Anna Sotnikova
Postdoctoral Researcher at École polytechnique fédérale de Lausanne, EPFL
Lausanne, Vaud, Switzerland
Summary
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Senior🎓
Top SchoolAnna Sotnikova is a postdoctoral researcher at EPFL's NLP lab with 14 years of experience at the intersection of machine learning, applied mathematics, and human-centered evaluation of generative AI. She holds a PhD in Applied Mathematics from the University of Maryland, where her research probed ethical challenges in AI and combined rigorous quantitative methods with qualitative assessment. Her work spans both research and engineering: she contributes to open-source ML projects like Vowpal Wabbit and helped develop course materials and labs for a machine-learning textbook, demonstrating a commitment to reproducible education and efficient, maintainable code. At EPFL she focuses on evaluating large language models and studying their societal and educational impacts, blending algorithmic insight with practical evaluation frameworks. Based in Lausanne, she brings deep numerical and algorithmic expertise, plus real-world software engineering experience from roles including backend and ML system development. Colleagues value her ability to translate complex mathematical ideas into usable tools and clear evaluations that inform policy and pedagogy.
14 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Applied Mathematics, 3.89 out of 4, Doctor of Philosophy - PhD, Applied Mathematics, 3.89 out of 4 at University of MarylandMaster's degree, Applied Mathematics, Master's degree, Applied Mathematics at ITMO University
Github Skills (18)
algorithms10
c-language10
python10
data-science10
machine-learning10
cprogramming-language10
algorithm9
numpy9
implement9
decision-tree8
scikit8
knn8
linear-models8
scikit-learn8
matplotlib7
Programming languages (4)
Github contributions (5)
hal3/ciml
Aug 2015 - Jan 2017
A Course in Machine Learning
Role in this project:
ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:65 commits, 51 pushes, 1 branch in 1 year 5 months
Contributions summary:Anna contributed to a machine-learning course by adding a BibTeX style file, style files for headers and footers, and labs. The user's commits included code for labs related to decision trees and KNN algorithms and a dataset. The user provided code for plotting distances in high dimensions.
deep-learningpythonmachine-learningdata-science
VowpalWabbit/vowpal_wabbit
Jan 2012 - Jul 2017
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:654 commits, 24 PRs, 44 comments in 5 years 7 months
Contributions summary:Anna made a series of commits primarily focused on implementing and refactoring core functionalities within the Vowpal Wabbit (VW) machine learning system. These contributions include the introduction of templated methods for norm computation and templated updates, demonstrating a focus on code efficiency and maintainability. The user also worked on a project using search and a python interface, showing expertise in algorithms and application.
hashingtechniquescpppythonactive-learning
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