Contact

Phone: (818) 354 2818
Email: shahrouz.r.alimo@jpl.nasa.gov
Office: 144-204

Mail Stop: 144-210
4800 Oak Grove Drive
Pasadena, CA 91109

Education

Postdoc Scholar - California Institute of Technology
Computational Science (Ph.D.) – UC San Diego
Mechanical Eng. Specialization in High Performance Computing (M.Sc.) - UC San Diego
Mechanical Engineering (B.Sc.) – Sharif University of Technology

Research Interests

Integration of Deep Learning and optimization, Vision-based navigation, Formation Flying Spacecraft, Autonomy

Publications

Alimo, R., Beyhaghi, P., & Bewley, T. R. (2020) Delaunay-based derivative-free optimization via global surrogates. Part III: nonconvex constraints. Journal of Global Optimization, 1-34.

ABeyhaghi, P., Alimo, R., & Bewley, T. (2020) A derivative-free optimization algorithm for the efficient minimization of functions obtained via statistical averaging. Computational Optimization and Applications, 1-31.

Sonawani, S., Alimo, R., Detry, R., Jeong, D., Hess, A., & Amor, H. B. (2020) Assistive Relative Pose Estimation for On-orbit Assembly using Convolutional Neural Networks. arXiv preprint arXiv:2001.10673

Biyik, E., Margoliash, J., Alimo, S. R., & Sadigh, D. (2019, July) Efficient and safe exploration in deterministic markov decision processes with unknown transition models. In 2019 American Control Conference (ACC) (pp. 1792-1799). IEEE.

Capuano, V., Alimo, S. R., Ho, A. Q., & Chung, S. J. (2019) Robust features extraction for on-board monocular-based spacecraft pose acquisition. In AIAA Scitech 2019 Forum (p. 2005)

Alimo, R., Cavaglieri, D., Beyhaghi, P., & Bewley, T. R. (2020) Design of IMEXRK time integration schemes via Delaunay-based derivative-free optimization with nonconvex constraints and grid-based acceleration. Journal of Global Optimization, 1-25.

Zhao, M., Alimo, S. R., & Bewley, T. R. (2018, December) An active subspace method for accelerating convergence in Delaunay-based optimization via dimension reduction. In 2018 IEEE Conference on Decision and Control (CDC) (pp. 2765-2770). IEEE.

Lakhmiri, D., Alimo, R., & Digabel, S. L. (2020) Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system. arXiv preprint arXiv:2006.03962.