Daniel Dadush receives A.W. Tucker Prize 2015

Daniel Dadush

Daniel Dadush of CWI's Networks & Optimization group has been awarded the A. W. Tucker Prize 2015. He received the prize for his doctoral thesis that he completed at Georgia Tech in 2012. The A. W. Tucker Prize is established by the Mathematical Optimization Society in 1985, and is awarded at each International Symposium on Mathematical Programming for an outstanding doctoral thesis.

The citation reads: "In his PhD thesis 'Integer Programming, Lattice Algorithms, and Deterministic Volume Computation' Dadush presents several impressive results on algorithmic convex geometry, geometry of lattices, and the complexity of integer programming. His results include a proof of the claim that the Chvatal-Gomory closure of a convex body is a rational polyhedron, improved algorithms for finding the shortest and closest lattice vectors, an optimal deterministic algorithm for computing an M-ellipsoid of a convex body, and a much-improved and nearly-optimal deterministic algorithm for computing the volume of a convex body. By combining all the techniques derived in his thesis, Dadush derives the fastest currently known algorithm for integer programming. The complexity of the algorithm represents a significant improvement over classical algorithms by Lenstra and by Kannan and shows Dadush's deep understanding of lattice techniques and convex geometry. In his work Dadush pays great attention to detail and exposition, which results in a thesis that is truly worthy of the 2015 A.W. Tucker prize."

Daniel Dadush obtained an ScB in Mathematics from Brown University in 2006, and a PhD from the Algorithms, Combinatorics, and Optimization program at Georgia Tech under the guidance of Santosh Vempala in 2012. Following his PhD, Daniel spent two years as a Simons Postdoctoral Fellow in the Computer Science department at New York University. In September 2014 he joined CWI as a tenure track researcher in the Networks and Optimization group. He is currently interested in developing techniques for solving a broad range of optimization problems, where he particularly likes those benefiting from geometric thinking. In his free time Daniel enjoys traveling, swing dancing, and riding his bike through the canals of Amsterdam.


Source: Georgia Tech