Yubei Chen

Postdoctoral Associate (Incoming Assistant Professor, ECE @ UC Davis)
Center for Data Science, New York University
Postdoctoral Associate, Advisor Professor Yann LeCun

I am a postdoctoral associate at NYU Center for Data Science (CDS) and Meta Fundamental AI Research (FAIR), working with Prof. Yann LeCun. Prior to joining CDS and FAIR, I completed my Ph.D. at Redwood Center for Theoretical Neuroscience and Berkeley AI Research (BAIR), UC Berkeley, advised by Prof. Bruno Olshausen. Undergraduate studies from Tsinghua University, Beijing. Co-founded Aizip Inc. that builds robust, efficient, and scalable AI-IoT solutions.

My research is at the intersection of computational neuroscience and deep unsupervised (self-supervised) learning, enhancing our understanding of the computational principles governing unsupervised representation learning in both brains and machines, and reshaping our insights into natural signal statistics.


Mar22, 2023
I will join the ECE department at UC Davis as an assistant professor in late 2023. :wink:
Mar21, 2023
Jan21, 2023
Three papers are accepted at ICLR 2023 with one Oral and one Spotlight! :sparkle: 1) Oral: On the duality between contrastive and non-contrastive self-supervised learning arXiv. 2) Spotlight: Minimalistic unsupervised learning with the sparse manifold transform arXiv. 3) Simple Emergent Action Representations from Multi-task Policy Training arXiv.
Jan11, 2023
I gave talk at Center for Computational Neuroscience (CCN) Research Seminar, Flatiron Institute on the principles of unsupervised representation learning. :coffee:
Nov18, 2022
I gave a talk at University of Washington NeuroAI Seminar on the principles of unsupervised representation learning. :coffee:
Nov4, 2022
Simple Emergent Action Representations from Multi-Task Policy Training, is accepted to Deep RL Workshop@NeurIPS 2022! :sparkle: arXiv, Website
Oct21, 2022
Disentangling images with Lie group transformations and sparse coding, is accepted to NeurReps@NeurIPS 2022 as a full PMLR paper! :sparkle: arXiv, GitHub
Sep30, 2022 New Paper: Minimalistic Unsupervised Learning with the Sparse Manifold Transform. :wink: arXiv
Sep9, 2022
I gave a talk at Bay Area Vision Research Day (BAVRD) on the principles of unsupervised representation learning. :coffee:
Aug16, 2022
Compact and Optimal Deep Learning with Recurrent Parameter Generators, is accepted to WACV 2022! :sparkle: arXiv