Kelvin Shuangjian Zhang
I am a Postdoctoral Fellow at the Department of Mathematics and Applications, ENS Paris, working with Professor Gabriel Peyré.
I received my Ph.D. degree from the Department of Mathematics, University of Toronto in 2018, under the supervision of Professor Robert J. McCann. During summer 2018, I visited Professor Guillaume Carlier at MOKAPLAN, INRIA. Then I worked with Professor Marco Cuturi as a Postdoctoral Fellow at CREST - ENSAE ParisTech, before I moved to ENS Paris.
Optimal Transport, and its applications in
- - Economics (Screening, Auction)
- - Machine Learning (GANs)
- - Statistics (MCMC)
Interested Topics Include:
- Prescribed Jacobian equation, Monge–Ampère equation
- Langevin Monte Carlo, Fokker–Planck equation
- The principal-agent framework, Duopoly models
- Wasserstein GANs
 Circular cone: a novel approach for protein ligand shape matching using modified PCA [pdf]. Kelvin Shuangjian Zhang, Jun Du, Liang Zhang, Cheng Zeng, Qiao Liu, Tao Zhang and Gang Hu. Computer Methods and Programs in Biomedicine 108(1) (2012) 168-175.
 Implicit manifold learning on generative adversarial networks [pdf]. Kry Yik Chau Lui, Yanshuai Cao, Maxime Gazeau, Kelvin Shuangjian Zhang. ICML2017 Workshop on Implicit Generative Models, Sydney, 2017.
 On concavity of the monopolist's problem facing consumers with nonlinear price preferences [pdf], with Robert J. McCann. Comm. Pure Appl. Math. 72(7) (2019) 1386-1423.
 Existence in multidimensional screening with general nonlinear preferences [pdf]. Econ. Theory 67(2) (2019) 463-485.
 Existence, Uniqueness, Concavity and Geometry of the Monopolist’s Problem Facing Consumers with Nonlinear Price Preferences [pdf]. PhD Thesis, University of Toronto, 2018.
 Existence of solutions to principal-agent problems with adverse selection under minimal assumptions [pdf], with Guillaume Carlier. J. Math. Econ. 88 (2020) 64-71.
 Wasserstein Control of Mirror Langevin Monte Carlo [pdf]. Kelvin Shuangjian Zhang, Gabriel Peyré, Jalal Fadili, Marcelo Pereyra. In Proc. COLT'20, 2020. [Slides]
arXiv | MathSciNet | AMS | CMS | CREST | ENSAE | BIRS | MSRI | Fields | PIMS | CRM | CIM | INRIA | MOKAPLAN | Vector | BorealisAI | Layer6AI | fast.ai | GitHub | Coursera | UofT library | ScienceDirect | JSTOR | LibGen | Google Scholar | UofT Economics seminars | UofT Math seminars | CREST Economics seminars | CREST Statistics seminars | Statistical Machine Learning in Paris | Séminaire Parisien d'Optimisation