About meWelcome to my personal website. I'm currently a HDSI Postdoc Fellow at the Halιcιoğlu Data Science Institute, University of California San Diego. My mentors are Gal Mishne and Yusu Wang.I obtained my PhD in Mathematics from the Ohio State University. My advisor is Facundo Mémoli. I earned my Bachelor's degree in Mathematics at Peking University in 2016. Here is my current CV. |
|
Research Interests
My primary research interests lie in the development of novel mathematical tools and frameworks for understanding and analyzing complex data structures, with a focus on geometric and topological data analysis. My work spans across multiple domains, including probability theory, geometry, graph theory, and machine learning, with the goal of advancing our ability to analyze complex data.
Check out my recent papers!
- Characterization of Gromov-type geodesics
(With Facundo Mémoli.)
To appear in Differential Geometry and its Applications. arXiv preprint arXiv:2105.05369 (2023).
Novel characterizations of geodesics in the Gromov-Hausdorff space and in the Gromov-Wasserstein space - A generalization of the persistent Laplacian to simplicial maps
(With Aziz Burak Gülen, Facundo Mémoli, Yusu Wang.)
To appear in 39th International Symposium on Computational Geometry (SoCG 2023). arXiv preprint arXiv:2302.03771.
A novel generalization of the framework of the persistent Laplacian to the setting of filtrations of simplicial complexes related via simplicial maps - Weisfeiler-Lehman meets Gromov-Wasserstein
(With Samantha Chen, Sunhyuk Lim, Facundo Mémoli, Yusu Wang.)
International Conference on Machine Learning (ICML 2022).
We introduce a distance between Markov chains based on the classic Weisfeiler-Lehman graph isomorphism test. This distance can be used to analyze GNNs.
Contacts
Email: zcwan[at]ucsd.edu.
Address: E114, San Diego Supercomputer Center, 10100 Hopkins Dr, La Jolla, CA 92093