معرفی
Stefan M. Wild serves as Director of the Applied Mathematics and Computational Research (AMCR) Division and Senior Scientist at Lawrence Berkeley National Laboratory, while holding an adjunct faculty position in the Industrial Engineering and Management Sciences (IEMS) department at Northwestern University's McCormick School of Engineering. He also serves as a Senior Fellow at NAISE (Northwestern Initiative for AI and Society).
Dr. Wild earned his Ph.D. and M.S. in Operations Research from Cornell University (2009, 2007) and his M.S. and B.S. in Applied Mathematics from the University of Colorado, Boulder (2003, 2002). His academic journey includes an Argonne Director's Postdoctoral Fellowship (2008-2010) and the DOE Computational Science Graduate Fellowship (2005-2008).
His research program focuses on developing numerical optimization and automated learning algorithms for challenging science and engineering problems at interfaces involving computer simulations, complex data, and physical experiments. Dr. Wild leads multiple community software projects including BAND, parMOO, libEnsemble, deepHyper, NUCLEI, POptUS, and surmise, with applications spanning nuclear physics, materials science, and astrophysics. His work bridges derivative-free optimization, uncertainty quantification, high-performance computing, and scientific machine learning.
Dr. Wild has advised numerous postdocs who have established successful careers at national laboratories and academic institutions. His editorial responsibilities include Mathematical Programming Computation, INFORMS Journal on Computing, Data Science in Science, and SIAM Review.
- U.S. Department of Energy Early Career Research Award (2020)
- STS Forum Future Leader (2018)
- IDC HPC Innovation Excellence Award (2015)
- SIAM SIGEST Award (2014)
- Strategic Laboratory Leadership Program (UChicago Booth School) (2013)
- DOE Computational Science Graduate Fellowship (2005-2008)
As AMCR Division Director, he leads a diverse team of applied mathematicians, computational scientists, and software engineers addressing some of the world's most challenging computational problems across scientific and engineering disciplines. His leadership emphasizes both technical excellence and commitment to inclusion, diversity, equity, and accountability in scientific research.
