Szpankowski Wins Flajolet Prize
Wojciech Szpankowski, Saul Rosen Distinguished Professor of Computer Science with a courtesy appointment in the School of Electrical and Computer Engineering at Purdue University, was chosen as the 2020 recipient of the Philippe Flajolet Lecture Prize and will deliver his lecture at the Analysis of Algorithms (AoA) meeting in Klagenfurt, Austria, June 15-19, 2020. The Philippe Flajolet Lecture Prize for outstanding contributions to analytic combinatorics and analysis of algorithms is awarded every two years by the AoA community. The award recognizes sustained record of high impact, seminal contributions to the research areas most directly impacted by the work of Flajolet. Previous winners are Don Knuth (2014), Bob Sedgewick (2016), and Luc Devroye (2018).
The prize is named in honor and recognition of the extraordinary accomplishments of the late Philippe Flajolet (1948 - 2011), a French computer scientist best known for fundamental advances in mathematical methods for the analysis of algorithms. His research laid the foundation of a subfield of mathematics, now known as analytic combinatorics.
Szpankowski joined the faculty in the Department of Computer Science in 1985. He is a Fellow of IEEE, the Erskine Fellow, and 2010 recipient of the Humboldt Research Award. His research interests cover analysis of algorithms, information theory, bioinformatics, analytic combinatorics, and stability problems of distributed systems. He published the book "Average Case Analysis of Algorithms on Sequences," John Wiley & Sons, 2001. Szpankowski has been a guest editor and an editor of several technical journals, including Theoretical Computer Science, ACM Transaction on Algorithms, Algorithmica, IEEE Transactions on Information Theory, and Combinatorics, Probability, and Computing. Currently, he is the Director of the NSF Science and Technology Center for Science of Information, whose mission is to extend classical information theory to modern settings, including knowledge discovery and information extraction from massive datasets.
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