Alexey Gritsenko
Staff Research Scientist at Google DeepMind
Zurich, Zurich, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolAlexey Gritsenko is a Staff Research Scientist at Google DeepMind in Zurich with 12 years of experience building and scaling ML systems at the intersection of algorithm design, stochastic modelling, and computational biology. He holds a PhD in Bioinformatics/Computational Biology from TU Delft and has blended deep academic expertise with industry impact across Google Brain and Booking.com. Alexey contributes to prominent open-source JAX projects such as Flax and Scenic—adding benchmarks, examples, and a new Hungarian matching solver and DETR reimplementation—demonstrating strong ML engineering and reproducible research skills. His work spans distributed algorithms, optimisation, and very large datasets, frequently fixing race conditions and improving training pipelines for production research code. Colleagues know him for tackling cross-disciplinary problems that bridge gene regulation and financial markets, bringing mathematical rigor to practical engineering. He is continually expanding his toolkit and seeks problems that require both theoretical depth and robust software craftsmanship.
12 years of coding experience
11 years of employment as a software developer
Master’s Degree Biomathematics Bioinformatics and Computational Biology, Master’s Degree Biomathematics Bioinformatics and Computational Biology at Leiden UniversitySpecialist Applied Mathematics and Computer Science, Specialist Applied Mathematics and Computer Science at Immanuel Kant Baltic Federal UniversityDoctor of Philosophy (Ph.D.) Bioinformatics Computational Biology Computer Science, Doctor of Philosophy (Ph.D.) Bioinformatics Computational Biology Computer Science at Delft University of Technology
Russian, English, Dutch
Github Skills (12)
attention-mechanism10
transformers10
object-detection10
computer-vision10
machine-learning10
deeplearning-ai10
deep-learning10
jax10
dtr10
python10
flax10
tensorflow7
Programming languages (5)
Github contributions (5)
google/flax
Mar 2020 - Apr 2021
Flax is a neural network library for JAX that is designed for flexibility.
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 27 commits, 22 PRs in 1 year 1 month
Contributions summary:Alexey contributed to the core library by adding example code and documentation. The user added, modified and improved examples for image classification and natural language processing tasks. The user also added a benchmark testing framework for the project.
deep-learningneural-networksneural-networkflaxjax
google-research/scenic
Jul 2021 - Aug 2022
Scenic: A Jax Library for Computer Vision Research and Beyond
Role in this project:ML Engineer
ML EngineerContributions:21 commits, 15 comments, 8 issues in 1 year
Contributions summary:Alexey primarily contributed to the core components of the Scenic library, specifically focusing on machine learning model implementations, particularly in object detection. Their work includes the addition of a new Hungarian matching solver for object detection tasks, and a re-implementation of a DETR model with several updates to the configuration and optimization processes. Furthermore, the user has improved the training and evaluation pipeline by fixing race conditions.
scenictransformersvision-transformervisiondeep-learning
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