Ilia Ilmer
Machine Learning Engineer at Clio - Cloud-Based Legal Technology
Virginia, United States
Summary
👤
Senior🎓
Top SchoolIlia Ilmer is a Machine Learning Engineer with nine years of experience blending research-grade math and production ML across startups and academia, currently building ML systems at Clio. With a PhD-track background in computer science and an MSc in mathematics, he has a strong foundation in differential equations and symbolic methods that he applies to scientific ML. He has contributed to the prominent SciML ModelingToolkit.jl—adding tutorials on parameter identifiability and improving ODE/discrete system support—demonstrating deep familiarity with computer algebra and physics-informed modeling. His career spans applied research roles, teaching, and senior ML engineering at BILL, showing he moves smoothly between prototyping, productionization, and mentoring. Notably, he helped integrate StructuralIdentifiability.jl into the SciML ecosystem during Google Summer of Code, highlighting an ability to bridge niche open-source tooling with broader frameworks. Based in Virginia, he combines rigorous theoretical insight with practical engineering to tackle complex, equation-driven ML problems.
9 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at The Graduate Center, City University of New YorkMaster of Science (MSc) Mathematics, Master of Science (MSc) Mathematics at University of CalgaryBachelor's degree Applied Mathematics and Computer Science, Bachelor's degree Applied Mathematics and Computer Science at National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)
Русский, English, French
Github Skills (8)
symbolic-computation10
differential-equations10
ode10
modeling10
julia10
scientific-machine-learning9
computer-algebra9
testing8
Programming languages (13)
Github contributions (5)
SciML/ModelingToolkit.jl
Aug 2021 - Feb 2022
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
Role in this project:
Backend Developer
Backend DeveloperContributions:5 reviews, 20 commits, 5 PRs in 5 months
Contributions summary:Ilia's contributions focused on enhancing the ModelingToolkit.jl framework. They added a tutorial on parameter identifiability, expanding the documentation and providing guidance on advanced usage. Furthermore, the user integrated updates from the master branch and improved the code, including changes to the discrete and ODE systems. These modifications, along with associated test updates, demonstrate a deep understanding of the library's internal architecture and functionality.
sdescientific-machine-learningtransformationspartial-differential-equationsdifferential
iliailmer/sian-weighted-degree
Dec 2020 - Aug 2022
Contributions:1 PR, 86 pushes, 5 branches in 1 year 7 months
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