Collaborators

I have been very fortunate to work with a number of great collaborators over the years.

Senior collaborators

• 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:

Machine Learning

• 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.

Online courses and tutorials

• Introductive course on Machine Learning bu Andrew Ng, for example the Stanford CS229 course here.
• Deep Learing DIY used for courses at ENS and X, by Marc Lelarge, Andrei Bursuc, and Jill-Jênn Vie.
• 3Blue1Brown
• Pytorch tutorial