Paul Reverdy
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Assistant Professor of Aerospace and Mechanical Engineering
Paul Reverdy received the B.S. degree in engineering physics and the B.A. degree in applied mathematics from the University of California, Berkeley, Berkeley, CA, USA, in 2007 and the M.A. and Ph.D degrees in mechanical and aerospace engineering from Princeton University, Princeton, NJ, USA, in 2011 and 2014, respectively. From 2007 to 2009, he worked as a Research Assistant at the Federal Reserve Board of Governors, Washington, DC, USA. From 2014-17 he was a Postdoctoral Fellow with the Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA, before joining the University of Arizona in 2018.
Degrees
- Ph.D. Mechanical and Aerospace Engineering
- Princeton University, Princeton, New Jersey, United States
- Human-inspired algorithms for search
- M.A. Dynamics and Control
- Princeton University, Princeton, New Jersey, United States
- B.S. Engineering Physics
- University of California, Berkeley, Berkeley, California, United States
- B.A. Applied Mathematics
- University of California, Berkeley, Berkeley, California, United States
Courses
Dynamics
AME 250 (Spring 2019)
AME 250 (Spring 2018)
Directed Research
AME 492 (Spring 2019)
AME 492 (Fall 2018)
Intro Adv Linear Control Thry
AME 558 (Fall 2018)
Research
AME 900 (Spring 2019)
AME 900 (Fall 2018)
Selected Publications
Journals/Publications
- Reverdy, P. B., & Koditschek, D. E. (2018). A dynamical system for prioritizing and coordinating motivations. SIAM Journal on Applied Dynamical Systems, 17(2), 1683--1715.
Proceedings Publications
- Reverdy, P. (2018, October). Decision mechanisms from cognitive science for human-robot learning. In Workshop on Human/Robot in-the-loop Machine Learning, International Conference on Intelligent Robots and Systems (IROS).
- Reverdy, P., & Srivastava, V. (2018, April). Multi-armed bandits for human-machine decision making. In IEEE International Conference on Acoustics, Speech and Signal Processing.
- Reverdy, P., Landgren, P., Srivastava, V., & Leonard, N. (2018, May). Multirobot foraging using the graphical multiarmed bandit framework. In IEEE Conference on Robotics and Automation (ICRA) 2018, Workshop on Informative Path Planning and Adaptive Sampling.