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Current coursework: CS591: Deep Learning, CS537: Randomness in Computing, CS531: Advanced optimization algorithms. Completed coursework: CS542: Machine Learning, CS565: Algorithmic Data Mining. Previously Arsenii was studying economics, and took several undergraduate and graduate-level courses on econometrics and time-series analysis. Prior to coming to BU, Arsenii has been working as a machine learning developer. Arsenii spent four years in Mainland China and speaks fluent Mandarin, in addition to fluent English and native Russian. |
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Algorithmic: Deep Learning (including RNN & CNN), Reinforcement Learning, classic Machine Learning algorithms, Time Series analysis Software: Tensorflow (1&2), PyTorch, Linux (+git), SQL |
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Arsenii completed several learning, research and practice-oriented projects on machine learning. |
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Arsenii got second place on graduate machine learning class competition where students were required to predict airbnb property listing prices without using external machine learning libraries. |
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Arsenii has completed this project during his work with Intel (Shanghai). This this prject he has build a neural network module from scrath, using only numpy. He applied this module to implement Reinforcement learnig technics, from simple DQN and Policy Gradient, to TRPO and PPO with GAE, which back then were state of art algorithms. |
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In this paper Arsenii applied quantitative analysis to assess, how formation of Eurasian Custom Union and Eurasion Economic union affected trade between member countries. Difference-in-diffirence methodology and various settings of gravity model were applied, trade dynamics of member countries was compared with trade dynamics among wider set of countries - members of Commonwealth of Independent States. Research have shown, that such strong measures as custom and econimic union formation has not significantly affected trade between union members. |
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