Yubei Chen

Assistant Professor
Electrical and Computer Engineering Department, University of California, Davis
Room 3139, Kemper Hall, One Shields Avenue, Davis, CA 95616

I am an assistant professor in the ECE Department at University of California, Davis. Prior to joining UC Davis, I did my postdoc study with Prof. Yann LeCun at NYU Center for Data Science (CDS) and Meta Fundamental AI Research (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.

I am actively looking for Graduate/Undergraduate students interested in unsupervised learning, generative models, and world models. Please email me your CV if you are interested in working with me.

news

Nov20, 2023	Unsupervised learning of structured representations via closed-loop transcription, is accepted at Conference on Parsimony and Learning (CPAL)! Paper
Nov14, 2023	Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning, is accepted at Transactions on Machine Learning Research (TMLR)! Paper
Jun21, 2023	I have given an open lecture on unsupervised representation learning at TechBeat.net.
Mar22, 2023	I will join the ECE department at UC Davis as an assistant professor in late 2023.
Mar21, 2023	We got ICLR 2023 Outstanding Paper Honorable Mention Award! Paper
Jan21, 2023	Three papers are accepted at ICLR 2023 with one Oral and one Spotlight! 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.
Nov18, 2022	I gave a talk at University of Washington NeuroAI Seminar on the principles of unsupervised representation learning.
Nov4, 2022	Simple Emergent Action Representations from Multi-Task Policy Training, is accepted to Deep RL Workshop@NeurIPS 2022! arXiv, Website
Oct21, 2022	Disentangling images with Lie group transformations and sparse coding, is accepted to NeurReps@NeurIPS 2022 as a full PMLR paper! arXiv, GitHub