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.


Research Interests:

Optimal Transport, and its applications in

  • - Economics (Screening, Auction)
  • - Machine Learning (GANs)
  • - Statistics (MCMC)
Convex Analysis, Duality, Optimization

Interested Topics Include:
  • Prescribed Jacobian equation, Monge–Ampère equation
  • Langevin Monte Carlo, Fokker–Planck equation
  • The principal-agent framework, Duopoly models
  • Wasserstein GANs

Publications:

[1] 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.
[2] 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.
[3] 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.
[4] Existence in multidimensional screening with general nonlinear preferences [pdf]. Econ. Theory 67(2) (2019) 463-485.
[5] Existence, Uniqueness, Concavity and Geometry of the Monopolist’s Problem Facing Consumers with Nonlinear Price Preferences [pdf]. PhD Thesis, University of Toronto, 2018.
[6] Existence of solutions to principal-agent problems with adverse selection under minimal assumptions [pdf], with Guillaume Carlier. J. Math. Econ. 88 (2020) 64-71.
[7] Wasserstein Control of Mirror Langevin Monte Carlo [pdf]. Kelvin Shuangjian Zhang, Gabriel Peyré, Jalal Fadili, Marcelo Pereyra. In Proc. COLT'20, 2020. [Slides]


Invited Talks
Useful Links:

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

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Dr. Kelvin Shuangjian Zhang
Département de Mathématiques et Applications
École Normale Supérieure (ENS)
45 Rue d’Ulm
75005 Paris, FRANCE
Office: Room T21
Email: szhang [at(@)] ens.fr