Алексей Шпильман
Исследовательские группы
- Методы машинного обучения в области программной инженерии
- Прикладное машинное обучение и глубокое обучение
- Агентные системы и обучение с подкреплением
Биография
Окончил факультет Биоинженерии и Биоинформатики МГУ им. Ломоносова.
Профессиональная активность
Руководитель образовательной программы, Научно-исследовательский и образовательный центр «ДжетБрейнс», Санкт-Петербург.
Заведующий центром анализа данных и машинного обучения НИУ ВШЭ, Санкт-Петербург.
Руководитель лаборатории прикладного машинного обучения и глубокого обучения, JetBrains Research, Санкт-Петербург.
Руководитель лаборатории агентных систем и обучения с подкреплением, JetBrains Research, Санкт-Петербург.
Публикации
Lipophilicity Prediction with Multitask Learning and Molecular Substructures Representation
December 2020
Nina Lukashina, Alisa Alenicheva, Elizaveta Vlasova, Artem Kondiukov, Aigul Khakimova, Emil Magerramov, Nikita Churikov, Aleksei Shpilman
Automatic generation of reviews of scientific papers
December 2020
Anna Nikiforovskaya, Nikolai Kapralov, Anna Vlasova, Oleg Shpynov, Aleksei Shpilman
Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously
July 2020
Mikita Sazanovich, Konstantin Chaika, Kirill Krinkin, Aleksei Shpilman
Using Large-Scale Anomaly Detection on Code to Improve Kotlin Compiler
June 2020
Timofey Bryksin, Victor Petukhov, Ilya Alexin, Stanislav Prikhodko, Alexey Shpilman, Vladimir Kovalenko and Nikita Povarov
Artificial Intelligence for Prosthetics-challenge solutions
November 2019
Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty, Jennifer Hicks, Sean F Carroll, Bo Zhou, Hongsheng Zeng, Fan Wang, Rongzhong Lian, Hao Tian, Wojciech Jaśkowski, Garrett Andersen, Odd Rune Lykkebø, Nihat Engin Toklu, Pranav Shyam, Rupesh Kumar Srivastava, Sergey Kolesnikov, Oleksii Hrinchuk, Anton Pechenko, Mattias Ljungström, Zhen Wang, Xu Hu, Zehong Hu, Minghui Qiu, Jun Huang, Aleksei Shpilman, Ivan Sosin, Oleg Svidchenko, Aleksandra Malysheva, Daniel Kudenko, Lance Rane, Aditya Bhatt, Zhengfei Wang, Penghui Qi, Zeyang Yu, Peng Peng, Quan Yuan, Wenxin Li, Yunsheng Tian, Ruihan Yang, Pingchuan Ma, Shauharda Khadka, Somdeb Majumdar, Zach Dwiel, Yinyin Liu, Evren Tumer, Jeremy Watson, Marcel Salathé, Sergey Levine, Scott Delp
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning
October 2019
Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman
End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box
October 2019
Vladislav Belyaev, Aleksandra Malysheva, Aleksei Shpilman
Automatic Classification of Error Types in Solutions to Programming Assignments at Online Learning Platform
June 2019
Artyom Lobanov, Timofey Bryksin, and Alexey Shpilman
A comparative evaluation of machine learning methods for robot navigation through human crowds
December 2018
A. Gaydashenko, D. Kudenko, A. Shpilman
Framework for Deep Reinforcement Learning with GPU-CPU Multiprocessing
December 2018
Ivan Sosin, Oleg Svidchenko, Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman
Deep Multi-Agent Reinforcement Learning with Relevance Graphs
December 2018
Aleksandra Malysheva, Tegg Taekyong Sung, Chae-Bong Sohn, Daniel Kudenko, Aleksei Shpilman
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data
November 2018
A. Malysheva, D. Kudenko, A. Shpilman
Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation
November 2018
I. Sosin, D. Kudenko, A. Shpilman
Automatic Recommendation of Move Method Refactorings Using Clustering Ensembles
September 2018
Timofey Bryksin, Evgenii Novozhilov, and Aleksei Shpilman
Learning to Run with Reward Shaping from Video Data
July 2018
A. Malysheva, A. Shpilman, D. Kudenko
Automated Refactoring of Object-Oriented Code Using Clustering Ensembles
June 2018
Timofey Bryskin, Alexey Shpilman, and Daniel Kudenko
Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks
December 2017
A. Shpilman, D. Boikiy, M. Plyakova, D. Kudenko, A. Burakov, E. Nadezhdina
Dependence of stress granule formation on microtubules supports the idea of stress granule specific “glue” arising in stress conditions
2014
Shpilman A., Shudinova E., Ivanov P., Nadezhdina E.
Using Quazi-3D Approach for Microtubule Self-Organization Model
2014
Primako E.M., Shpilman A.A.
Investigation on Dependence of Stress Granules Formation on Microtubule Network Parameters and Stress Agent Concentration Through Numerical Model
2014
Shpilman A.A., Chudinova E.V., Lubitelev A., Ivanov P.A. Nadezhdina E.S.
Role of Microtubules in Formation of Stress Granules
2013
Chudinova E.M., Shpilman A.A., Lubitelev A.V., Ivanov P.A., Nadezhdina E.S.
Molecules that Organize Cell Microtubule Systems and Why are They Doing It
2012
Brodsky I.B., Burakov A.V., Zhapparova O.N., Ivanov P.A., Fokin A.I., Chudinova E.M., Shanina A.N., Shpilman A.A., Nadezhdina E.S.
Microtubules govern stress granule mobility and dynamics, Biochimia Biophysica Acta
2010
Nadezhdina E.S., Lomakin A.J., Shpilman A.A., Chudinova E.M., Ivanov P.A.
Simple Computer Model Shows Posibility of Non-Motor Microtubule Binded Gradient Transport
2009
Shpilman A.
Disturbance of the Radial System of Interphase Microtubules in the Presence of Excess Serum in Cell Culture Medium
2008
E.V. Usova, A.V.Burakov, A.A. Shpilman, E.S. Nadezhdina
Imitational Modeling of Cytoskeleton: Dynamics, Differentiation, Active Transport and Membrane Interactions
2008
Shpilman A., Nadezhdina E.S.
In silico vs in vitro: Mimitation v1.0 program allows to predic morphological and dynamic changes of tubulin cytoskeleton caused by microtubule stabilization
2008
Shpilman A.A, Nadezhdina E.S.
Stochastic Modeling of Microtubule Systems
2006
Shpilman A.A.
Computational Stochastic Modeling of Cellular Microtubule Network
2006
Shpilman A.A., Nadezhdina E.S.
Stochastic Computer Model of the Cell Microtubule Dynamics
2006
А.А. Shpil’man and E.S. Nadezhdina