معرفی
Leonid Petrov is a Professor of Mathematics at the University of Virginia, where he has been faculty since completing his postdoctoral position at Northeastern University (2011-2014). He maintains an active research program in integrable probability and serves as an AI guide for the Department of Mathematics.
His educational background includes a Ph.D. completed in 2010 in Moscow under advisor Grigori Olshanski. Petrov has held visiting positions at prestigious institutions including MIT (2017-2018), MSRI (Fall 2021), and IPAM (Spring 2024).
- Integrable probability (primary research area)
- Applications of symmetric functions and Yang-Baxter equations
- Analysis of stochastic models in statistical physics
- Real-world applications including condensed matter structure, magnetism, and traffic models
- AI tool development for mathematicians
Petrov's research examines the asymptotic behavior of stochastic models at the interface between probability/statistical physics and representation theory/quantum integrability. His work connects theoretical mathematics with practical applications across multiple scientific domains.
- NSF grant 2153869 'Random Systems from Symmetric Functions and Vertex Models' (2022-2025)
- Focused Research Group in Integrable Probability (2017-2022)
- Organizer of 'All roads to the KPZ universality class' workshop at American Institute of Mathematics (March 2025)
Petrov actively mentors students and postdocs, currently advising Ph.D. student Mikhail Tikhonov and previously mentoring postdocs Daniel Slonim (now at Hillsdale College), Axel Saenz (now at Oregon State), and Svetlana Gavrilova (now Ph.D. student at MIT). He teaches graduate courses including 'Random Matrices' in Spring 2025 and has developed interactive simulations to illustrate concepts in integrable probability. Petrov also participates in initiatives to broaden access to AI tools for working mathematicians through sharing best practices and panel discussions.
