Homepage of Professor Yi Ma

University of California, Berkeley Berkeley Seal

Department of Electrical Engineering and Computer Sciences

Electrical Engineering and Computer Sciences
University of California, Berkeley

Office:    EECS Department, 333A Cory Hall # 1770
              University of California, Berkeley, CA 94720-1770, USA.
Email: yima@eecs.berkeley.edu
Phone:   +1 (510) 664 4565

Other Affiliations
Berkeley Artificial Intelligence Research (BAIR)
Berkeley Center for Augmented Cognition (CRC)
Berkeley FHL Vive Center for Enhanced Reality
Malong Technologies

Research

Publications

Talks

Students

Teaching

Services

Biography

Honors

CV.pdf

News:

A New Textbook: High-Dimensional Data Analysis with Low-Dimensional Models (pre-production version).
A New Manuscript: ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction.
Fall 2021 Course EECS208: Computational Principles for High-Dimensional Data Analysis (syllabus.pdf).

Events:

Plenary Talk at the IEEE International Conference on Acoutstics, Speech, and Signal Processing (ICASSP), Singapore, May 22-27, 2022.
Keynote Speech at the International School on Deep Learning (DeepLearn 2022), United Kingdom, January 17-21, 2022.
Invited Talk at Department of Mathematics, RWTH Aachen University, Germany, August 23, 2021.
Keynote Speech at the International Conference on Image, Vision and Computing, Qingdao, China, July 23-25, 2021.
Invited Talk at MILA, University of Montreal, Quebec, July 16, 2021.
Keynote Speech at the Annual Meeting of Chinese Society of Industrial and Applied Mathematics (CSIAM), China, July 11, 2021.
Invited Talk at the Mathematics of Deep Learning program, Isaac Newton Institute, University of Cambridge, 2021.
Keynote Speech at CVPR21 Workshop on Neural Architecture Search, June 25, 2021.
Invited Talk at CVPR21 Workshop on 3D Scene Understanding, June 19, 2021.
Invited Talk at Seminar on Mathematics of Deep Learning, Flatiron Institute, Simons Foundation, June 15, 2021.
Keynote Speech (Bilibili Video in Chinese) at Beijing Academy of Artificial Intelligence (BAAI) Conference, June 2, 2021.
Invited Talk (Tecent Video in Chinese) at the AIR Seminar of the Institute for AI Industrial Research, Tsinghua University, May 27, 2021.
Keynote Speech at the Annual Meeting of the AI Institute of Harbin Institute of Technology, China, May 22, 2021.
Invited Talk at the MAD Seminar of Center for Data Science and Courant Institute, NYU, May 14, 2021.
Invited Talk at the Data Science Institute (DSI) Seminar at Lawrence Livermore National Laboratory, May 10, 2021.
Invited Talk at Qualcomm Research, April 29, 2021.
Invited Talk (YouTube video) at the Communications and Signal Processing Seminar of EECS Dept., University of Michigan, April 29, 2021.
Invited Talk (YouTube video) at the Math-Science Literature Lecture Series, Center of Mathematical Sciences and Applications, Harvard University, April 16, 2021.
Invited talk at MADD seminar, Institute of Data Science, UC Davis, April 13, 2021.
Invited Talk at the Technion Computer Vison Colloquium, the Pixel Club of Technion, Israel Institute of Technology, April 6, 2021.
Invited Talk (YouTube video) at One World MINDS Seminar, April 1st, 2021.
Deep Networks from First Principles, talk at Redwood Center for Theoretical Neuroscience, UC Berkeley, March 12, 2021.
Deep Networks from First Principles, talk at Semiautonomous Seminar, UC Berkeley, February 19, 2021.
Overview of Reinforcement Learning and Optimal Control, and YouTube video, February 17, 2021.
Keynote Speech at Computational Imaging Conference, January 21, 2021.
Invited talk at HTC and VIA, January 19, 2021.
Invited talk at CSMl and EE Seminar, Princeton University, January 15, 2021.
Berkeley Assured Autonomy Seminar (TBD, Spring 2021).
Berkeley Learning Theory Study Group (Friday noon, Spring 2021).

Project Websites:

Deep Networks from the Principle of Rate Reduction.
Pursuit of Large-Scale 3D Structures and Geometry.
UIUC/MSRA: Low-Rank Matrix Recovery via Convex Optimization (with Wright, Lin and Candes et. al.).
UIUC: Face Recognition via Sparse Representation (with Wright, Ganesh, Yang, Zhou and Wagner et. al.).
UIUC: Clustering and Classification via Lossy Compression (with Wright Yang, Mobahi, and Rao et. al.).
UIUC: Generalized Principal Component Analysis (with Huang and Vidal).