#### About me

I study computational optimal control of nonlinear dynamical systems under uncertainty. My work involves using machine learning to efficiently build approximations of flow maps and time-dependent probability distributions of high-dimensional nonlinear systems, which are then integrated into the optimization loop. In general, I am interested in computational optimal control, dynamical systems, optimization, machine learning, and aerospace applications.

I recieved my B.A. from UC Santa Cruz with a double major in pure mathematics and language studies in 2015. Since 2016, I have been working towards a Ph.D. in applied mathematics and statistics, also at UC Santa Cruz.

#### Contact

Email: tenakamu AT ucsc DOT edu

LinkedIn

#### Presentations

Data-driven approximation of high-dimensional flow maps and probability distributions using deep neural networks, given at SIAM Conference on Computational Science and Engineering 2019, Spokane, Washington.

Deep learning for probability density and flow map approximation of high dimensional nonlinear systems, given at Bay Area Scientific Computing Day 2018, Sandia National Laboratories, Livermore, California.

Data-driven computational optimal control for nonlinear systems under uncertainty, given at SIAM Annual Meeting 2018, Portland Oregon.

Copyright Tenavi Nakamura-Zimmerer, 2019. Website under construction.