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[ Sparse matrix computations | Parallel computing | Algorithms | CS education ]

My primary research interest at the moment is in sparse matrix computations. This interest goes back to my days as an undergraduate and has since led to explorations in other areas including algorithms (particularly graph algorithms), parallel algorithms (together with implementation and performance analysis), complexity theory, and machine learning. Other research interests include high performance computing (particularly performance modelling), algorithms, and computer science education.

My research is currently funded by an NSF CAREER grant (abstract).

Below (in no particular order) is more information on various aspects of my work. Some publications are listed in more than one category.

Sparse Matrix Computations

  • Some topics of interest:
  • preconditioners
  • perfect-elimination orderings
  • fill-reducing orderings
  • incomplete LU preconditioners
  • balancing sparse matrices

  • Publications:
  • E. Kuefler* and T.-Y. Chen. On using reinforcement learning to solve sparse linear systems. Proceedings of the International Conference on Computational Science, also Lecture Notes in Computer Science, 5101: 955-964, 2008. <pdf>
  • A. Holloway** and T.-Y. Chen. Neural networks for predicting the behavior of preconditioned iterative solvers. Proceedings of the International Conference on Computational Science, also Lecture Notes in Computer Science, 4487: 302--309, 2007. <DOI link> <pdf>
  • M. Lazzareschi* and T.-Y. Chen. Using performance profiles to evaluate preconditioners for iterative methods. Proceedings of the 2006 International Conference on Computational Science and its Applications, also Lecture Notes in Computer Science, 3982: 1081--1089, 2006. <DOI link>
  • T.-Y. Chen and M. Egan**. On the existence of nonsymmetric matrices with perfect elimination orderings. Proceedings of the Fifth Grace Hopper Celebration of Women in Computing, 2004. <pdf> <ps>
  • T.-Y. Chen. ILUTP_Mem: A space-efficient incomplete LU preconditioner, Proceedings of the 2004 International Conference on Computational Science and its Applications, also Lecture Notes in Computer Science, 3046: 31--39, 2004. <springerlink>
  • T.-Y. Chen. Preconditioning sparse matrices for computing eigenvalues and solving linear systems of equations. Ph.D. Thesis, Computer Science Division, UC Berkeley, 2001. <pdf> <abstract>
  • T.-Y. Chen and J. Demmel. Balancing sparse matrices for computing eigenvalues. Linear Algebra and Its Applications, 309: 261--287, 2000. <pdf> <ps>
  • T.-Y. Chen and J. Demmel. Balancing sparse matrices for computing eigenvalues. Section 7.2 in Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide. Editors Z. Bai, J. Demmel, J. Dongarra, A. Ruhe, and H. van der Vorst. SIAM, Philadelphia, 2000. <online book>
  • T.-Y. Chen, J. Gilbert, and S. Toledo. Toward an efficient column minimum degree code for symmetric multiprocessors. Proceedings of the 9th SIAM Conference on Parallel Processing for Scientific Computing, 1999. <pdf> <ps>
  • T.-Y. Chen. Balancing sparse matrices for computing eigenvalues. Masters Thesis, Computer Science Division, UC Berkeley, 1998. <ps>

  • Posters:
  • A. Holloway** and T.-Y. Chen. Using supervised machine learning techniques to understand preconditioner behavior. To appear in SIAM Conference on Computational Science and Engineering. February 2007.

    • High Performance Computing

  • Some topics of interest:
  • performance modeling
  • workload characterization
  • algorithms

  • Publications:
  • (to appear) T.-Y. Chen, O. Khalili, R. L. Campbell Jr., L. Carrington, M. Tikir, and A. Snavely. Performance prediction and ranking of supercomputers. Chapter 3 in High Performance Computing, volume 72 in series Advances in Computers. Academic Press, 2008.
  • T.-Y. Chen, M. Gunn*, B. Simon, L. Carrington, and A. Snavely. Metrics for ranking the performance of supercomputers. Cyberinfrastructure Technology Watch, 2(4B): 59--67, November 2006. <html>
  • T.-Y. Chen, J. Gilbert, and S. Toledo. Toward an efficient column minimum degree code for symmetric multiprocessors. Proceedings of the 9th SIAM Conference on Parallel Processing for Scientific Computing, 1999. <pdf> <ps> <overview>
  • T.-Y. Chen. The effect of caches on the performance analysis of data parallel CM-Fortran programs. Proceedings of the 1994 MIT Student Workshop on Scalable Computing, MIT LCS TR-622, 1994.

    • Algorithms

  • Some topics of interest:
  • Experimental algorithmics

  • Publications:
  • S. Azenkot*, T.-Y. Chen, and G. Cormode. An evaluation of the edit-distance-with-moves metric for comparing genetic sequences. DIMACS Technical Report 2005-39, November 2005. <abstract> <ps.gz>

    • CS Education Research

  • Some topics of interest:
  • preconceptions
  • Student reasoning about computers and about programming
  • Student approaches to design ( Multi-national, multi-institutional study of student-generated software designs )

  • Publications:
  • (to appear) B. Simon, D. Bouvier, T.-Y. Chen, G. Lewandowski, R. McCartney, and K. Sanders. Commonsense computing (episode 4): Debugging. Computer Science Education. Special issue on Debugging.
  • T.-Y. Chen, G. Lewandowski, R. McCartney, K. Sanders, and B. Simon. Commonsense Computing: using student sorting abilities to improve instruction. In Proceedings of SIGCSE, 2007. <DOI link>
  • B. Simon, T.-Y. Chen, G. Lewandowski, R. McCartney, and K. Sanders. Commonsense computing: What students know before we teach (Episode 1: Sorting). In the Proceedings of the 2006 International Computer Science Education Research Workshop (ICER), 2006. Canterbury, UK. <DOI link>
  • T.-Y. Chen, A. Monge, and B. Simon. Relationship of early programming language to novice generated design. In Proceedings of the 2006 Technical Symposium on Computer Science Education (SIGCSE), 2006. <DOI link>
  • T.-Y. Chen, S. Cooper, R. McCartney, and L. Schwartzman. The (relative) importance of software design criteria. In Proceedings of the 10th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE), 2005. Also SIGCSE Bulletin, 37(3): 34--38, September 2005. <DOI link>
  • S. Fincher, M. Petre, J. Tenenberg, K. Blaha, D. Bouvier, T.-Y. Chen, D. Chinn, S. Cooper, A. Eckerdal, H. Johnson, R. McCartney, A. Monge, J. E. Mostrom, K. Powers, M. Ratcliffe, A. Robins, D. Sanders, L. Schwartzman, B. Simon, C. Stoker, A. E. Tew, T. VanDeGrift. Cause for alarm?: A multi-national, multi-institutional study of student-generated software designs. In the Proceedings of the 4th Annual Finnish/Baltic Sea Conference on Computer Science Education, 2004. Invited to Informatics in Education, 4(1): 143--162, 2005. (Earlier version available as tech report: University of Kent, Canterbury: Computing Laboratory, Technical Report No. 16-04, 2004. <abstract> <pdf>)

  • Posters:
  • T.-Y. Chen, G. Lewandowski, R. McCartney, K. Sanders, and B. Simon. What do beginning students know, and what can they do? In Proceedings of the 11th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE), 2006. Also SIGCSE Bulletin, 38(3): 329, September 2006. <DOI link>

    • [*] work done while an undergraduate researcher
    [**] work done while a postbaccalaureate researcher

    "If we knew what it was we were doing, it would not be called research, would it?"
    -- Albert Einstein