Valeriy Avanesov
Senior Software Engineer at Google
Berlin, Germany
Summary
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Rockstar🎓
Top SchoolValeriy Avanesov is a Senior Software Engineer based in Berlin with 12 years of experience bridging mathematical statistics and production software. With a PhD in Mathematical Statistics and roots from MIPT and MSU, he has moved from leading ML research teams to building robust back-end systems at Google. His contributions to tools like Google's Orbit profiler highlight a practical focus on performance analysis, data estimators, and clear visualization of metrics. Previously he led a Machine Learning subdivision and consulted for industry, blending research-grade stochastic methods with production engineering. Valeriy is known for making “mathematics work” in real systems—translating rigorous statistical ideas into tested, user-facing tooling.
12 years of coding experience
11 years of employment as a software developer
Bachelor of Science (BS), Applied Mathematics, Bachelor of Science (BS), Applied Mathematics at Moscow Institute of Physics and Technology (State University) (MIPT)Doctor of Philosophy (PhD), Mathematical Statistics, Doctor of Philosophy (PhD), Mathematical Statistics at Humboldt University of BerlinComputer Science, Computer Science at Yandex School of Data AnalysisLomonosov Moscow State University
English, German
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (15)
performance-analytics10
c-language10
performance-analysis10
performance-monitor10
performance-monitoring10
cprogramming-language10
code-profiling10
profiler10
profiling10
statistic9
unit-testing9
statistics9
data-visualization8
data-visualisation8
data-visualizations8
Programming languages (4)
Github contributions (5)
google/orbit
Feb 2022 - Oct 2022
C/C++ Performance Profiler
Role in this project:
Back-end Developer
Back-end DeveloperContributions:624 reviews, 179 commits, 310 PRs in 8 months
Contributions summary:Valeriy primarily contributed to the development of performance profiling tools in C/C++. They focused on implementing and testing core functionalities like binomial confidence intervals and performance metrics visualization. Their work included the addition of new data structures, estimators, and unit tests to enhance the capabilities of the performance profiler, demonstrating a focus on data analysis and performance improvements. Moreover, they refactored the code and updated the UI to improve the clarity and usability of the performance data.
instrumentationhookingvisualizeretwperformance
akopich/spark-gp
Mar 2018 - Mar 2021
Gaussian Process Classification and Regression on Apache Spark
Contributions:106 commits, 69 pushes, 1 branch in 3 years 1 month
regressionspark-mlmachine-learningapachespark
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