As a researcher with over a decade of experience, I specialize in designing and controlling agile, efficient, and adaptive robots. My work improves assistive, humanoid, and industrial robotics, providing these fields with advanced capabilities.I have led and inspired research teams in the US and Singapore, mentoring dedicated students and researchers to solve some of the toughest problems in robotics. My research has developed real-time optimal control algorithms, explored new theories of robot and human movement, and innovated compliant robots and actuators. Supported by multi-million dollar funding in both the US and Singapore, I received the 2022 NSF CAREER Award and the 2014 IEEE King-Sun Fu Transactions on Robotics Best Paper Award.I thrive on multidisciplinary research. My key contributions include:• Constrained Iterative Linear Quadratic Regulator: Validated on the DLR David robot, this method improves control for complex robotic systems.• Hardware-in-the-Loop Optimal Control Method: Enables robot learning without precise models, allowing optimal control with limited data.• Theory of Human Augmentation: Mechanically adaptive exoskeletons predict a 100% increase in human jump height, doubled weight-bearing capacity, and a 50% increase in running speed.• Energetically-Conservative Variable Stiffness Springs: New mechanisms enhancing adaptation in assistive, legged, and industrial robots.• Controllers for Legged Robots: Algorithms that enable robot walking similar to humans.Passionate about advancing robotics, I invite you to explore these works on my website: davidbraunrobots.com.Skills: Optimization Theory and Methods, Control Systems, Optimal Control, Theoretical and Applied Mechanics, Solid and Rigid Body Mechanics, Dynamical Systems, Design Innovation, Compliant Actuators, Robot Exoskeletons, Legged Robots, Numerical Methods, ODE and DAE Integration Methods, MATLAB Programming, Scientific Writing, Effective Communication