Ph.D. Candidate, Applied Mathematics and Statistics

University of California Santa Cruz

I work at the exciting interface between machine learning and dynamical systems.
Specifically, I seek novel ways to apply machine learning tools for modeling and computational optimal control of high-dimensional nonlinear systems under uncertainty.
More generally, 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.

Email: tenakamu AT ucsc DOT edu

LinkedIn

W. Kang, Q. Gong, and T. Nakamura-Zimmerer. *Algorithms of data development for deep learning and feedback design.*
arXiv: 1912.00492, 2019.

T. Nakamura-Zimmerer, D. Venturi, Q. Gong, and W. Kang. *Density propagation with characteristics-based deep learning.*
arXiv: 1911.09311, 2019.

T. Nakamura-Zimmerer, Q. Gong, and W. Kang. *Adaptive deep learning for high-dimensional Hamilton-Jacobi-Bellman equations.*
arXiv: 1907.05317, 2019.

*Adaptive learning for optimal feedback control of high-dimensional nonlinear systems.*
Advancement to Candidacy Presentation, University of California, Santa Cruz CA, 2019.

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

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

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

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