I work on sequential decision-making and representation learning for structured data, such as point clouds or graphs.
Research
The main question that motivates my research is: How can we actively learn new complex environments?
My interests span multiple topics around sequential decision-making, bayesian optimization, representation learning, generative modeling.
I am sparked by designing robust algorithms with quantified uncertainty and well-understood limitations (theoretically and empirically), that would be applicable in society-critical areas.
Previously, I was a doctoral student in Learning and Adaptive Systems Group at ETH Zurich supervised by Andreas Krause. My dissertation focuses on Bayesian optimization, proposing methods for risk-averse and computationally effective decision-making. Prior to that, I received master’s degrees in computer science and math from MIPT and Skoltech and a bachelor’s degree in math and physics from MIPT. In my master’s, I visited Columbia University in NYC and worked on deep learning for weakly-supervised semantic segmentation supervised by Victor Lempitsky and Hod Lipson.
I did research internships at Google DeepMind (RL and RLHF for LLMs) Yandex (deep learning for precipitation nowcasting) and Amazon Web Services (Bayesian optimization for AutoML).
-
Dr. Sc., 2023
ETH Zurich
-
MSc in Computer Science, 2017
Skolkovo Institute of Science and Technology (Skoltech), Moscow
-
MSc in Applied Mathematics (with honors), 2017
Moscow Institute of Physics and Technology (Phystech), Moscow
-
BSc in Applied Math and Physics (with honors), 2015
Moscow Institute of Physics and Technology (Phystech), Moscow
News
- October 2023: Safe risk-averse BO for controller tuning (paper) accepted to IEEE Robotics and Automation Letters.
- September 2023: I successfully defended my doctoral thesis titled “Bayesian Optimization in the wild: risk-averse and computationally-effective decision-making”! Grateful to my advisor Andreas Krause, and to my brilliant collaborators!
- August 2023: New preprint on arxiv: Adversarial Causal BO.
- July 2023: New preprint on arxiv: Safe Risk-averse Bayesian Optimization for Controller Tuning.
- May 2023: Attending ICLR in Rwanda and giving a talk about Model-based Causal BO, slides and talk
- April 2023: Joining Google DeepMind, Brain team, as a research scientist intern, going to dive into RL from human feedback for LLM
- January 2023: Model-based Causal BO accepted to ICLR featured spotlight (top 25% of accepted papers)
- August 2022: Talk at Google TechTalk (Google BayesOpt Speaker Series), slides and video are available.
- August 2022: Talk at AWS ML Science Tech Presentations series, slides are available.
- Summer 2022: Our paper got the best paper award at AutoML Conf! I gave a contributed talk, recording, paper, intuitive blog post.
Publications
Experience
Research areas: Probabilistic Machine Learning, Bayesian Optimization, Deep Learning, Tensors, Computer Vision
Advisor: Prof. Andreas Krause
Worked with Hod Lipson and collaboration with Victor Lempitsky.
- Worked on deep learning based methods for weakly-supervised semantic segmentation
- Developed an efficient architecture for image-based plant disease detection
Teaching
- Head Teaching assistant: lead course reorganization, teaching and examination
- Probabilistic Artificial Intelligence at ETH, 2020 (600 students course and 30 TAs)
- Teaching assistant: create howeworks, projects, teach tutorials, supervise exam
- Probabilistic Artificial Intelligence at ETH, 2018, 2019
- Introduction to Machine Learning at ETH, 2018, 2019
- Advanced Topics in Machine Learning at ETH, 2017, 2018
- Data Mining: Learning from Large Data Sets at ETH, 2017
- Signal and Image Processing by Prof. Stamatios Lefkimmiatis at Skoltech, 2017
Supervised theses
I (co-)supervised MSc theses of several bright students, some resulting into research publications:
- Alicja Chaszczewicz: Following Gradients to Calibrate Equilibrium Reaching Simulators,
jointly with Max Paulus, ETH Zurich, December 2020 - May 2021. - Ankit Dhall: Learning Representations for Images With Hierarchical Labels (paper @DiffCVML'20),
jointly with Octavian Ganea and Dario Pavllo, ETH Zurich, March - September 2019. - Erik Daxberger: Mixed-Variable Bayesian Optimization (paper IJCAI'20),
jointly with Matteo Turchetta, ETH Zurich, October 2018 - April 2019. - Stefan Beyeler: Multi-fidelity Batch Bayesian Optimization for the Calibration of Transport System Simulations,
jointly with Matteo Turchetta, ETH Zurich, October 2017 - April 2018.