Ye He

Ye He serves as a Hale Visiting Assistant Professor in the School of Mathematics at the Georgia Institute of Technology, hosted by Prof. Molei Tao. His research bridges mathematical theory and artificial intelligence, focusing on developing rigorous frameworks for machine learning and data science applications.

Education:

• Ph.D. in Mathematics, University of California, Davis (advised by Prof. Krishna Balasubramanian)

Research Interests:

Dr. He specializes in the mathematical foundations of artificial intelligence, with particular emphasis on scalable inference methods for complex data distributions. His work addresses fundamental challenges in sampling theory, diffusion-based generative modeling, and stochastic optimization, aiming to establish theoretical guarantees for practical machine learning algorithms. Current investigations focus on heavy-tailed distributions, non-log-concave sampling, and discrete data generation mechanisms.

Publication Trends:

His 11 most recent publications (2020-2025) reveal a concentrated research program advancing theoretical understanding of sampling algorithms and diffusion models. Work spans top venues including NeurIPS, COLT, and IEEE Transactions on Information Theory, with recurring themes in heavy-tailed distribution sampling, convergence analysis of Langevin dynamics, and optimization of generative architectures. The output demonstrates strong interdisciplinary connections between probability theory, statistics, and machine learning.

Scientific Awards:

No awards were documented in the source materials.

Advising and Grants:

The provided information contains no details regarding student supervision, research grants, or funding sources. Teaching responsibilities include undergraduate courses in calculus, linear algebra, and differential equations.

Research Environment:

Dr. He operates within Georgia Tech's School of Mathematics, collaborating closely with Prof. Molei Tao's research group. His office is located in Skiles 016, and his work contributes to the institution's strengths in mathematical data science and computational theory.