Andrei Paleyes
Visiting Researcher at University of Cambridge
Cambridge, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolAndrei Paleyes is a Cambridge-based researcher and PhD candidate with 11 years of software engineering experience spanning startups, outsourcing, and Big Tech. He blends production-focused MLOps and Bayesian optimization research with hands-on deployment experience from roles at Amazon and Seldon, and currently splits his time between academia and leading engineering at Pasteur Labs & ISI. Notably, he contributed key functionality and maintainability improvements to the popular GPyOpt Bayesian optimization library, enabling external evaluation workflows and stronger test automation. His background combines rigorous mathematical training with practical systems engineering—making him comfortable both designing research-grade ML methods and shipping them reliably at scale.
11 years of coding experience
14 years of employment as a software developer
Master's degree, Mathematics and Computer Science, Master's degree, Mathematics and Computer Science at Belarusian State UniversityDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of CambridgeHigh School, Mathematics, High School, Mathematics at Lyceum of Belarusian State University
Russian, Russian, English
Stackoverflow
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Github Skills (53)
ajax10
razor10
json10
jquery10
controller10
python10
linq10
testing10
machine-learning10
uncertainty-quantification10
asp-net-mvc10
gaussian-processes10
asp-net10
gpy10
bayesian10
Programming languages (12)
Github contributions (5)
SheffieldML/GPyOpt
Oct 2017 - Jan 2023
Gaussian Process Optimization using GPy
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 38 commits, 37 PRs in 5 years 4 months
Contributions summary:Andrei primarily worked on the GPyOpt library, implementing and releasing version 1.2.0. Their contributions included integrating a method to suggest the next locations for external evaluation of the objective function, which allows for flexible integration of external evaluation systems. They also refactored initial design code and automated notebook validity checks, demonstrating a focus on both functionality and maintainability, and added tests to improve the reliability of the code.
pythongpyoptimizationgaussiangaussian-process
anyscale/academy
Jun 2021 - Jun 2021
Ray tutorials from Anyscale
Role in this project:Technical Writer
Technical WriterContributions:5 commits, 1 PR, 3 comments in 2 days
Contributions summary:Andrei primarily contributed to the repository by fixing typos and making minor content improvements within the Ray tutorials. These changes were focused on enhancing the clarity and accuracy of the existing documentation, including code comments and explanatory text within several Jupyter notebooks. The user's commits corrected grammatical errors, added missing words, and removed extraneous content, demonstrating a focus on improving the quality of the educational materials. The changes indicate the user's role is focused on maintaining and refining the educational content for the Anyscale Ray tutorials.
raypythonmachine-learning
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