Maxim Kochurov
Founder at NtechLab
Moscow, Moscow, Russia
Summary
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Rockstar🎓
Top SchoolMaxim Kochurov is a founder and principal data scientist with 11 years of experience bridging Bayesian statistics, deep learning, and computer vision. He leads Probabilistic Solutions and advises at PyMC Labs while contributing core backend code to major probabilistic libraries (PyMC, PyTensor/Aesara), including GPU-refactor work and a JAX implementation for FillDiagonal. His industrial track record includes top-ranked face recognition work at NtechLab and large-scale training pipelines, complemented by academic publications from Samsung R&D and an MS in Data Science from Skoltech. Maxim combines research-grade probabilistic modeling (variational inference, NUTS) with production engineering, making him adept at turning advanced Bayesian methods into scalable systems. A detail that sets him apart is hands-on contribution to foundational ML tooling—improving internals of PyMC and array-expression engines—so his impact spans both algorithms and the libraries that power them.
11 years of coding experience
5 years of employment as a software developer
Bachelor's degree, Economics, Bachelor's degree, Economics at Московский Государственный Университет им. М.В. Ломоносова (МГУ)Master's degree, Data Science, Master's degree, Data Science at Skolkovo Institute of Science and Technology
Русский, Английский
Stackoverflow
301reputation
28kreached
3answers
0questions
Github Skills (35)
algorithm10
algorithms10
variational-inference10
probabilistic-programming10
py10
operation10
python10
back-end-development10
tensorrt10
statistics10
data-structure10
gaussian-processes10
mcmc10
pymc10
scipy-stats10
Programming languages (17)
Github contributions (5)
pymc-devs/pymc4
Jun 2018 - Jul 2020
Experimental PyMC interface for TensorFlow Probability. Official work on this project has been discontinued.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:65 commits, 22 PRs, 73 pushes in 2 years 1 month
Contributions summary:Maxim significantly contributed to the PyMC4 project, focusing on implementing and refining core backend functionalities, particularly related to model evaluation and sampling. Their work included refactoring model components, enhancing error handling, and integrating TensorFlow Probability. The user added essential features such as NUTS sampling, XLA compilation, and integration with the ArviZ library, while also implementing more continuous distributions and addressing various bugs. The commits demonstrate a strong understanding of probabilistic programming, TensorFlow, and the project's internal workings.
pythonpymcmachine-learningdiscontinuedprobability
pymc-devs/pymc
Aug 2016 - Dec 2022
Bayesian Modeling and Probabilistic Programming in Python
Role in this project:Data Scientist
Data ScientistContributions:77 reviews, 340 commits, 174 PRs in 6 years 5 months
Contributions summary:Maxim contributed to the development and enhancement of Bayesian modeling and probabilistic programming capabilities within the PyMC project. The user focused on implementing and refining variational inference techniques, including the development of specialized kernels and estimators for Gaussian processes and Gaussian mixture models. The contributions included writing testing implementations for different methods of variational inference, and also fixing issues with the model and its performance. The user’s work involved in scaling the density estimation for different models
pythonbayesian-inferencestatistical-inferencemachine-learningprobabilistic-programming
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