I am an upcoming Ph.D. student at EPFL and Oxford as a part of ELLIS, under the guidance of Prof. Mackenzie Mathis and Prof. Timothy Behrens. My research will be focused on developing methods for learning multimodal data-driven priors in neuroscience with self-supervision, and on improving the explainability of such algorithms.
Currently, I am completing my Master’s in Data Science at Ecole polytechnique fédérale de Lausanne. Additionally, I serve as a research assistant at the Adaptive Motor Control Lab, where I am working on extensions for CEBRA, led by Steffen Schneider. Also, I am improving the generalization ability of pose estimation models in DeepLabCut with Shaokai Ye.
Also, I have spent time as Quantitative research intern at hedge fund WorldQuant, working on LLMs fine-tuning on financial in-house data.
I pursued my BSc degree in Applied Mathematics and Physics at Moscow Institute of Physics and Technology. During my undergraduate studies, I worked in the Machine Intelligence Lab as AI Researcher. As a member of the Computer Vision and Signal Processing team, I conducted research and helped big technical companies to build and deploy AI models on wearable devices.
My research focuses on studying adaptation and generalization in minds and machines, utilizing mathematics, AI, and neuroscience. Specifically, I am passionate about learning data-driven priors and relationships from neural and behavioral data through AI for learning representations. My interests lie particularly in self-supervised learning, disentangled representation learning, generative models, and Bayesian inference. More details about my experience in my resume.
MSc in Data Science, 2021 - 2023 (expected)
Ecole polytechnique fédérale de Lausanne
BSc in Applied Math and Physics, 2017 - 2021
Moscow Institute of Physics and Technology