Karypis’s current research interests span the areas of data mining, bio-informatics, parallel processing, CAD, and scientific computing.
Over the years George Karypis has developed algorithms to solve a variety of problems including dynamic load balancing of unstructured parallel computations, graph and circuit partitioning, protein remote homology prediction and fold recognition, protein structure prediction, recommender systems, data clustering, document classification and clustering, frequent pattern discovery in diverse datasets (transactions, sequences, graphs), parallel Cholesky factorization, and parallel preconditioners.
Karypis' research has resulted in the development of software libraries for serial and parallel graph partitioning (METIS and ParMETIS), hypergraph partitioning (hMETIS), for parallel Cholesky factorization (PSPASES), for collaborative filtering-based recommendation algorithms (SUGGEST), clustering high dimensional datasets (CLUTO), and finding frequent patterns in diverse datasets (PAFI). In addition, he has developed two web-based servers for clustering gene expression data (gCLUTO) and for predicting the secondary structure of proteins (YASSPP).
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