Rate My Professor Tim Mitchell

His research interests encompass numerical computing, numerical analysis, numerical linear algebra, continuous optimization, scientific computing, robust control, and stability measures of dynamical systems. Mitchell focuses on designing fast and reliable algorithms and software for robust control and stability analysis of dynamical systems. These efforts extend to applications in model-order reduction, nonsmooth constrained optimization, and benchmarking of numerical algorithms. His methodologies integrate techniques from numerical linear algebra, optimization, and scientific computing, with an emphasis on scalable solutions for large-scale problems and enhanced algorithms for smaller-scale applications. Notable publications include "Convergence Rate Analysis and Improved Iterations for Numerical Radius Computation" published in the SIAM Journal on Matrix Analysis and Applications (2022), "Optimization for Robustness Evaluation Beyond ℓ_p Metrics" (ICASSP 2023), "Implications of Solution Patterns on Adversarial Robustness" (CVPRW 2023), "Low-Order Control Design using a Reduced-Order Model with a Stability Constraint" (CDC 2018), and contributions to fixed low-order controller design and H∞ optimization (ROCOND 2015). Mitchell has also developed NCVX, a scalable package for nonconvex optimization in machine learning. His scholarly work has garnered over 435 citations according to Google Scholar.