About me
Background
I grew up in Moscow, Russia. At 17 I moved to Boston to attend Boston University. My first academic interest was economics but I switched to math mid-way. In 2017 I spent a semester in Auckland, New Zealand, where I took my first proof based-math class. In 2019 I graduated from BU but stayed another year for a Masters degree in computer science, graduating in 2020. I started my PhD in pure math at the University of Washington in 2020, working on algebraic topology, but switched to applied math in 2022 after the first internship at Google. Since then I have worked on applications of deep learning and interacting particle systems to the Landau equation and to sampling.
Professional interests
I have a wide range of interests. In undergrad I worked stochastic simulation of jump processes with applications to mathematical biology. Then I spent three years learning algebra, algebraic topology, and category theory in grad school. When I switched to applied mathematics, I got interested in using interacting particle systems to numerically solve kinetic equations such as Fokker-Planck and Landau equations. In 2024 I got interested in mathematical underpinnings of deep learning, as well as its applications to practical problems such as sampling. Additionally, I have been working with the interactive proof assistant Lean, leading undergraduent research projects and a special topic course on Lean.
Publications and Featured Talks
2025 | Community and Mentorship Through the Experimental Lean Lab (talk at JMM) | abstract We share lessons we've learned in building community and mentoring undergraduate research projects in Lean.
2025 | RealEdit: Reddit Edits As a Large-scale Empirical Dataset for Image Transformations (submitted to CVPR) A dataset and diffusion generative model to perform arbitrary image edits. The first global edit model trained on real data.
2025 | Deterministic sampling with adaptive score based transport modeling (first author, talk at JMM) | abstract An algorithm for deterministic sampling by integrating the Fokker-Planck equation using particles.
2024 | Transport based particle methods for the Fokker-Planck-Landau equation (first author, submitted to CMS) | pre-print An algorithm for simulation of plasma using a neural network, inspired by score-based generative modeling.
2024 | Extending JumpProcesses.jl for fast point process simulation with time-varying intensities | paper An algorithm to efficiently simulate any point process on the real line with a continuous intensity rate.
2023 | Catalyst: fast biochemical modeling with Julia (PLOS Comp Bio) | paper Julia library for modeling and high-performance simulation of chemical reaction networks.
Personal
In my free time I do rock bouldering and calisthenics. I also like reading popular science books: some my favorites are "How to Hide an Empire" and "The Sixth Extinction."
Contact information
email: vasilin97@gmail.com
Google Scholar: Vasily Ilin
Github: vilin97
CV: Resume