Aleksei Shpilman
- Senior Lecturer:HSE Campus in St. Petersburg / St. Petersburg School of Physics, Mathematics, and Computer Science / Department of Informatics
- Center Director:HSE Campus in St. Petersburg / St. Petersburg School of Physics, Mathematics, and Computer Science / Centre for Data Analysis and Machine Learning
- Programme Academic Supervisor:Machine Learning and Data Analysis
- Aleksei Shpilman has been at HSE University since 2018.
Education and Degrees
- 2021
Candidate of Sciences* (PhD)
Saint Petersburg State University - 2010
Degree in Bioengineering and Bioinformatics
Lomonosov Moscow State University
According to the International Standard Classification of Education (ISCED) 2011, Candidate of Sciences belongs to ISCED level 8 - "doctoral or equivalent", together with PhD, DPhil, D.Lit, D.Sc, LL.D, Doctorate or similar. Candidate of Sciences allows its holders to reach the level of the Associate Professor.
Courses (2023/2024)
- Machine Learning (Bachelor’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 3 year, 2 module)Rus
Machine Learning (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; field of study "01.04.02. Прикладная математика и информатика", field of study "01.04.02. Прикладная математика и информатика"; 1 year, 2, 3 module)Rus
- Mentor's Seminar (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 1-4 module)Rus
- Mentor's Seminar (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 2 year, 1-3 module)Rus
- Past Courses
Courses (2022/2023)
- Deep Learning (Bachelor’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 3 year, 3, 4 module)Rus
- Deep Learning (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 3, 4 module)Eng
- Machine Learning (Bachelor’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 3 year, 2 module)Eng
- Machine Learning (Mago-Lego; 2, 3 module)Rus
Machine Learning (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; field of study "01.04.02. Прикладная математика и информатика", field of study "01.04.02. Прикладная математика и информатика"; 1 year, 2, 3 module)Rus
Mentor's Seminar (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; field of study "01.04.02. Прикладная математика и информатика", field of study "01.04.02. Прикладная математика и информатика"; 1 year, 1-4 module)Rus
- Research seminar "Technologies of machine learning" (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 4 module)Rus
Technological Workshop (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; field of study "01.04.02. Прикладная математика и информатика", field of study "01.04.02. Прикладная математика и информатика"; 2 year, 3 module)Rus
- Web Searching and Ranging (Bachelor’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 4 year, 1, 2 module)Eng
Courses (2021/2022)
- Additional Machine Learning Chapters (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 1-4 module)Rus
- Machine Learning (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 2 year, 1, 2 module)Eng
- Machine Learning (Bachelor’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 3 year, 2 module)Eng
- Machine Learning (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 2-4 module)Eng
- Machine Learning (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 3 module)Rus
- Web Searching and Ranging (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 2 year, 1-4 module)Rus
Courses (2020/2021)
- Artificial Intelligence and Cognitive Systems (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 2 year, 1, 2 module)Rus
- Computational Neuroscience (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 2 year, 1, 2 module)Rus
- Distributed Processing and Big Data Analysis (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 2 year, 2, 3 module)Eng
Driverless Cars (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; field of study "01.04.02. Прикладная математика и информатика", field of study "01.04.02. Прикладная математика и информатика"; 2 year, 1, 2 module)Rus
- Image and Text Analysis (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 2 year, 1, 2 module)Rus
- Machine Learning (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 2-4 module)Eng
- Modern Methods of Data Analysis (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 2 year, 1, 2 module)Rus
- Modern Methods of Data Analysis (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 1-4 module)Rus
- Modern Methods of Decision Making (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 1-4 module)Rus
- Modern Methods of Decision Making (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 3, 4 module)Rus
- Research Seminar "Machine Learning and Applications" (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 1-4 module)Rus
- Topological data analysis (Optional course (faculty); St. Petersburg School of Physics, Mathematics, and Computer Science ; 1, 2 module)Rus
- Web Searching and Ranging (Bachelor’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 4 year, 1 module)Eng
- Web Searching and Ranging (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 2 year, 2-4 module)Rus
Courses (2019/2020)
Artificial Intelligence and Cognitive Systems (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; field of study "01.04.02. Прикладная математика и информатика", field of study "01.04.02. Прикладная математика и информатика"; 2 year, 1, 2 module)Rus
- Big Data Applications: Real-Time Streaming (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 4 module)Rus
- Big Data Processing (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 4 module)Eng
- Computational Statistics (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 2, 3 module)Rus
- Deep Learning (Bachelor’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 4 year, 1, 2 module)Eng
- Deep Reinforcement Learning (Bachelor’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 4 year, 3 module)Eng
- Distributed Processing and Big Data Analysis (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 2 year, 3 module)Rus
- Effective software development (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 1, 2, 4 module)Rus
- Image Analysis (Bachelor’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 4 year, 2, 3 module)Rus
- Large-Scale Machine Learning (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 3, 4 module)Rus
- Machine Learning (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 2-4 module)Eng
- Machine Learning (Bachelor’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 3 year, 2 module)Eng
- Project Seminar (Master’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 1 year, 2, 4 module)Rus
- Software Development (Bachelor’s programme; St. Petersburg School of Physics, Mathematics, and Computer Science ; 3 year, 1, 3, 4 module)Rus
Courses (2018/2019)
Publications33
- Chapter Vladimir Egorov, Alexei Shpilman. Scalable Multi-Agent Model-Based Reinforcement Learning, in: AAMAS '22: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems. International Foundation for Autonomous Agents and Multiagent Systems, 2022. P. 381-390.
- Article Lukashina N., Kartysheva E., Spjuth O., Elizaveta Virko, Shpilman A. SimVec: predicting polypharmacy side effects for new drugs // Journal of Cheminformatics. 2022. Vol. 14. Article 49. doi
- Chapter Eichenberger C., Neun M., Martin H., Herruzo P., Spanring M., Lu Y., Choi S., Konyakhin V., Lukashina N., Shpilman A., Wiedemann N., Raubal M., Wang B., Vu H. L., Mohajerpoor R., Cai C., Kim I., Hermes L., Melnik A., Veliogl R., Vieth M., Schilling M., Bojesomo A., Marzouqi H. A., Liatsis P., Santokhi J., Hillier D., Yang Y., Sarwar J., Jordan A., Hewage E., Jonietz D., Tang F., Gruca A., Kopp M., Kreil D., Hochreiter S. Traffic4cast at NeurIPS 2021 - Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes, in: Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track. PMLR, 2022. P. 97-112.
- Chapter Ivanov D., Egorov V., Shpilman A. Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive Environments, in: AAMAS'2021: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems. IFAAMAS, 2021. P. 1536-1538.
- Chapter Laurent F., Schneider M., Scheller C., Watson J., Li J., Chen Z., Zheng Y., Chan S., Махнев К. И., Svidchenko O., Егоров В. С., Ivanov D., Shpilman A., Spirovska E., Tanevski O., Nikov A., Grunder R., Galevski D., Mitrovski J., Sartoretti G., Luo Z., Damani M., Bhattacharya N., Agarwal S., Egli A., Nygren E., Mohanty S. Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World, in: Proceedings of Machine Learning Research Vol. 133: Proceedings of the NeurIPS 2020: Competition and Demonstration Track. PMLR, 2021. P. 275-301.
- Chapter Svidchenko O., Shpilman A. Maximum Entropy Model-based Reinforcement Learning, in: NeurIPS'2021 Deep Reinforcement Learning Workshop. , 2021.
- Chapter Ivanov D., Пшихачев Г. А., Егоров В. С., Shpilman A. Self-Imitation Learning from Demonstrations, in: NeurIPS'2021 Deep Reinforcement Learning Workshop. , 2021.
- Chapter Зенкова Н. В., Sedykh E., Shugaeva T., V S., Ermak T., Shpilman A. Simple End-to-end Deep Learning Model for CDR-H3 Loop Structure Prediction, in: NeurIPS'2021 Machine Learning for Structural Biology Workshop. , 2021.
- Chapter V K., Lukashina N., Shpilman A. Solving Traffic4Cast Competition with U-Net and Temporal Domain Adaptation, in: NeurIPS 2021 Traffic4cast Competition. , 2021.
- Preprint Vsevolod K., Lukashina N., Shpilman A. Solving Traffic4Cast Competition with U-Net and Temporal Domain Adaptation / Cornell University. Series Computer Science "arxiv.org". 2021.
- Chapter Sazanovich M., Nikolskaya A., Belousov Y., Shpilman A. Solving black-box optimization challenge via learning search space partition for local bayesian optimization, in: Proceedings of Machine Learning Research Vol. 133: Proceedings of the NeurIPS 2020: Competition and Demonstration Track. PMLR, 2021. P. 77-85.
- Chapter Shpilman A., Kidzinski L., Ong C., Mohanty S. P., Hicks J., Carroll S., Zhou B., Zeng H., Wang F., Lian R., Tian H., Jaskowski W., Garrett A., Lykkebo O. R., Toklu N. E., Shyam P., Srivastava R. K., Kolesnikov S., Hrinchuk O., Pechenko A., Mattias L., Wang Z., Hu X., Hu Z., Qiu M., Huang J., Sosin I., Svidchenko O., Malysheva A., Kudenko D., Rane L., Bhatt A., Wang Z., Qi P., Yu Z., Peng P., Yuan Q., Li W., Tian Y., Yang R., Ma P., Khadka S., Majumdar S., Dwiel Z., Liu Y., Tumer E., Watson J., Salathe M., Levine S., Delp S. Artificial Intelligence for Prosthetics: Challenge Solutions, in: The NeurIPS '18 Competition: From Machine Learning to Intelligent Conversations. Springer, 2020. doi P. 69-128. doi
- Chapter Kidziński Ł., Ong C., Mohanty S. P., Hicks J., Carroll S., Zhou B., Zeng H., Wang F., Lian R., Tian H., Jaśkowski W., Andersen G., Lykkebø O. R., Toklu N. E., Shyam P., Srivastava R. K., Kolesnikov S., Hrinchuk O., Pechenko A., Ljungström M., Wang Z., Hu X., Hu Z., Qiu M., Huang J., Shpilman A., Sosin I., Svidchenko O., Малышева А. И., Kudenko D., Rane L., Bhatt A., Wang Z., Qi P., Yu Z., Peng P., Yuan Q., Li W., Tian Y., Yang R., Pingchuan M., Khadka S., Majumdar S., Dwiel Z., Liu Y., Tumer E., Watson J., Salathé M., Levine S., Delp S. Artificial Intelligence for Prosthetics: Challenge Solutions, in: The NeurIPS '18 Competition: From Machine Learning to Intelligent Conversations. Springer, 2020. doi P. 69-128.
- Chapter Nikiforovskaya A., Kapralov N., Vlasova A., Shpynov O., Shpilman A. Automatic generation of reviews of scientific papers, in: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA 2020). Miami : IEEE, 2020. P. 314-319. doi
- Chapter Mikita Sazanovich, Chaika K., Krinkin K., Shpilman A. Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously, in: Workshop on AI for Autonomous Driving (AIAD). , 2020.
- Chapter Lukashina N., Аленичева А. Р., Vlasova E., Kondiukov A., Khakimova A., Magerramov E., Churikov N., Shpilman A. Lipophilicity Prediction with Multitask Learning and Molecular Substructures Representation, in: Machine Learning for Molecules at NeurIPS'2020. , 2020.
- Chapter Bryksin T., Petukhov V., Alexin I., Prikhodko S., Shpilman A., Kovalenko V., Povarov N. Using Large-Scale Anomaly Detection on Code to Improve Kotlin Compiler, in: MSR '20: Proceedings of the 17th International Conference on Mining Software Repositories. ACM Press, 2020. P. 455-465. doi
- Chapter Shpilman A., Malysheva A., Belyaev V. End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box, in: Proceedings of 2019 XVI International Symposium "Problems of Redundancy in Information and Control Systems" (REDUNDANCY). IEEE, 2019. P. 165-170. doi
- Chapter Shpilman A., Malysheva A., Kudenko D. MAGNet: Multi-Agent Graph Network for Deep Multi-Agent Reinforcement Learning, in: Proceedings of 2019 XVI International Symposium "Problems of Redundancy in Information and Control Systems" (REDUNDANCY). IEEE, 2019. P. 171-176. doi
- Chapter Shpilman A., Malysheva A., Kudenko D. MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning, in: Adaptive and Learning Agents Workshop at International Joint Conference on Autonomous Agents and Multiagent Systems. , 2019. P. 1-8.
- Chapter Омельченко А. В., Шпильман А. А., Москвин Д. Н., Храбров А. И. Особенности построения образовательных программ в области анализа данных в финансах // В кн.: Сборник научных трудов Санкт-Петербургской конференции исследователей в сфере экономики, бизнеса и общества: итоги 2019 года / Под общ. ред.: Е. М. Рогова, Ю. А. Тарасова, Е. А. Шакина. СПб. : Национальный исследовательский университет "Высшая школа экономики", 2019. С. 14-14.
- Chapter Shpilman A., Kudenko D., Gaydashenko A. A Comparative Evaluation of Machine Learning Methods for Robot Navigation Through Human Crowds, in: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018. P. 553-557. doi
- Chapter Bryksin T., Shpilman A., Kudenko D. Automated Refactoring of Object-Oriented Code Using Clustering Ensembles, in: Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence. , 2018. P. 754-757.
- Chapter Shpilman A., Sosin I., Kudenko D. Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation, in: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2018. P. 1436-1441. doi
- Chapter Shpilman A., Malysheva A., Sung T. T., Sohn C., Kudenko D. Deep Multi-Agent Reinforcement Learning with Relevance Graphs, in: Deep RL Workshop NeurIPS 2018. , 2018. P. 1-10.
- Chapter Shpilman A., Malysheva A., Kudenko D. Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data, in: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2018. P. 286-291. doi
- Chapter Malysheva A., Shpilman A., Kudenko D. Learning to Run with Reward Shaping from Video Data, in: ALA 2018 - Workshop at the Federated AI Meeting 2018. ALA, 2018. P. 1-7.
- Chapter Shpilman A., Boikiy D., Polyakova M., Kudenko D., Burakov A., Nadezhdina E. Deep Learning of Cell Classification Using Microscope Images of Intracellular Microtubule Networks, in: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2017. doi P. 1-6. doi
- Article Usova E., Burakov A., Shpilman A.A. Disturbance of the radial system of interphase microtubules in the presence of excess serum in cell culture medium // Biofizika. 2010. Vol. 53. No. 6. P. 523-526. doi
- Article Alexey A. Shpilman, Nadezhdina E. S., Lomakin A. J., Chudinova E. M., Ivanov P. A. Microtubules govern stress granule mobility and dynamics // Biochimica et Biophysica Acta - Molecular Cell Research. 2010. Vol. 1803. No. 3. P. 361-371. doi
- Article Shpil'man A.A., Nadezhdina E. Stochastic computer model of the cell microtubule dynamics // Biofizika. 2006. Vol. 51. No. 5. P. 880-884. doi
Employment history
2020-now Gazprom neft, Head of AI development programs
2018-now HSE University, Director of center of data analysis and machine learning
2015-now JetBrains Research, Head of AI Labs
2015-2018 Academic University, senior lecturer
2008-2015 Yandex, Researcher-software developer
2008-2015 MIPT, Lecturer
2008-2013 Lomonosov MSU, Junior lecturer
'If You've Completed a Hard Route, It Doesn't Mean You Should Give Up Your Training'
On February 8, HSE University-St Petersburg celebrates the Day of Russian Science. We congratulate all researchers and wish them continued openness to new discoveries. To mark the occasion, we talked to scientists about the sources of their inspiration, moments of anxiety, and having fun.
What Awaits Participants of the Winter School 2022
Next weekend, February 19 and 20, HSE – St. Petersburg will host an annual event for those who plan to enrol in the campus master's programmes – the Winter School. Participants will be able to get acquainted with educational opportunities and attend open lectures by famous speakers – Pavel Barakaev, Sergey Mardanov, Vladimir Knyaginin, and others. In this article, we will tell you what has been included in the Winter School 2022.
'I Would Certainly Be Interested in Attending a Summer Course at HSE in the Future!'
On August 21st, Saint Petersburg HSE Summer School, which has been taking place since 2017, came to a close. This year it moved online due to the pandemic. 44 participants from 18 countries, including France, Ireland, the USA, the Philippines, China, and others were joining Zoom during four weeks. The organisers of the Summer School were happy to welcome students of 15 HSE partner universities such as Università Cattolica del Sacro Cuore, King’s College London, Sciences Po Lyon, University of Basel, Georg-August-Universität Göttingen, and others.