I have been very fortunate to work with a number of great collaborators over the years.
- Prof. Romain Couillet:
- University Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, France.
- Holder of the UGA MIAI LargeDATA Chair.
- Prof. Michael Mahoney:
- Department of Statistics, International Computer Science Institute (ICSI), and Lawrence Berkeley National Laboratory (LBNL) at UC Berkeley, USA.
- Director of the UC Berkeley FODA (Foundations of Data Analysis) Institute grant. Amazon Scholar.
- Prof. Michał Dereziński:
- Department of Computer Science and Engineering, University of Michigan, USA.
- Dr. Fanghui Liu:
- Postdoc Researcher, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland.
Junior collaborators and students
- Chengmei Niu: Ph.D. student, EIC, HUST, China.
- Zhanbo Feng: Ph.D. student, CSE, Shanghai Jiao Tong University, China.
- Yuanjie Wang: master student, EIC, HUST, China.
- Lingyu Gu: master student, EIC, HUST, China.
- Yongqi Du: master student, EIC, HUST, China.
- Yueying Hu: Ph.D. student, Hong Kong University of Science And Technology (HKUST), China.
I am always looking for self-motivated students with a strong background/interest in statistics and machine learning. Below are a few references on related topics:
- Pattern Recognition and Machine Learning by Christopher M. Bishop, available online here.
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, available online here.
Random Matrix Theory and/or High-dimensional Statistics
- Probability and Measure by Patrick Billingsley.
- High-Dimensional Probability: An Introduction with Applications in Data Science by Roman Vershynin, available online here.
- Random Matrix Methods for Machine Learning by Romain Couillet and myself, can be hard-to-digest for non-math majored undergraduate students, available online here.