- Sinkhorn barycenter via functional gradient descent, 2020, Thirty-fifth Conference on Neural Information Processing Systems
- Sinkhorn natural gradient for generative models, 2020, (Spotlight) Advances in Neural Information Processing Systems
- Stochastic conditional gradient++:(non) convex minimization and continuous submodular maximization, SIAM Journal
Research Experience
- Post-doctoral researcher at the University of Pennsylvania from 2019 to 2022, working under the guidance of Professors Alejandro Ribeiro and Hamed Hassani; Since the end of 2022, a post-doctoral researcher at ETH Zürich, supervised by Prof. Niao He.
Education
- Obtained Bachelor's degree from Zhejiang University in 2014, supervised by Prof. Hui Qian; Obtained Ph.D. from Zhejiang University in 2019, also under the supervision of Prof. Hui Qian.
Background
- Research interests: The connection between physics and machine learning. Current research focuses on developing neural network-based methods for solving partial differential equations using entropy dissipation principles. Also actively involved in optimization in the probability space and stochastic optimization techniques for addressing machine learning problems.
Co-authors