Modeling, Control & Optimization
Research Advisors for Modeling, Control & Optimization
Bio ItemNicole Abaid , bio
Dr. Abaid's research focuses on networked dynamical systems. She studies diverse biological systems, ranging from animal groups to brain networks, to inspire novel results in mathematical modeling and control.
Bio ItemChristopher Beattie , bio
The principal research interests of Professor Beattie are in the areas of scientific computing and large scale computational linear algebra, with an emphasis on iterative Krylov methods. His primary focus is on model reduction of large scale dynamical systems with a goal of developing practical and rigorous computational algorithms for efficient manipulation and simulation of systems arising from physical models frequently described by systems of partial differential equations.
Bio ItemJeff Borggaard , bio
Professor Borggaard studies the design and control of fluids. This includes computational fluid dynamics, control theory, optimization, sensitivity analysis, uncertainty quantification, and reduced-order models. In each case, the application of these research areas to partial differential equations that describe fluids are of interest.
Bio ItemJohn Burns , bio
Professor Burns' current research is focused on computational methods for modeling, control, estimation and optimization of complex systems where spatially distributed information is essential. This includes systems modeled by partial and delay differential equations. Recent applications include modeling and control of thermal fluids, design and thermal management systems and optimization of mobile sensor networks.
Bio ItemLauren M. Childs , bio
Professor Childs develops and analyzes mathematical and computational models to examine biologically-motivated questions.
Bio ItemMatthias Chung , bio
Professor Chung's research concerns computational methods in the intersection of computational modeling, machine learning, data analytics with an emphasis on inverse problems. Driven by its application, he and his group develop and analyze efficient numerical methods for inverse problems. Applications of interest are, but not limited to, systems biology, medical and geophysical imaging, and dynamical systems.
Bio ItemStanca M. Ciupe , bio
Dr. Ciupe's research interest is in the field of applied mathematics, specifically, systems of ordinary and delay differential equations and their application to biology and medicine.
Bio ItemSerkan Gugercin , bio
Professor Gugercin studies computational mathematics, numerical analysis, and systems and control theory with a focus on data-driven modeling and model reduction of large-scale dynamical systems with applications to inverse problems, structural dynamics, material design, and flow control.
Bio ItemHonghu Liu , bio
Professor Liu's research focuses on the design of effective low-dimensional reduced models for nonlinear deterministic and stochastic PDEs as well as DDEs. Applications to classical and geophysical fluid dynamics are actively pursued. Particular problems that are addressed include bifurcation analysis, phase transition, surrogate systems for optimal control, and stochastic closures for turbulence.
Bio ItemShu-Ming Sun , bio
Professor Sun's research interests include the mathematical theory of fluid mechanics, the theory of partial differential equations, and applied nonlinear analysis.
Bio ItemLayne T. Watson , bio
Dr. Watson's research interests include numerical analysis; nonlinear programming; mathematical software; solid mechanics; fluid mechanics; image processing; parallel computation; bioinformatics.
Bio ItemLizette Zietsman , bio
Professor Zietsman's research area covers the development and analysis of fundamental numerical algorithms arising in the study of stability, control and estimation of distributed parameter systems typical in structural control, fluid flow control, and thermal systems.
Researchers in Modeling, Control & Optimization
Bio ItemAndrea Carracedo Rodriguez , bio
Dr Carracedo Rodriguez conducts research in numerical analysis, with a focus on efficiently building approximations to dynamical systems from data or via model reduction.