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Research Fellow/Professor  |  Hsu, Tsan-sheng  
 
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Research Descriptions
 

My research focuses on theory and practice of massive data computing. In fundamental algorithm design for massive data, we currently investigate algorithms using the semi-streaming model which only consider a limited amount of main memory is available while the data stream can be read over and over again. The cost of running such algorithms consists of the amount of main memory used and the number of passes the data stream is read. Some fundamental properties of basic graph algorithms are discovered. On the application side of this framework, with the rapid development of computer and communication technology, it has become much easier to access and store massive amounts of data electronically. We are interested in research problems concerning efficient computation of massive data, which include classical computer games, and constructing and viewing of medical-related big data. In classical computer games, we currently focus on a stochastic two-player game called Chinese Dark Chess, and on extremely large endgame databases of various games. In medical-related big data, we have been working on efficient epidemic simulation, disease networks and risk prediction. Notably, our research in data-intensive computing often overlaps and benefits from our studies of graph theory and algorithm.

 

 
 
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