Dr. Du Toit received a Ph.D. degree from the California Institute of Technology in Control and Dynamical Systems in 2009. He also received a M.S. degree from the University of Michigan in Applied Mathematics, and an Honors B.S. degree in Physics summa cum laude from Brigham Young University. During his ﬁve years as a Senior Research Scientist and Program Manager at Numerica Corporation, Dr. Du Toit has led teams in fundamental scientiﬁc research and software development. His work in algorithm development draws on wide-ranging ﬁelds of applied mathematics including estimation, combinatorial optimization, optimal control, convex analysis, continuation methods, and dynamical systems. Applications of these techniques have been primarily in the areas of optimal mission planning for unmanned aerial systems, optimal design of spacecraft trajectories, and latent pattern detection on networks.
In the machine learning ﬁeld, Dr. Du Toit has used L1 methods to apply learning approaches to identify hidden patterns and anomalies on noisy computer networks. The approach provides rigorous guarantees for recovery of latent low-dimensional structure, while at the same time providing a fast algorithmic framework. The complexity of the algorithms scale as the dimension of the latent low-dimensional structure making the approach suitable for analysis of very large data sets typical of today’s ‘big-data’ environments. He is an author of a pending U.S. patent titled ”Pattern Detection in Sensor Networks”.
Dr. Du Toit has also developed stochastic optimization algorithms on SBIR research programs funded by the US Navy to improve mission planning and unmanned aerial systems subject to safety criteria, and NASA for which he developed algorithms for design of optimal spacecraft trajectories.
Ph.D. – Control and Dynamical Systems, California Institute of Technology
M.S. – Applied Mathematics, University of Michigan
B.A. – Physics, Brigham Young University