I am a Professor in Halιcιoğlu Data Science Institute at University of California, San Diego, as well as an affiliated faculty in the Computer Science and Engineering Department at UCSD. I am also the Director for NSF National AI Institute TILOS, The Institute for Learning Enabled Optimization at Scale, which is a joint effort of an amazing team of researchers from UCSD (lead), MIT, National University, UPenn, UT Austin and Yale.

My office is located at HDSI 446.

I obtained my M.S and Ph.D degrees from Duke Univ. (where I received the Best PhD Dissertation Award at CS Dept), and B.S. degree from Tsinghua Univ (graduated with First Class Honors). From 2004-2005, I was a post-doctoral researcher at Geometric Computing lab in Stanford Univ from 2004-2005. Prior to joining UCSD, I was a Professor in the Computer Science and Engineering Department at the Ohio State University. I co-directed the Foundations of Data Science Research Community of Practice (CoP) at Translational Data Analytics Institute (TDAI@OSU) from 2018-2020. From Nov. 2021 - Aug. 2023, I served as the Associate Director for Research for NSF AI Institute TILOS. I received DOE (Dept. of Energy) Career award in 2006, and NSF (National Science Foundation) Career award in 2008. I am on the editorial board for SICOMP, JoCG, and CGTA. I currently serve on the Computational Geometry Steering Committee, as well as the AATRN (Applied and Algebraic Topology Research Network) Advisory Committee. I also serve on SIGACT CATCS Committee, and AWM (Association for Women in Mathematics) Meetings Committee.

New Book Announcement! Together with my collaborator Tamal K. Dey, our new book Computational Topology for Data Analysis was recently published by Cambridge University Press. You can download a e-copy here. A dedicated website is here .

I work in geometric and topological data analysis. I am particularly interested in (1) developing effective and theoretically justified algorithms to analyze complex data using geometric and topological ideas and methods, and (2) integrating algorithmic, geometric, and topological methods with modern machine learning frameworks, especially in graph learning and in geometric deep learning. An important theme of my work is to apply theoretical insights obtained to develop effective machine learning methods for practical domains, including chip design, material science, neuroscience, and computational biology.

Family: Here is a link to my husband Misha Belkin, and our two wonderful children.