## Stefan Wild

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.

#### Research Interests

- Simulation-based and derivative-free optimization
- Data analysis and analytics
- Automating high-performance computing

#### Projects

- Accelerating HEP Science: Inference and Machine Learning at Extreme Scales
- CACHE: Communication Avoidance and Communication Hiding at the Extreme Scale
- COMPASS: Community Petascale Project for Accelerator Science and Simulation
- Community Project for Accelerator Science and Simulation 4 (COMPASS-4)
- Community Project for Accelerator Science and Simulation 4
- Derivative-Free Optimization of Complex Systems
- Exploratory Research for Extreme-Scale Science: Quantum Algorithm Team (QAT)
- Machine Learning
- NUCLEI: Nuclear Computational Low-Energy Initiative
- OSCon: Optimizing superconductor transport properties through large-scale simulation
- Preparing PETSc/TAO for Exascale
- ProVESA: Program Verification for Extreme-Scale Applications
- Quantum Algorithms, Mathematics and Compilation Tools for Chemical Sciences
- Quantum Computing
- ROMPR: Robust Optimization and Modeling for Phase Retrieval
- SEANO: Structure-Exploiting Algorithms for Nonlinear Optimization
- SUPER: Institute for Sustained Performance, Energy and Resilience
- SciDAC-4: Scientific Discovery through Advanced Computing
- TAO: Toolkit for Advanced Optimization