Research of Professor Yi Ma

Areas of Interest:

My main research interest is in finding the most pertinent mathematical principles for analyzing and understanding high-dimensional sensorial data such as images, so that machines and human can make more intelligent decisions. The research endeavor has led me touch upon the following areas.

Perception: Computer Vision	Inference: Mathematics & Statistics	Decision & Control
- 3D Vision & Multiple-View Geometry - Image & Video Segmentation - Robust Face/Object Recognition	- Low-dim Models and Deep Networks - Data Clustering, Classification, and Compression - Scalable Optimization Algorithms	- Active Vision Based Control - Autonomous and Intelligent Robots - Optimal Control and Reinforcement Learning

Projects:

Hong Kong University: Integrated Intelligent Systems.

Berkeley: Whitebox Transformers via Sparse Rate Reduction (with Yaodong Yu et. al.).
Berkeley: Deep Networks from the Principle of Rate Reduction (with Yaodong Yu, Ryan Chan, Haozhi Qi, and Chong You).
Berkeley: General In-hand Object Rotation with Vision and Touch (with Haozhi Qi, Brent Yi, Jitendra Malik, etc.)
Berkeley: Dexterous Robot Hand Manipulation (with Haozhi Qi, Roberto Calandra, and Jitendra Malik).
Berkeley: Canonical Factors for Hybrid Neural Fields (with Brent Yi, Weijia Zeng, and Sam Buchanan).
Berkeley: Pursuit of Large-Scale 3D Structures and Geometry (with Yichao Zhou and Xili Dai).
UIUC/MSRA: Low-Rank Matrix Recovery via Convex Optimization (with Wright, Ganesh, 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, Vidal, and Sastry).
UIUC/Berkeley: Multiple-View Geometry and 3D Reconstruction (with Kosecka, Soatto, and Sastry).

Funded Projects:

Federal:

Mathematical and Scientific Foundations of Deep Learning (sponsored by NSF and Simons Foundation).
Leveraging Egocentric and Allocentric Representations for Navigation (LEARN) (sponored by ONR).
A Holistic Approach to Reconstructing Urban Scenes (sponsored by NSF-IIS).
Scene and Task Aware Knowledge Enhanced Compressive Sensing, DARPA KECoM.
Advances in the Theory and Practice of Low-Rank Matrix Recovery and Modeling (with Emmanuel Candes, sponsored by NSF-CIF).
Harnessing Sparse and Low-Dimensional Structures for Robust Clustering of Visual Data (sponsored by ONR).
SGER: Explorations of Robust Image Classification (sponsored by NSF-IIS).
PHYSNET: Physical Interaction Using the Internet (sponsored by NSF-HCC).
Control and Sensing under Limited Information (sponsored by NSF-ECS).
Estimation of Hybrid Models as Algebraic Sets (sponsored by NSF-CCF).
Estimation of Hybrid Models in Computer Vision (sponsored by ONR).
Identification of Hybrid Dynamical Systems (sponsored by NSF-EHS).
Identifying Spatial and Dynamical Patterns from Images (sponsored by NSF-IIS).

University:

HKUST and Berkeley Joint Research Project.
Tsinghua-Berkeley Shenzhen Institute Research Fund.
UC Berkeley Faculty Startup Fund.
ShanghaiTech: Data Science Research Center Fund.
DARPA Urban Challenge (sponsored by the University of Illinois and others).
Geometry and Algebra in Computer Vision, RAP Program (sponsored by Math Department, the University of Illinois).
Vision-Based Control (sponsored by the University of Illinois Research Board).

Industry:

Sony Inc.
Bytedance Inc.
Berkeley FHL VIVE Center, HTC/VIVE.
Adobe Research Sponsorship.
Microsoft Research Fellowship (sponsored by the Microsoft Live Lab.).

Reflections on Research:

An essay reflecting on the methodology for research: Warning Signs of Bogus Research Progresses.
Powerpoint slides from a talk on How to do Research.
Powerpoint slides from a talk on How to Give a Good Presentation.
Powerpoint slides from my talk at CCF Elite Forum 2014 on Research as a Career.