I am a postdoctoral researcher at Harvard University.
My research interests include robotics, dynamics, and controls with a specific focus on soft robots.
Currently, I am exploring physics-based and data-driven modeling and control methods for soft robots in an effort to make such systems more capable and reliable.
I am originally from Farmington Hills, Michigan and received a B.S. in Engineering Sciences from Harvard University in 2013.
After graduating, I worked as a math teacher at the Jalen Rose Leadership Academy in Detroit, then earned a PhD in mechanical engineering at the University of Michigan in 2020.
- Paper accepted to IROS + RAL (Jun. 17, 2021)
Our paper, "Koopman-based Control of a Soft Continuum Manipulator Under Variable Loading Conditions," by Daniel Bruder, Xun Fu, and Ram Vasudevan was accepted to IROS and IEEE Robotics and Automation Letters.
- Paper accepted to ICRA + RAL (Feb. 21, 2021)
Our paper, "Advantages of Bilinear Koopman Realizations for the Modeling and Control of Systems with Unknown Dynamics," by Daniel Bruder, Xun Fu, and Ram Vasudevan was accepted to ICRA and IEEE Robotics and Automation Letters.
- Paper accepted to IEEE TRO (Oct. 13, 2020)
Our paper, "Data-driven Control of Soft Robots Using Koopman Operator Theory," by Daniel Bruder, Xun Fu, R. Brent Gillespit, C. David Remy, and Ram Vasudevan was accepted into the IEEE Transactions on Robotics journal.
- Defended PhD Dissertation (Jul. 15, 2020)
I successfully defended my PhD dissertation entitled "Towards a Universal Modeling and Control Framework for Soft Robots."
- DIY ventilator design featured by the Michigan Engineering News Center (Apr. 17, 2020)
To help address the ventilator shortage caused by the COVID-19 pandemic, I developed a makeshift ventilator prototype that can be assembled primarily from readily available hospital equipment and requires no custom fabrication of parts.