Andrei Atanov
Researcher PHD Student at EPFL (École polytechnique fédérale de Lausanne)
Lausanne, Vaud, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolAndrei Atanov is a CS PhD researcher at EPFL with 11 years of industry and academic experience spanning machine learning, computer vision, and data analysis. He has held research internships at Apple and a junior research fellowship at the Samsung-HSE lab, and he teaches and mentors students in core probabilistic and ML topics. His hands-on contributions to open-source ML teaching material (including implementing regularized linear models and a GLAD generative label model) demonstrate a strong applied toolkit in NumPy, scikit-learn, and pandas. Based in Lausanne, he combines rigorous academic training from EPFL and HSE with practical experience building and evaluating models for real-world problems. An understated strength is his blend of pedagogy and research: he both develops research-grade solutions and codifies them into educational materials that scale learning.
11 years of coding experience
2 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at Higher School of EconomicsDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at EPFL
Github Skills (12)
scikit10
data-preprocessing10
data-analysis10
linear-regression10
pandas10
eval10
machine-learning10
python10
evaluation10
numpy10
scikit-learn10
cross-validation9
Programming languages (2)
Github contributions (5)
esokolov/ml-course-hse
Sep 2018 - May 2019
Машинное обучение на ФКН ВШЭ
Role in this project:
Data Scientist
Data ScientistContributions:17 commits, 16 pushes, 2 comments in 8 months
Contributions summary:Andrei contributed to a machine learning course repository, specifically adding and modifying seminar materials and homework assignments. Their work involved using NumPy, Scikit-learn, and Pandas for data analysis, preprocessing, and model training. The commits demonstrate the implementation and evaluation of linear regression, including regularization techniques, and the application of a generative model (GLAD) for label analysis.
python
AndrewAtanov/StochBN
Aug 2017 - Feb 2018
Contributions:126 commits, 17 pushes in 5 months
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