Aukosh Jagannath

Research

I work at the interface of probability theory, high-dimensional statistics, optimization, and mathematical physics. My recent work focuses on the mathematical foundations of data science, including high-dimensional learning dynamics, statistical-computational tradeoffs, random optimization, and spin glass methods.

Selected publications

1. Local geometry of high-dimensional mixture models: Effective spectral theory and dynamical transitions

   with G. Ben Arous, R. Gheissari, and J. Huang

   Annals of Statistics (to appear)

2. High-dimensional limit theorems for SGD: Effective dynamics and critical scaling

   with G. Ben Arous and R. Gheissari

   Communications on Pure and Applied Mathematics 77 no. 3 (2024) 2030–2080

   Conference version appeared in NeurIPS 2022 (Outstanding Paper Award)

3. Shattering versus metastability in spin glasses

   with G. Ben Arous

   Communications on Pure and Applied Mathematics 77 no. 1 (2024) 139–176

4. Hardness of random optimization problems for boolean circuits, low-degree polynomials, and Langevin dynamics

   with D. Gamarnik and A. S. Wein

   SIAM Journal on Computing 53 no. 1 (2024) 1–46

5. Online stochastic gradient descent on non-convex losses from high-dimensional inference

   with G. Ben Arous and R. Gheissari

   Journal of Machine Learning Research 22 no. 106 (2021) 1–51

6. Statistical thresholds for tensor PCA

   with P. Lopatto and L. Miolane

   Annals of Applied Probability 30 no. 4 (2020) 1910–1933

Full publication list and professional activities