Tingran Gao
William H. Kruskal Instructor
Department of Statistics
University of Chicago
5747 S Ellis Avenue Jones 316
Chicago IL 60637-1441

## Professional Employments

William H. Kruskal Instructor (2017—present)
Committee on Computational and Applied Mathematics, Department of Statistics, The University of Chicago

Visiting Assistant Professor (2015—2017)
Department of Mathematics, Duke University

## Education

Ph.D. Mathematics (2010—2015)
Duke University, Durham, NC, United States

M.S. Computer Science (2013—2015)
Duke University, Durham, NC, United States

B.S. Mathematics (2006—2010)
Tsinghua University, Beijing, China

I am an applied geometer. Currently, I'm most interested in applying differential geometry, algebraic topology, and representation theory tools to emerging problems in mathematical data science and statistical machine learning.

My research broadly concerns discrete and continuous geometric structures in natural and social sciences. I have worked on scientific problems in marine biology, imaging sciences, evolutionary anthropology, and medical data analysis.

## Latest News

[2019-09-03] Paper "Uniform-in-Time Weak Error Analysis for Stochastic Gradient Descent Algorithms via Diffusion Approximation" with Yuanyuan Feng, Lei Li, Jian-Guo Liu, and Yulong Lu accpted by Communications in Mathematical Sciences!

[2019-09-03] Paper"Unsupervised Co-Learning on $\mathcal{G}$-Manifolds Across Irreducible Representations" with Yifeng Fan and Zhizhen Zhao accepted to NeurIPS 2019!

[2019-08-29] From the Gulf of Maine to the Florida Keys, check out "Why there are so many species at the equator—and so few at the poles" featured in UChicago News!

[2019-08-02] Paper "The Diffusion Geometry of Fibre Bundles: Horizontal Diffusion Maps" accepted by Applied and Computational Harmonic Analysis

[2019-07-19] PI of NSF DMS-1854831

[2019-07-10] The CDAC Data Science Discovery project "Predicting Shifts in Biological Growth Driven by Climate Change: A Geometric Deep Learning Approach" with Dr. Katie S. Collins, Dr. Stewart M. Edie, and Prof. David Jablonski from the biv3d team is featured on the CDAC official website and UChicago News

[2019-06-04] New Preprint "Representation Theoretic Patterns in Multi-Frequency Class Averaging for Three-Dimensional Cryo-Electron Microscopy" with Yifeng Fan and Zhizhen Zhao available on arxiv

[2019-04-21] Paper "Multi-Frequency Phase Synchronization" with Zhizhen Zhao accepted to ICML 2019

[2019-04-14] Paper "The Geometry of Synchronization Problems and Learning Group Actions" with Jacek Brodzki and Sayan Mukherjee accepted by Discrete & Computational Geometry

[2019-03-22] Collaborative project "Predicting Shifts in Biological Growth Driven by Climate Change: A Geometric Deep Learning Approach" with Dr. Jablonski's Group in the Department of the Geophysical Sciences is selected for funding by the University of Chicago Center for Data and Computing (CDAC) Data Science Discovery Fund

[2019-02-12] Two Gaussian process landmarking papers published in the inaugural issue of the SIAM Journal on Mathematics of Data Science

[2019-02-04] New preprint "Uniform-in-Time Weak Error Analysis for Stochastic Gradient Descent Algorithms via Diffusion Approximation" with Yuanyuan Feng, Lei Li, Jian-Guo Liu, and Yulong Lu available on arxiv

[2019-01-24] New preprint "Multi-Frequency Phase Synchronization" with Zhizhen Zhao available on arxiv

[2019-01-08] Paper "Gaussian Process Landmarking on Manifolds" with Shahar Z. Kovalsky and Ingrid Daubechies accepted by SIAM Journal on Mathematics of Data Science (SIMODS)

[2018-12-27] Paper "Gaussian Process Landmarking for Three-Dimensional Geometric Morphometrics" with Shahar Z. Kovalsky, Doug M. Boyer, and Ingrid Daubechies accepted by SIAM Journal on Mathematics of Data Science (SIMODS)