Charles E. Leiserson
Professor, Department of Electrical Engineering and Computer Science; Associate Director, Computer Science and Artificial Intelligence Laboratory
Charles E. Leiserson is the Edwin Sibley Webster Professor in MIT’s Department of Electrical Engineering and Computer Science, and the associate director and chief operating officer for the Computer Science and Artificial Intelligence Laboratory. His research centers on the theory and practice of performance engineering, where he develops algorithms and software to make computationally intensive tasks run quickly with minimal computing resources. His current focus is Fast AI. As computer performance due to semiconductor miniaturization dwindles with the end of Moore’s Law, Leiserson sees the opportunity to speed up applications by making software more efficient. His group is currently engaged in a major effort with IBM Research to develop fast, graph-based machine-learning applications for anti-money laundering and other applications.
His research contributions include the Cilk multithreaded programming language and runtime system, the fat-tree interconnection network, systolic architectures, cache-oblivious algorithms, and the Tapir compiler technology. His co-authored textbook, Introduction to Algorithms, has sold over 800,000 copies and is the one of the most cited publications in computer science. He is an elected fellow of the American Association for the Advancement of Science, Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and Society for Industrial and Applied Mathematics and is a member of the National Academy of Engineering. His awards include an ACM-IEEE Computer Society Ken Kennedy Award, an IEEE Computer Society Taylor L. Booth Education Award and an ACM Paris Kanellakis Theory and Practice Award. He earned a BS from Yale University, and a PhD from Carnegie Mellon University.
- M. Weber, G. Domeniconi, J. Chen, D.K.I. Weidele, C. Bellei, T. Robinson, C.E. Leiserson. (2019). Anti-money laundering in Bitcoin: Experimenting with graph convolutional networks for financial forensics. 25th SIGKDD Conference on Knowledge Discovery and Data Mining, Anomaly Detection in Finance Workshop.
- Al. Pareja, G. Domeniconi, J. Chen, T. Ma, T. Suzumura, H. Kanezashi, T. Kaler, T.B. Schardl, C.E. Leiserson. (2019). EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs. Thirty-Fourth AAAI Conference on Artificial Intelligence.
- M. Weber, J. Chen, T. Suzumura, A. Pareja, T. Ma, H. Kanezashi, T. Kaler, C.E. Leiserson, T.B. Schardl. (2018). Scalable Graph Learning for Anti-Money Laundering: A First Look. Thirty-Second Conference on Neural Information Processing Systems (NeurIPS) Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy.
- T.H. Cormen, C.E. Leiserson, R.L. Rivest, and C. Stein. (2009). Introduction to Algorithms, third edition. The MIT Press.
- T.B. Schardl, W.S. Moses, and C.E. Leiserson. (2019). Tapir: Embedding recursive fork-join parallelism into LLVM’s intermediate representation. ACM Transactions on Parallel Computing, Vol. 6, No. 4, Article 15.
- January 30, 2017: MIT News, Optimizing Code.
- February 9, 2016: MIT News, Three from MIT elected to the National Academy of Engineering.
- October 28, 2010. MIT News, Teaching real-world programming.