Vasily Ilin
About Me
I am a Ph.D. student at the University of Washington, working with Jingwei Hu.
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.
Research Interests
- Scientific computing: solve high-dimensional PDEs using interacting particle systems and neural networks.
- Math formalization: accelerate mathematics research with Lean and AI.
News
- [Feb. 2025] RealEdit paper accepted in CVPR, Landau paper accepted in the CMS journal.
- [Jan. 2025] Gave two talks at JMM 2025 on sampling and Lean.
- [Nov. 2024] Submitted a paper on image editing with diffusion models to CVPR 2025.
- [Sep. 2024] Finished my third internship at Google, working on detection of defective TPUs using maching learning.
- [Jun. 2024] Participated in the SLMath summer school on interacting particle systems.
- [May 2024] Released pre-print Transport based particle methods for the Fokker-Planck-Landau equation.
- [Feb. 2024] Passed my general exam.
Publications
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CVPR
Accepted to CVPR, 2025A dataset and a diffusion generative model to perform edits from a prompt. The first global edit model trained on real data. -
CMS
Accepted to Communications in Mathematical Sciences, 2025.An algorithm for simulation of plasma using a neural network, inspired by score-based generative modeling. -
JCon
JuliaCon, 2023.An algorithm to efficiently simulate any point process on the real line with a continuous intensity rate. -
PLOS
PLOS Computational Biology, 2023Julia library for modeling and high-performance simulation of chemical reaction networks.
Selected Talks
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JMM
Joint Mathematics Meetings, 2025An algorithm for deterministic sampling by integrating the Fokker-Planck equation using particles. -
JMM
Joint Mathematics Meetings, 2025Lessons learned in building community and mentoring undergraduate research projects in Lean. -
UW Math AI seminarAn SDE-first introduction to diffusion generative modeling.
Work Experience
- [2024] ML Intern at Google Cloud (Python, SQL) - Trained XGBoost to predict defective TPUs from telemetry, achieved 80% accuracy and improved test efficiency 4x.
- [2023] ML Intern at YouTube (Python, C++, SQL) - Trained ML models to enhance cache efficiency achieving 2x Egress/Ingress improvement. Implemented better load balancing based on two random choices model.
- [2022] Data Engineering Intern at Google (FlumeJava, SQL) - Built distributed pipeline for Nearby Share. Reduced dashboard loading from 30s to 1s using approximate aggregation.
- [2021] Google Summer of Code (Julia) - Implemented and optimized jump process simulation algorithms, improving runtime by 70% (blog post).
Services
Teaching Volunteering
- Experimental Lean Lab 2021-present
- Directed Reading Program 2021
- UW Math Circle 2020-2021
Organizing
- Math AI Seminar 2024-present
Open Source Software
- JumpProcesses.jl 2019-2023
- Mathlib4 2024-present
Teaching
Instructor
- Introduction to Mathematical Formalization, 2024 - New class at the University of Washington, teaching mathematics students to use Lean.
Teaching Assistant
- Discrete Mathematical Modeling, 2023
- Calculus, 2020-2022
- Data Science, 2020
Projects
- [2024] Conditional Score-based generative modeling (PyTorch) - Implemented conditional score-based generative modeling on MNIST from scratch.
- [2024] Formalizing Central Limit Theorem (Lean4) - Formalized several lemmas on the way to the full CLT formalization, pushed them upstream to mathlib4.
- [2024] AI DnD Bot (OpenAI API, PostgreSQL) - Telegram bot for Dungeons & Dragons with long- and short-term memory and image generation features.
- [2024] Mean-field limit of a ResNet (PDE, gradient flows) - Infinite-width limit of a ResNet trained with gradient descent is a Wasserstein gradient flow ( slides ).
- [2024] Stein Variational Gradient Descent (Jax) - Analyzed SVGD algorithm for deterministic sampling (report).
- [2022] Deep Reinforcement Learning in DeepRacer (Python) - Placed in top 3% out of 1000+ participants in AWS virtual race (video, report).
- [2020] Cartier Duals (Homological Algebra) - Several examples of Cartier duals.
- [2020] Fully Dynamic Connectivity (Python) - Benchmarked graph connectivity data structure, exponentially faster than DFS (report).
- [2020] Object-Oriented Projects (Java) - Built a Quest game (CLI, 3k LOC) and Bank terminal (GUI, data persistence, 11k LOC) with the best OOD practices.
- [2020] Dense Low Rank Subgraph (Julia) - Introduced a novel combinatorics problem, proved it is polynomial-time solvable, and developed algorithms (report).
- [2019] Data Mining Competition (Python) - Placed second in class of 100+ graduate students in the competition of predicting citation links between publications in DBLP.
- [2019] Stochastic Simulation Algorithms (Julia) - Implemented and compared numerical methods for chemical stochastic systems.
- [2019] Mini Language (Haskell) - Created an imperative language in Haskell and solved five Project Euler problems.
- [2018] Behavioral Economics Research - Researched intuitive belief propagation.
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