Professor Beeson’s research blends aspects of optimal control, local and global optimization, dynamical systems, probability theory and learning to develop methods that produce a diverse set of solutions in an efficient (numerical) manner for global trajectory optimization problems. A main application of this research is towards low-thrust spacecraft trajectory planning in complex dynamical environments. He also performs research in the area of nonlinear filtering theory, with applications to spacecraft guidance, navigation and control, space situational awareness, as well as Earth and space weather estimation. His research in nonlinear filtering aims to bring ideas from control and information theory, stochastic dynamical systems, and learning together to develop more capable methods for filtering problems that may include properties of being: highly nonlinear, chaotic, or high dimensional.
Professor Beeson received his doctoral degree from the University of Illinois at Urbana-Champaign in Aerospace Engineering (’20), an M.S. in Mathematics (’20). He also holds an M.S. (’10) and B.S. (’08) in Aerospace Engineering from Illinois. He was a senior scientist for CU Aerospace L.L.C., where he directed research on development of numerical optimization algorithms and spacecraft trajectory software. He has previously worked in research divisions at Caterpillar Inc.