About

I’m an n-th year PhD student in Data Science at New York University advised by Yann LeCun and Kyunghyun Cho. I’m also currently a visiting researcher at Meta AI Research. I’m broadly interested in representation learning, world models, planning, reinforcement learning, and autonomous vehicles.
I’m on the job market!

Before, I completed my undergrad at the University of Warsaw in Poland. I also have extensive industry experience from doing internships at at Google, Nvidia and Jane Street.

Publications

Ordered chronologically:

X-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs
Vlad Sobal, Mark Ibrahim, Randall Balestriero, Vivien Cabannes, Diane Bouchacourt, Pietro Astolfi, Kyunghyun Cho, Yann LeCun
(Submitted to ICLR 2025)
Hierarchical World Models as Visual Whole-Body Humanoid Controllers
Nicklas Hansen, Jyothir S V, Vlad Sobal, Yann LeCun, Xiaolong Wang, Hao Su
(Submitted to ICLR 2025)
Gradient-based Planning with World Models
Jyothir S V, Siddhartha Jalagam, Yann LeCun, Vlad Sobal
(Generative Models for Decision Making Workshop at ICLR 2024)
A Cookbook of Self-Supervised Learning
Joint work with many other collaborators, contributed the section on RL
Light-weight probing of unsupervised representations for Reinforcement Learning
Wancong Zhang, Anthony GX-Chen, Vlad Sobal, Yann LeCun, Nicolas Carion’ (Reinforcement Learning Conference 2024)
Joint Embedding Predictive Architectures Focus on Slow Features
Vlad Sobal, Jyothir S V, Siddhartha Jalagam, Nicolas Carion, Kyunghyun Cho, Yann LeCun
(SSL Theory and Practice Workshop at NeurIPS 2022)
Separating the World and Ego Models for Self-Driving
Vlad Sobal, Alfredo Canziani, Nicolas Carion, Kyunghyun Cho, Yann LeCun
(Generalizable Policy Learning in the Physical World Workshop at ICLR 2022)
TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning
Jiachen Zhu, Rafael M. Moraes, Serkan Karakulak, Vlad Sobal, Alfredo Canziani, Yann LeCun