Sergei Tikhonov

United States

Join Prog.AI to see contacts

Summary

🤩

Rockstar

🎓

Top School

Sergei Tikhonov is a quantitative researcher and machine learning practitioner with 14 years of experience bridging statistics, ML, and quantitative finance. He has delivered production-ready Python systems for risk modeling, VaR automation, and a generative-AI pipeline that combined LLMs, RAG, and SQL to summarize evolving sell-side perspectives. Sergei contributes to prominent open-source Python projects—improving core libraries like aiohttp and Django utilities—where his work fixed subtle race conditions, multipart/streaming bugs, and robustness issues in model tracking and caching. His background includes academic research in diffusion generative models and state-space DSGE time series, and he has taught stochastic processes and introductory ML/deep learning courses. Notably, he pairs hands-on back-end engineering with rigorous validation practices (Monte Carlo/back-testing) and a knack for turning research ideas into reliable, auditable code.

14 years of coding experience

2 years of employment as a software developer

PhD, PhD at The University of Texas at Austin

Bachelor's degree, Bachelor's degree at Higher School of Economics

Master's degree, 3.9/4.0, Master's degree, 3.9/4.0 at University of Chicago

English, Russian

Github Skills (16)

testing10

caching10

http10

orm10

python10

redis10

django10

asyncio10

aiohttp10

software-design9

unit-testing9

http-server7

http-client7

docker4

gunicorn4

Programming languages (7)

JavaC++ShellCGoHTMLPython

Github contributions (5)

jazzband/django-model-utils

Nov 2018 - Oct 2021

Django model mixins and utilities.

Role in this project:

Back-end Developer

Contributions:9 reviews, 6 commits, 8 PRs in 2 years 11 months

Contributions summary:Sergei primarily contributed to improving the `django-model-utils` library by fixing bugs, enhancing existing features, and addressing code issues related to model utilities. Their work involved patching the `FieldTracker` class to correctly handle `save` and `refresh_from_db` methods, ensuring proper functionality and fixing non-picklable model instances. They also added a context manager to allow for more granular control over the field reset behavior of `FieldTracker`, enhancing its flexibility. Further changes also focused on the correct handling of `update_fields` in the context of the save method.

pythonmixinsdjangodjango-modeldjango-admin

Suor/django-cacheops

Jan 2013 - Nov 2021

A slick ORM cache with automatic granular event-driven invalidation.

Role in this project:

Back-end Developer

Contributions:6 commits, 1 PR, 7 comments in 8 years 10 months

Contributions summary:Sergei primarily focused on improving the caching mechanism within the Django application. They addressed race conditions, fixed an extra loop, and added comments for clarity. Additionally, the user corrected a bug in the testing suite by adding a failing test case. Their commits involved modifying the core logic within the `cacheops/invalidation.py` file, improving the stability and reliability of the caching functionality.

pythondjangocachinggranularinvalidation

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.

Request Free Trial