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+## Welcome
+
+I'm George Karypis, a Professor at the Department of Computer Science & Engineering
+at the University of Minnesota in the Twin Cities of Minneapolis and Saint Paul.
+
+## Research
+
+My research interests are concentrated in the areas of data mining, recommender
+systems, learning analytics, high-performance computing, and chemical informatics and
+from time-to-time, I look at various problems in the areas of health informatics,
+information retrieval, bioinformatics, and scientific computing. 
+
+Within these areas, my research focuses in developing novel algorithms for solving
+important existing and/or emerging problems, and on developing practical software
+tools implementing some of these algorithms. The results from this research have been
+presented in various conferences and published in leading peer reviewed journals and
+highly selective conference proceedings.
+
+In my research I strive to develop algorithms that are practical (they can be easily
+implemented on commercially available platforms), efficient (require as little time
+as possible), effective (do a good job in solving the underlying problem), and
+scalable (remain efficient and effective as we increase the size of the problem
+and/or the number of processors). Quite often I consider my research as that of
+algorithm engineering.
+
+### Projects
+
+Over the years I have developed many algorithms for 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.
+
+### Software
+
+The research of my group 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, my group has developed two web-based servers for clustering gene
+expression data (gCLUTO) and for predicting the secondary structure of proteins
+(YASSPP).
+
+### Publications
+
+I have coauthored over one hundred journal and conference papers on these topics and
+a book titled "Introduction to Parallel Computing" (Publ. Addison Wesley, 2003, 2nd
+edition).
+
+
+## Education, Mentoring, and Advising
+
+I take great joy in teaching, advising, and mentoring undergraduate and graduate
+students and I consider it to be one of the perks of my job.
+
+My greatest pleasure and ultimate job satisfaction comes at those points during a
+course at which my students transition from simply listening and reading the material
+to actually understanding them, grasping them, internalizing them, and seeing how
+they fit within the general computer science discipline and how they are applied to
+solve real problems in real applications.
+
+### Teaching
+
+My regular teaching responsibilities include the introductory course on algorithms
+and data structures (CSci 4041), a course on parallel computing (CSci 5451), a course
+on data mining (CSci 5523), and a course describing various computational techniques
+used in bioinformatics (CSci 5481).
+
+
+### Advising 
+
+Over the years I had the opportunity to advise and mentor many Ph.D. and M.S.
+students. I find this to be a very rewarding experience and I have learned (and still
+learning) a lot from all of my former and current students.
+
+I believe that effective advisor-advisee relationships must be built on mutual trust,
+commitment, and benefit, and be designed to advance the advisees education and
+provide them with a solid foundation upon which to build their future career.
+
+Pursing an advanced graduate degree and contacting research to advance the
+state-of-the-art is not an easy task. Its outcome is never guaranteed, there are
+always unforeseen complications, and not every idea works out! It requires
+commitment, dedication, hard work, and the ability to know when to continue pursuing
+a good idea even when the initial results are not very encouraging and when to stop
+pursing a bad idea and start over again. I do not expect my students to be expert
+researchers when joining my group, but I expect them to be bright, dedicated,
+motivated, and willing to work as hard as I do.
+
+
+
+