- PhD, Georgia Institute of Technology
Biography:Kamesh Madduri is an assistant professor in the Computer Science and Engineering department at The Pennsylvania State University. He received his PhD in Computer Science from Georgia Institute of Technology's College of Computing in 2008, and was previously a Luis W. Alvarez postdoctoral fellow at Lawrence Berkeley National Laboratory. He is interested in all aspects of high-performance computing for solving informatics and scientific data analysis challenges, including the design of new scalable methods, parallel algorithm design, performance studies on emerging hardware platforms, and developing high-performance software systems. He was awarded the first Junior Scientist prize from the SIAM Activity group on Supercomputing (2010), an Outstanding Graduate Research Assistantship award from Georgia Tech's College of Computing (2008), and the NASA Graduate Student Researchers Program Fellowship (2006-08).
Combinatorial Scientific Computing, Scalable Scientific Data Analysis and Management, Parallel Graph Algorithms with Applications to Computational Biology and Social Network Analysis
1. A. Buluç and K. Madduri. Parallel breadth-first search on distributed memory systems. In Proc. ACM/IEEE Conf. on High Performance Computing (SC 2011). ACM/IEEE, Nov 2011.
2. K. Madduri, E-J. Im, K.Z. Ibrahim, S. Williams, S. Ethier, L. Oliker. Gyrokinetic Particle-in-Cell Optimization on Emerging Multi- and Manycore Platforms. Parallel Computing 37(9), pages 501-520, 2011.
3. K. Madduri and K. Wu. Efficient joins with compressed bitmap indices. In Proc. 18th ACM Conf. on Information and Knowledge Management (CIKM 2009), pages 1017–1026. ACM, Nov 2009.
4. K. Madduri and D.A. Bader. Compact graph representations and parallel connectivity algorithms for massive dynamic network analysis. In Proc. 23rd IEEE Int’l. Parallel and Distributed Processing Symposium (IPDPS 2009). IEEE Computer Society, May 2009.
5. D.A. Bader and K. Madduri. A graph-theoretic analysis of the human protein-interaction network using multicore parallel algorithms. Parallel Computing 34(11), pages 621-639, 2008.