JetBrains Research

Алексей Шпильман

alexey@shpilman.com

Биография

Окончил факультет Биоинженерии и Биоинформатики МГУ им. Ломоносова.

Профессиональная деятельность

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.

Научное руководство

Руководит научными проектами бакалавров, магистров и аспирантов в НИУ ВШЭ, ИТМО, СПбГУ.

Проекты

Детекция аномалий в коде на Kotlin

Мы скачали с Гитхаба весь код на Kotlin, векторизовали его, и прогнали через алгоритм детекции аномалий. Мы нашли интересный команде компилятора Kotlin код.

Руководитель проекта: Тимофей Брыксин

Публикации

Anna Nikiforovskaya, Nikolai Kapralov, Anna Vlasova, Oleg Shpynov, Aleksei Shpilman

Automatic generation of reviews of scientific papers

ICMLA2020, Декабрь 2020
Mikita Sazanovich, Konstantin Chaika, Kirill Krinkin, Aleksei Shpilman

Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously

Workshop on AI for Autonomous Driving (AIAD), the 37th International Conference on Machine Learning (ICML2020), Июль 2020
Timofey Bryksin, Victor Petukhov, Ilya Alexin, Stanislav Prikhodko, Alexey Shpilman, Vladimir Kovalenko, Nikita Povarov

Using Large-Scale Anomaly Detection on Code to Improve Kotlin Compiler

17th International Conference on Mining Software Repositories (MSR), Июнь 2020
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

Artificial Intelligence for Prosthetics-challenge solutions

The NeurIPS '18 Competition, Ноябрь 2019
Vladislav Belyaev, Aleksandra Malysheva, Aleksei Shpilman

End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box

XVI International Symposium "Problems of Redundancy in Information and Control Systems" (REDUNDANCY), Октябрь 2019
Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman

MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning

XVI International Symposium "Problems of Redundancy in Information and Control Systems" (REDUNDANCY), Октябрь 2019
Artyom Lobanov, Timofey Bryksin, and Alexey Shpilman

Automatic Classification of Error Types in Solutions to Programming Assignments at Online Learning Platform

The 20th International Conference on Artificial Intelligence in Education (AIED), Июнь 2019
Aleksandra Malysheva, Aleksei Shpilman and Daniel Kudenko

MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning

Adaptive and Learning Agents at AAMAS, Май 2019
A. Gaydashenko, D. Kudenko, A. Shpilman

A comparative evaluation of machine learning methods for robot navigation through human crowds

ICMLA, Декабрь 2018
Aleksandra Malysheva, Tegg Taekyong Sung, Chae-Bong Sohn, Daniel Kudenko, Aleksei Shpilman

Deep Multi-Agent Reinforcement Learning with Relevance Graphs

NeurIPS Deep Reinforcement Learning Workshop, Декабрь 2018
Ivan Sosin, Oleg Svidchenko, Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman

Framework for Deep Reinforcement Learning with GPU-CPU Multiprocessing

Open Access Software, Декабрь 2018
A. Malysheva, D. Kudenko, A. Shpilman

Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data

ICARCV, Ноябрь 2018
I. Sosin, D. Kudenko, A. Shpilman

Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation

ICARCV, Ноябрь 2018
Timofey Bryksin, Evgenii Novozhilov, and Aleksei Shpilman

Automatic Recommendation of Move Method Refactorings Using Clustering Ensembles

2nd International Workshop on Refactoring (IWoR), Сентябрь 2018
A. Malysheva, A. Shpilman, D. Kudenko

Learning to Run with Reward Shaping from Video Data

Workshop on Adaptive and Learning Agents (ALA) at ICML-AAMAS, Июль 2018
T. Bryskin, A. Shpilman, D. Kudenko

Automated refactoring of object-oriented code using clustering ensembles

AAAI Workshop on Natural Language Processing for Software Engineering, Февраль 2018
A. Shpilman, D. Boikiy, M. Plyakova, D. Kudenko, A. Burakov, E. Nadezhdina

Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks

ICMLA, Декабрь 2017
Primako E.M., Shpilman A.A.

Using Quazi-3D Approach for Microtubule Self-Organization Model

V ICMBB, 2014
Shpilman A.A., Chudinova E.V., Lubitelev A., Ivanov P.A. Nadezhdina E.S.

Investigation on Dependence of Stress Granules Formation on Microtubule Network Parameters and Stress Agent Concentration Through Numerical Model

V ICMBB, 2014
Shpilman A., Shudinova E., Ivanov P., Nadezhdina E.

Dependence of stress granule formation on microtubules supports the idea of stress granule specific “glue” arising in stress conditions

54th Annual Meeting of the American Society for Cell Biology, 2014
Chudinova E.M., Shpilman A.A., Lubitelev A.V., Ivanov P.A., Nadezhdina E.S.

Role of Microtubules in Formation of Stress Granules

AIPRC, 2013
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.

Molecules that Organize Cell Microtubule Systems and Why are They Doing It

III SCBM, 2012
Nadezhdina E.S., Lomakin A.J., Shpilman A.A., Chudinova E.M., Ivanov P.A.

Microtubules govern stress granule mobility and dynamics, Biochimia Biophysica Acta

Biochimia Biophysica Acta, 2010
Shpilman A.

Simple Computer Model Shows Posibility of Non-Motor Microtubule Binded Gradient Transport

49th Annual Meeting of the American Society for Cell Biology, 2009
Shpilman A., Nadezhdina E.S.

Imitational Modeling of Cytoskeleton: Dynamics, Differentiation, Active Transport and Membrane Interactions

48th Annual Meeting of the American Society for Cell Biology, 2008
Shpilman A.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

Biological Motility: Achievements and Perspectives, 2008
E.V. Usova, A.V.Burakov, A.A. Shpilman, E.S. Nadezhdina

Disturbance of the Radial System of Interphase Microtubules in the Presence of Excess Serum in Cell Culture Medium

Biophysics, 2008
Shpilman A.A.

Stochastic Modeling of Microtubule Systems

I ICMBB, 2006
А.А. Shpil’man and E.S. Nadezhdina

Stochastic Computer Model of the Cell Microtubule Dynamics

Biophysics, 2006
Shpilman A.A., Nadezhdina E.S.

Computational Stochastic Modeling of Cellular Microtubule Network

46th Annual Meeting of the American Society for Cell Biology, 2006

Лаборатория машинного обучения и организации информации Консультант
Методы машинного обучения в области программной инженерии Консультант
Прикладное машинное обучение и глубинное обучение Руководитель лаборатории
Агентные системы и обучение с подкреплением Руководитель лаборатории