Sergey Kolesnikov
Head Of Applied Research at Shadow Startup
Singapore
Summary
🤩
Rockstar🎓
Top SchoolSergey Kolesnikov is a Head of Applied Research based in Singapore with a decade of experience bridging academic research and industrial AI productization, focused on reinforcement learning, deep learning, and sequential decision making. He founded and led Tinkoff's AI Research department, publishing 20+ papers including multiple spotlights while building open-source ecosystems and an academic partnership with MIPT. As creator and lead developer of the Catalyst PyTorch framework (3.3k stars, 7M downloads) and contributor to Kaggle/docker-python tests, he blends hands-on engineering with reproducible research practices. His career spans leading NLP, CV and recommender teams to doubling chatbot automation and deploying human-in-the-loop CV-OCR pipelines, demonstrating an ability to move models into production at scale. Now heading applied research at a stealth-stage startup, he continues to develop novel algorithms alongside practical systems engineering. A mathematician by training from MIPT, he uniquely pairs rigorous theory with pragmatic product delivery and developer-first tooling.
10 years of coding experience
6 years of employment as a software developer
Master's degree, Mathematics and Computer Science, Master's degree, Mathematics and Computer Science at Moscow Institute of Physics and Technology (State University) (MIPT)
Github Skills (6)
unit-testing10
pytorch10
unit-test10
python10
test-automation10
numpy8
Programming languages (10)
Github contributions (5)
Kaggle/docker-python
Sep 2019 - Sep 2019
Kaggle Python docker image
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:7 commits, 1 PR, 6 comments in 6 days
Contributions summary:Sergey's commits primarily focus on creating and updating test files within the `tests` directory. These tests cover functionality within the `catalyst` library, including version verification and the execution of an MNIST example with PyTorch. The user implemented and modified tests to ensure the correct behavior of the library, specifically focusing on the training and evaluation process of a neural network model. The commits reflect an iterative approach, adding new tests and refining existing ones to improve test coverage.
kagglepython-docker
catalyst-team/detection
Mar 2019 - Sep 2021
Catalyst.Detection
Contributions:15 commits, 5 PRs, 14 pushes in 2 years 6 months
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