Page 71 - 2017 Brochure
P. 71
究員
徐讚昇 Tsan-sheng Hsu
Research Fellow
Ph.D., Computer Sciences, University of Texas at Austin
Tel: +886-2-2788-3799 ext. 1701 Fax: +886-2-2782-4814
Email: tshsu@iis.sinica.edu.tw
http://www.iis.sinica.edu.tw/pages/tshsu
• Professor (Adjunct), Computer Science and Information Engineering, National Taiwan University (2004-present)
• Acting Chief, Information Center, Institute of Information Science, Academia Sinica (2013-2015)
• Director, Computing Center, Academia Sinica (2008-2010)
• Deputy Director, Institute of Information Science, Academia Sinica (2002-2004)
• M.S.C.S., Computer Sciences, University of Texas at Austin (1990)
• B.S., Computer Science and Information Engineering, National Taiwan University (1985)
Research Description in sequential, parallel and distributed algorithms. For example, we
are now studying efficient implementation of graph algorithms on
My current work concerns graph theory and its applications, the GPUs.
design, analysis, implementation and performance evaluation of
algorithms, and data-intensive computing. Data-intensive computing:
With the rapid development of computer and communication
Graph theory and its applications: technology, it has become much easier to access and store
Graphs model many important applications and are also tools that massive amounts of data electronically. We are interested in
may be used to solve theoretical problems. We often begin our research problems concerning efficient computation of massive
research by probing fundamental theoretical problems, such as the data, which include classical computer games, and constructing
structures of graphs with certain properties. With these properties, and viewing of medical-related big data. In classical computer
we then usually design efficient algorithms and solve applications. games, we currently focus on a stochastic two-player game called
One important problem we are currently interested in is efficient Chinese Dark Chess. In medical-related big data, we have been
graph algorithms on the streaming model. working on efficient epidemic simulation and disease networks.
Notably, our research in data-intensive computing often overlaps
Design, analysis, implementation and performance evaluation and benefits from our studies of graph theory and algorithm.
of algorithms:
Algorithm is one of the cores of computer sciences. We are
interested in all aspects of research on algorithms, including finding
new algorithms for interesting problems and designing efficient
implementations to solve real-world applications. We are interested
Publications 7. Martin Farach-Colton, Tsan-sheng Hsu, Meng Li and Meng-Tsung
Tsai, “Finding Articulation Points of Large Graphs in Linear Time,”
1. Bo-Nian Chen, Pangfeng Liu, Shun-Chin Hsu and Tsan-sheng Hsu, Proceedings of WADS 2015 : Algorithms and Data Structures
“Aggregating Consistent Endgame Knowledge in Chinese Chess,” Symposium, pages 363--372, 2015.
Knowledge-Based Systems, volume 34, pages 34--42, October 2012.
8. Hung-Jui Chang, Chih-Wen Hsueh and Tsan-sheng Hsu, “Convergence
2. Kung Chen, Tsan-sheng Hsu , Churn-Jung Liau, Da-Wei Wang, “A and Correctness Analysis of Monte-Carlo Tree search Algorithms: A
scripting language for automating secure multiparty computation,” Case Study of 2 by 4 Chinese Dark Chess,” Proceedings of the 2015
Proceedings of the 8th Asia Joint Conference on Information Security IEEE Conference on Computational Intelligence and Games (CIG
(AsiaJCIS), pages 127--134, July 2013. 2015), pages 260--266, 2015.
3. Bo-Nian Chen, Hung-Jui Chang, Shun-Chin Hsu, Jr-Chang Chen, 9. Hsin-Wen Wei, Tseng-Yi Chen and Tsan-sheng Hsu, “BASE: An
and Tsan-sheng Hsu, “Advanced meta-knowledge for Chinese Chess assistant tool to precisely simulate energy consumption and reliability
Endgame,” International Computer Game Association (ICGA) of energy-efficient storage systems,” Software: Practice and
Journal, volume 37, number 1, pages 17--24, March 2014. Experience, volume 46, number 5, pages 581--599, 2016.
4. Tsan-sheng Hs, Churn-Jung Liau, and Da-Wei Wang, “A Logical 10. Zong-De Jian, Tsan-sheng Hsu and Da-Wei Wang, “Searching
Framework for Privacy-Preserving Social Network Publication,” Vaccination Strategy with Surrogate-assisted Evolutionary
Journal of Applied Logic, volume 12, number 2, pages 151--174, Computing,” Proceedings of the 6th International Conference
2014. on Simulation and Modeling Methodologies, Technologies and
Applications (SIMULTECH), pages 56--63, 2016.
5. Chih-Hsuan Hsu, Cho-Chin Lin and Tsan-sheng Hsu, “Adaptable
Scheduling Algorithm for Grids with Resource Redeployment 69
Capability,” Journal of Grid Computing, volume 12, number 3, pages
447--463, September 2014.
6. Jr-Chang Chen, Ting-Yu Lin, Bo-Nian Chen, and Tsan-sheng Hsu,
“Equivalence Classes in Chinese Dark Chess Endgames,” IEEE
Transactions on Computational Intelligence and AI in Games, volume
7, number 2, pages 109--122, 2015.
徐讚昇 Tsan-sheng Hsu
Research Fellow
Ph.D., Computer Sciences, University of Texas at Austin
Tel: +886-2-2788-3799 ext. 1701 Fax: +886-2-2782-4814
Email: tshsu@iis.sinica.edu.tw
http://www.iis.sinica.edu.tw/pages/tshsu
• Professor (Adjunct), Computer Science and Information Engineering, National Taiwan University (2004-present)
• Acting Chief, Information Center, Institute of Information Science, Academia Sinica (2013-2015)
• Director, Computing Center, Academia Sinica (2008-2010)
• Deputy Director, Institute of Information Science, Academia Sinica (2002-2004)
• M.S.C.S., Computer Sciences, University of Texas at Austin (1990)
• B.S., Computer Science and Information Engineering, National Taiwan University (1985)
Research Description in sequential, parallel and distributed algorithms. For example, we
are now studying efficient implementation of graph algorithms on
My current work concerns graph theory and its applications, the GPUs.
design, analysis, implementation and performance evaluation of
algorithms, and data-intensive computing. Data-intensive computing:
With the rapid development of computer and communication
Graph theory and its applications: technology, it has become much easier to access and store
Graphs model many important applications and are also tools that massive amounts of data electronically. We are interested in
may be used to solve theoretical problems. We often begin our research problems concerning efficient computation of massive
research by probing fundamental theoretical problems, such as the data, which include classical computer games, and constructing
structures of graphs with certain properties. With these properties, and viewing of medical-related big data. In classical computer
we then usually design efficient algorithms and solve applications. games, we currently focus on a stochastic two-player game called
One important problem we are currently interested in is efficient Chinese Dark Chess. In medical-related big data, we have been
graph algorithms on the streaming model. working on efficient epidemic simulation and disease networks.
Notably, our research in data-intensive computing often overlaps
Design, analysis, implementation and performance evaluation and benefits from our studies of graph theory and algorithm.
of algorithms:
Algorithm is one of the cores of computer sciences. We are
interested in all aspects of research on algorithms, including finding
new algorithms for interesting problems and designing efficient
implementations to solve real-world applications. We are interested
Publications 7. Martin Farach-Colton, Tsan-sheng Hsu, Meng Li and Meng-Tsung
Tsai, “Finding Articulation Points of Large Graphs in Linear Time,”
1. Bo-Nian Chen, Pangfeng Liu, Shun-Chin Hsu and Tsan-sheng Hsu, Proceedings of WADS 2015 : Algorithms and Data Structures
“Aggregating Consistent Endgame Knowledge in Chinese Chess,” Symposium, pages 363--372, 2015.
Knowledge-Based Systems, volume 34, pages 34--42, October 2012.
8. Hung-Jui Chang, Chih-Wen Hsueh and Tsan-sheng Hsu, “Convergence
2. Kung Chen, Tsan-sheng Hsu , Churn-Jung Liau, Da-Wei Wang, “A and Correctness Analysis of Monte-Carlo Tree search Algorithms: A
scripting language for automating secure multiparty computation,” Case Study of 2 by 4 Chinese Dark Chess,” Proceedings of the 2015
Proceedings of the 8th Asia Joint Conference on Information Security IEEE Conference on Computational Intelligence and Games (CIG
(AsiaJCIS), pages 127--134, July 2013. 2015), pages 260--266, 2015.
3. Bo-Nian Chen, Hung-Jui Chang, Shun-Chin Hsu, Jr-Chang Chen, 9. Hsin-Wen Wei, Tseng-Yi Chen and Tsan-sheng Hsu, “BASE: An
and Tsan-sheng Hsu, “Advanced meta-knowledge for Chinese Chess assistant tool to precisely simulate energy consumption and reliability
Endgame,” International Computer Game Association (ICGA) of energy-efficient storage systems,” Software: Practice and
Journal, volume 37, number 1, pages 17--24, March 2014. Experience, volume 46, number 5, pages 581--599, 2016.
4. Tsan-sheng Hs, Churn-Jung Liau, and Da-Wei Wang, “A Logical 10. Zong-De Jian, Tsan-sheng Hsu and Da-Wei Wang, “Searching
Framework for Privacy-Preserving Social Network Publication,” Vaccination Strategy with Surrogate-assisted Evolutionary
Journal of Applied Logic, volume 12, number 2, pages 151--174, Computing,” Proceedings of the 6th International Conference
2014. on Simulation and Modeling Methodologies, Technologies and
Applications (SIMULTECH), pages 56--63, 2016.
5. Chih-Hsuan Hsu, Cho-Chin Lin and Tsan-sheng Hsu, “Adaptable
Scheduling Algorithm for Grids with Resource Redeployment 69
Capability,” Journal of Grid Computing, volume 12, number 3, pages
447--463, September 2014.
6. Jr-Chang Chen, Ting-Yu Lin, Bo-Nian Chen, and Tsan-sheng Hsu,
“Equivalence Classes in Chinese Dark Chess Endgames,” IEEE
Transactions on Computational Intelligence and AI in Games, volume
7, number 2, pages 109--122, 2015.
