I am a Postdoctoral Researcher in the Computer Vision Group at the University of Bern. I earned my Ph.D. in Computer Science from the University of Bern in 2024, where I was supervised by Prof. Dr. Paolo Favaro. Prior to that, I completed a Specialist degree (equivalent to B.S. + M.S.) in Fundamental Mathematics and Mechanics at MSU in 2020. Additionally, I graduated from YSDA in 2018. My research interests include Machine Learning, Computer Vision, Generative AI, and World Models.
A method that straightens sampling trajectories in the flow matching framework via storing and exchanging locally optimal data-noise couplings across minibatches.
A multi-modal and multi-domain ego-vision world model with precise control over object dynamics, ego-agent motion and human poses.
A model to compose and animate scenes from sparse sets of visual features.
A model to animate single frames with sparse motion control.
Single image to novel view synthesis without any supervision.
Conditioning only on a few randomly chosen past frames at each denoising step of flow matching results into a more efficient training procedure.
A model to discover agents' action spaces from a dataset of videos in an unsupervised way. The action spaces are decomposed into global (2D shifts) and local (discrete) actions.
A neural network optimization algorithm based on Kalman filtering.