Professor Ding's research interests include machine learning/data mining, bioinformatics, information retrieval, and web link analysis. He and his collaborators work on multi-class protein fold prediction is now standard benchmark for protein 3D structure prediction. Professor Ding and his team discovered that Principal Component Analysis (PCA) provides the solution to K-means clustering. They also proved that nonnegative matrix factorization is equivalent to K-means /spectral clustering. Professor Ding and his co-researcher generalized PCA to 2D Singular Value Decomposition for dimension reduction of a set of 2D matrices. Their MPH technology/software for integrating multi-component executables on distributed memory architectures are adopted in many state-of-art large scale models for predicting the long-term climate. Professor Ding also developed the vacancy tracking algorithm for provably optimal in-place multi-dimensional array index reshuffle .

Professor Ding previously worked at California Institute of Technology on Caltech Hypercubes developing parallel algorithms for Materials Science and Computational Biology; at NASA's Jet Propulsion Laboratory on developing algorithms for climate data assimilation, sparse matrix linear solvers and parallel graph partitioning; at the Lawrence Berkeley National Laboratory, working on high performance computing, algorithmic R&D for climate models, application benchmarking, giving tutorials on HPF, MPI, etc., and exploring new frontiers, the magic of matrix for clustering, ordering, ranking, embedding, bipartite graphs for systemic representation of proteins interaction networks, motifs, domains, complexes, functional modules, pathways .

Besides, Professor Ding has won four Best Paper Awards for climate data assimilation parallel algorithm and supernova detection using support vector machines, a NASA Group Achievement Award at JPL, and two Outstanding Performance Awards at Lawrence Berkeley National Laboratory. He served in review panels for US National Science Foundation, and as reviewer for research proposals of National Science Foundations of Ireland, Israel, and Research Grants Council of Hong Kong. He also served for Bioinformatics journal, and program committees of leading conferences in data mining, machine learning and bioinformatics. He co-organizes annual workshops on data mining using matrices and tensors. His work was reported by Science (PDF), Nature (PDF), SIAM, and National Research Council Report.