The Department of Electrical Engineering and Computer Science, University of Tennessee, USA

Organization and funding: The University of Tennessee is a comprehensive land-grant university and a Carnegie I Research Institution. Knoxville is the flagship campus of the University of Tennessee system. Students enroll from every state in the nation and over 100 foreign countries. Oak Ridge National Laboratory is the Department of Energy's primary institution for high performance computing. It has distinctive capabilities in biological science, materials science, neutron science and many other areas. ORNL employs roughly 1500 scientists and engineers and covers a total of 58 square miles. Dr. Langston regularly consults at and maintains accounts on ORNL's vast assortment of state-of-the-art clusters, supercomputers and mass storage systems. His team is funded 75% time thorough UT and 25% time through grants on which he serves as PI or co-PI.

Key PIs and their roles: Professor Langston leads a team of students, post doctoral fellows and research associates whose work is focused on efficient algorithm design, analysis and high performance implementations, with a special emphasis on applications to computational biology. He also serves as Collaborating Scientist at ORNL, where he maintains offices in the Biosciences Division and regularly consults in the Computer Science and Mathematics Division, the Chemical Sciences Division, the Joint Institute for Computational Science and the Computational Biology Institute. He is currently in the process of developing portals through which the community at large may access his team's computational tools. His work in developing ClustalXP is a prominent example. Professor Langston has authored over 200 refereed publications, including those in journals relevant to this project such as Nature Genetics, PLoS Computational Biology, Journal of the ACM, and Journal of Allergy and Clinical Immunology. He is perhaps best known for his long-standing work on combinatorial algorithms, complexity theory and design paradigms for sequential and parallel computation. In addition to maintaining his research program, he regularly teaches courses on algorithmic analysis, computational and systems biology, discrete optimization, graph theory and related subjects. His research has been funded in the U.S. by the National Science Foundation, the Department of Defense, the Department of Energy, the National Institutes of Health and a variety of other agencies. It has been funded abroad by the Australian Research Council and the European Commission. He has received numerous awards, most recently the Distinguished Service Prize from the Association for Computing Machinery Special Interest Group on Algorithms and Computation Theory.

Five relevant publications

1. F. N. Abu-Khzam, M. A. Langston, P. Shanbhag, and C. T. Symons, Scalable parallel algorithms for FPT problems, Algorithmica, vol. 45, 2006, 269-284.
2. M. Benson, B. Andersson, M. Adner, Å.Torinssson-Naluai, M. A. Langston, and L. O. Cardell, A Network-Based Analysis of the Late Phase Reaction of the Skin, Journal of Allergy and Clinical Immunology, vol. 118, 2006, 220-225.
3. E. J. Chesler, L. Lu, S. Shou, Y. Qu, J. Gu, J. Wang, H. C. Hsu, J. D. Mountz, N. E. Baldwin, M. A. Langston, J. B. Hogenesch, D. W. Threadgill, K. F. Manly, and R. W. Williams, Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function, Nature Genetics, vol. 37 (3), 2005, 233-242.
4. M. A. Langston, L. Lan, X. Peng, N. E. Baldwin, C. T. Symons, B. Zhang, and J. R. Snoddy, " A combinatorial approach to the analysis of differential gene expression data: the use of graph algorithms for disease prediction and screening," in Methods of Microarray Data Analysis IV, Papers from CAMDA '03, K. F. Johnson and S. M. Lin, Eds. (Boston: Kluwer Academic Publishers, 2005) 223-238.
5. B. H. Voy, J. A. Scharff, A. D. Perkins, A. M. Saxton, B. Borate, E. J. Chesler, L. K. Branstetter, and M. A. Langston, Extracting gene networks for low dose radiation using graph theoretical algorithms, PLoS Computational Biology, vol. 2 (7), 2006