Boris Oreshkin
Principal Scientist at Amazon
Montreal, Quebec, Canada
Summary
👤
Senior🎓
Top SchoolBoris Oreshkin is a Principal Scientist based in Montreal with two decades of expertise in algorithm design and research-driven software development, currently leading ML research at Amazon. He has a strong track record of turning academic advances into industrial impact—lead author of N-BEATS (state-of-the-art time series forecasting) and ProtoRes (3D pose authoring), and driving applied ML across 3D animation, biometric sensing, and forecasting. His career bridges deep academic work (PhD and multiple postdocs) with product-focused roles at Unity, Apple, Element AI and startups, delivering production-ready algorithms in C/C++, embedded systems, and large-scale ML pipelines. Known for shipping rigorous, validated models, he pairs statistical testing and cross-validation practices with GPU-accelerated neural methods to solve real-world problems. He also repeatedly led cross-functional teams and industry collaborations, translating complex research into customer POCs and partnerships. A less obvious strength is his breadth across domains—from medical imaging and sensor fusion to graphics and ad networks—enabling creative cross-pollination of techniques.
9 years of coding experience
9 years of employment as a software developer
Engineer Diploma Electrical Engineering, Engineer Diploma Electrical Engineering at Ryazan State Radiotechnical University (former Academy)Doctor of Philosophy (Ph.D.) Electrical Engineering, Doctor of Philosophy (Ph.D.) Electrical Engineering at McGill University
Russian, French, English
Github Skills (22)
probabilistic8
few-shot-learning8
bottom-up7
bank6
reserve6
machine-learning6
data-api6
python6
pytorch6
deep-learning5
probabilistic-programming5
data-science4
python-client4
regression4
api3
Programming languages (2)
Github contributions (5)
boreshkinai/delta-interpolator
Jan 2022 - Aug 2023
Contributions:2 reviews, 4 PRs, 28 pushes in 1 year 6 months
ServiceNow/TADAM
Jan 2019 - Dec 2021
The implementation of https://papers.nips.cc/paper/7352-tadam-task-dependent-adaptive-metric-for-improved-few-shot-learning
Contributions:1 PR, 5 pushes, 6 branches in 2 years 11 months
pytorchimproveddeep-learningadaptiveshot-learning
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.