Stefan Wild is a computational mathematician at Argonne and a Fellow in the Computation Institute at the University of Chicago. His primary research focus is on algorithms and software for numerical optimization and data analysis.
Wild joined Argonne as a Director's Postdoctoral Fellow in September 2008. Prior to this, he obtained his Ph.D. in operations research from Cornell University and his M.S. and B.S. in applied mathematics from the University of Colorado.
Wild is currently developing model-based methods for derivative-free optimization that exploit additional structures often found in practical applications, including constraints, discrete variables, computational noise, parallel computing environments, and parameter estimation. In addition to numerical optimization, he is interested in machine learning and numerical linear algebra and leads the ROMPR project.
AI/Machine Learning/Deep Learning
Performance Analysis and Optimization