I focus on designing algorithms and compressed data structures for indexing very large collections of sequences and develop methods scalable to the entire sequence read archive. These methods build graph representations that enable analysis and queries, which would otherwise be practically impossible using only the raw data.
Prior to my Ph.D. at ETH Zurich, I studied Math, Physics, and Optimal Control at the Moscow Institute of Physics and Technology (MIPT). Then, I did a double Master’s program studying Mathematics and Machine Learning at MIPT and Skoltech. At the same time, I completed a two-year CS program at the Yandex School of Data Analysis and then interned at Inria Grenoble-Rhône-Alpes working on various problems of computational structural biology.
Ph.D. in Computer Science, present
ETH Zurich, Zurich, Switzerland
M.Sc. in Math. and Computer Science, 2017
Skolkovo Institute of Science and Technology (Skoltech), Moscow, Russia
M.Sc. in Applied Math. and Physics, 2017
Moscow Institute of Physics and Technology (MIPT), Moscow, Russia
PG Dip. in Computer Science, 2016
Yandex School of Data Analysis, Moscow, Russia
B.Sc. in Applied Math. and Physics, 2015
Moscow Institute of Physics and Technology (MIPT), Moscow, Russia
Courses TAed at ETH Zürich, Institute for Machine Learning:
Deep Learning (Fall 2017)
Computational Intelligence Lab (Spring 2018, 2019)
Advanced Machine Learning (Fall 2018, 2019, 2020)
Introduction to Machine Learning (Spring 2020)
Statistical Learning Theory (Spring 2021)
Computational Challenges in Medical Genomics (Spring 2019, 2020, 2021, 2022)