INFORMS Optimization Society 2023 Egon Balas Prize


Alberto Del Pia (University of Wisconsin–Madison, Madison, WI)
Stefan Wild (Argonne National Laboratory, Lemont, IL)


Alberto Del Pia is an Associate Professor in the Department of Industrial and Systems Engineering and Wisconsin Institute for Discovery at the University of Wisconsin-Madison. He received his PhD from the University of Padova in 2009 and has held postdoctoral positions at Otto-von-Guericke-Universität, ETH Zurich, and as the Herman Golstine Memorial Postdoctoral Fellow at the IBM T.J. Watson Research Center. Dr. Del Pia was awarded the INFORMS Optimization Society Prize for Young Researchers in 2017 and serves on the editorial boards of Mathematical Programming and Discrete Optimization. Dr. Del Pia has made fundamental contributions to a wide range of problems in discrete mathematical optimization. His work has shed light on the power of classes of cutting planes for mixed-integer linear programming, established fundamental theory and algorithms for mixed-integer quadratic programming, and provided deep understanding of the multilinear set which arises in many nonconvex optimization problems. More recently, Dr. Del Pia has been establishing novel connections between discrete optimization theory and problems in machine learning and statistics. The award committee was impressed by the combined depth and breadth of Dr. Del Pia's work.


Stefan M. Wild directs the Applied Mathematics and Computational Research Division in the Computing Sciences Area at Lawrence Berkeley National Laboratory. Previously, he was a senior computational mathematician and a deputy division director of the Mathematics and Computer Science Division at Argonne National Laboratory. He received his PhD from Cornell University in 2009. He has served on editorial boards of INFORMS Journal on Computing, Mathematical Programming C, Operations Research, SIAM Journal on Scientific Computing, and SIAM Review. Stefan Wild is internationally known for his work on derivative-free optimization (DFO), from algorithmic development and convergence theory to practical implementations and software tools. He is the co-developer of the so-called data profiles, a technique widely used to measure progress of DFO methods for increasing budgets of function evaluations. He has made key contributions to computational noise estimation and model-based methods for global and non-smooth DFO. Dr. Wild has a long research record of problem solving in optimization involving expensive computer simulations, large data sets, and physical experiments. The award committee was impressed by Dr. Wild’s combination of fundamental contributions to optimization theory and algorithms with significant and impactful contributions to scientific computing and applications. 

Prize committee

Amirali Ahmadi, Dan Bienstock (chair),  Jim Luedtke, Luis Nunes Vicente