Since 2019 I am a Research Engineer at DeepMind, working on a range of language and bioinformatics problems.
I graduated from Moscow State University in 2014 with a major in Computer Science. During the last years of studies I focused on Machine Learning and joined Yandex School Of Data Analysis, where I studied such courses as Probabilistic Graphical Models by Dmitry Vetrov, Statistical Machine Translation by David Talbot, Deep Learning for Computer Vision by Victor Lempitsky.
Then I did my PhD in Natural Language Understanding under supervision of Prof. Konstantin Vorontsov. We developed Additive Regularization of Topic Models to learn probabilistic interpretable sparse embeddings for words, documents and other modalities.
As a PhD student, I did several internships:
Winter 2017-2018 - ETH Zurich, Data Analytics Lab (Thomas Hofmann’s group). Research on interpretability and sparsity of word embeddings.
Summer 2017 - Google, Assistant team. Research on soft and hard attention in neural networks for dialogue systems.
Fall-Winter 2016 - Yandex Data Factory. Several projects in applied machine learning: time series prediction for ATM withdrawal, news retrieval, news clustering and de-duplicating.
Summer 2016 - Google, Ads Quality team. Models to expand Ads categories with new concepts based on distributional similarities.
You can download my CV from 2017 here.