CSE Faculty Details

Chris Ding

Chris Ding
Professor

Office : 307 Nedderman Hall
Office Hours : TR: 3:00 - 5:00p
Phone : 817-272-7041
E-mail Address : chqding@uta.edu
Website : http://ranger.uta.edu/~chqding
Primary Research Stem : Bioinformatics
Additional Research Stem :
- Machine Learning
Research Interests : Data Mining, Bioinformatics, Bio-Social-Info Networks
Course Schedule :
- CSE 6319.001 : TR: 5:30 - 6:50p
Biography : My recent research include Bioinformatics, machine learning, data mining, information retrieval, web link analysis, and high performance computing. Our work on multi-class protein fold prediction is now standard benchmark for protein 3D structure prediction. Our paper on molecular dynamics simulation algorithm has been cited 241 times according to Science Citation Index. We discovered that Principal Component Analysis (PCA) provides the solution to K-means clustering. We proved that nonnegative matrix factorization is equivalent to K-means /spectral clustering. We generalize PCA to 2D Singular Value Decomposition for for dimension reduction of a set of 2D matrices. Our 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. We developed the vacancy tracking algorithm for provably optimal in-place multi-dimensional array index reshuffle . In 1987 I earned a Ph.D. from Columbia University in Theoretical Physics and Computer Science on building a parallel processor using Intel 80286s and commodity FPUs ( Science, front cover story, March 18, 1988), designing algorithms and doing large scale QCD simulations on it. From 1987 to 1993, I worked at California Institute of Technology on Caltech Hypercubes developing parallel algorithms for Materials Science (see Nature article by Editor John Maddox ) and Computational Biology (see a National Research Council Report ). From 1993 to 1996, I worked at NASA's Jet Propulsion Laboratory on developing algorithms for climate data assimilation (SIAM News, front page, October 1996), sparse matrix linear solvers and parallel graph partitioning. I joined LBNL in 1996, 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 ... I received a Pfister Fellowship at Columbia (1981-83), two 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 LBNL. I also served in review panels for National Science Foundation, editorial board for a Bioinformatics journal, and program committees for leading conferences in data mining, machine learning and Bioinformatics. My papers were cited 1431 times according to Google Scholar.