Rate My Professor Tim Mitchell

Tim Mitchell serves as Associate Professor of Computer Science in the Department of Computer Science at Queens College, City University of New York (CUNY), and as Associate Professor in the Computer Science PhD Program at the CUNY Graduate Center. Additionally, he holds the position of Deputy Director of Data Science for the MS and Advanced Certificate programs in Data Science at the CUNY Graduate Center. Mitchell received his Ph.D. in Computer Science from the Courant Institute of Mathematical Sciences at New York University in 2014. He also holds a B.S. degree in both computer science and mathematics from Tufts University. Before joining CUNY, he spent seven years as a research scientist in the Computational Methods in Systems and Control Theory group at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. Earlier, he was an Assistant Research Scientist at the Courant Institute of Mathematical Sciences, New York University.

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.