Chris Ding started in Theoretical Physics at Columbia University under Nobel Laureate Prof. T.D. Lee and helped building a parallel computer; this work was featured as a cover story of SCIENCE.  At California Institute of Technology, his work on Monte Carlo simulation on quantum spins was praised and explained in a commentary article written by NATURE editor-in-Chief John Maddox.

   At Lawrence Berkeley Lab, he and collaborators proved that PageRank is essentially a ranking by in-degree (sum of websites linking to it).  Proposed/invented in 2006 the matrix L21 norm that is widely used in artificial intelligence, machine learning, computer vision, computational biology. Designed the minimum redundancy
maximum relevance  feature selection that is widely adopted, e.g., by Uber and other tech companies. The research paper has been cited 11400 times, the highest cited paper in feature selection.

   Dr. Ding and co-workers' analysis on principal component analysis and nonegative matrix factorization revealed/demonstrated that data clustering and data reduction are intimately related -- two different manifestations of the same underlying mathematical model.

   Published 200+ research papers which were cited 60,000 times. In Stanford 2020, 2021, 2022, 2023 World Top 2% Scientists Ranking, Chris Ding is always ranked  top 0.1% of  7 million scientists in 176 disciplines/areas. In his research areas (artificial Intelligence, bioinformatics, information and communication), his world ranking is #133, out of 321,592 scientists in these categories (top 0.04%).

  He has given invited seminars at Stanford, UC Berkeley, Carnegie Mellon, IBM Research, Google Research,  Microsoft  Research, Univ. Alberta, Univ. Waterloo, Beijing Univ, Tsinghua Univ, Fudan Univ, Shanghai Jiaotong Univ, Zhejiang Univ, Nanjing Univ, USTC, Institute of Automation,  Institute of Computing, Chinese Academy, Univ Hong Kong, Chinese Univ Hong Kong, Nat'l Univ Singapore, Nat'l Taiwan Univ, etc.

At Lawrence Berkeley Lab, we proved that Google's PageRank is essentially identical to ranking by in-degree (the number of webpages pointing to the website). We proposed in 2006 the L12 norm which is widely used in artificial intelligence, machine learning, computer vision, computational biology.

Awards and honors