Maegan received her PhD in Mechanical Engineering (ME) from the California Institute of Technology (Caltech) in May 2023. Prior, she also received a M.S. in ME from Caltech in 2019 and a B.S. in ME from Georgia Tech in 2017. After graduating with her PhD, Maegan conducted a brief postdoc at Caltech (May - August 2023), followed by a brief research position at Disney Research (September - December 2023). Generally speaking, her research interests lie at the intersection of control theory and human-robot interaction, with specific applications towards lower-limb assistive devices. Much of her research is centered around the question: “What is the right way to walk?”. In her free time, Maegan enjoys puzzles, playing video games, and the piano.
- Lower-Body Assistive Devices
- Bipedal Locomotion
- Nonlinear Control Theory
- Human-Robot Interaction
- Preference-Based Learning
- Human Biomechanics
- Dean’s Jr. Professorship
- Caltech Centennial Prize for the Best Thesis in Mechanical and Civil Engineering award (2023)
- Caltech Simoudis Discovery Prize (2022)
- Berkeley ME Rising Star (2020)
- ICRA 2020 Best Overall Paper (Awarded)
- ICRA 2020 Best Paper in Human-Robot Interaction (Awarded)
- NSF Graduate Research Fellow (2019-2022)
- Tucker, M.*, Novoseller, E.*, et al. "Preference-Based Learning for Exoskeleton Gait Optimization." In 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020. (*Denotes equal contribution) [https://arxiv.org/pdf/1909.12316.pdf]
- Tucker, M., Csomay-Shanklin, N., Ma, W., & Ames, A. D. "Preference-based learning for user-guided HZD gait generation on bipedal walking robots." In 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021. [https://arxiv.org/pdf/2011.05424.pdf]
- Tucker, M., Csomay-Shanklin, N., and Ames, A. D. “Robust Bipedal Locomotion: Leveraging Saltation Matrices for Gait Optimization.” In 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023. [https://arxiv.org/pdf/2209.10452.pdf]
- Tucker, M., and Ames, A. D. “An input-to-state stability perspective on robust locomotion.” IEEE Control Systems Letters. 2023. [https://arxiv.org/pdf/2303.10231.pdf]