Rate My Professor Lisa Kusch

Kusch's research specializes in advanced optimization techniques for engineering design, emphasizing optimization under uncertainty, partial differential equation-constrained problems, multi-objective optimization, and one-shot methods. Her work bridges rigorous mathematical formulations with practical applications, including the design of imaging systems that balance optical performance, efficiency, and manufacturability, as well as aerodynamic shape optimization and internal flow design. Key publications include 'Design of a three-dimensional parallel-to-point imaging system using inverse methods' (2026, Journal of the Optical Society of America A), co-authored with Sanjana Verma and others; 'Adjoint-based multi-point optimization framework for mixing processes applied to a venturi mixer' (2025, Applied Thermal Engineering); 'A neural network approach for solving the Monge–Ampère equation with transport boundary condition' (2025, Journal of Computational Mathematics and Data Science); 'Neural network methods for two-dimensional finite-source reflector design' (2026, arXiv preprint); and 'Data-driven aerodynamic shape design with distributionally robust optimization approaches' (2024, Computer Methods in Applied Mechanics and Engineering). With over 16 journal articles, conference contributions, and book chapters, her contributions have garnered approximately 54 citations on the TU/e research portal. At TU/e, she teaches courses such as Calculus and Professional Portfolio, contributing to education in mathematics and computer science.