My research focus is at the intersection of robotics, control theory, biomechanics, and rehabilitation. I am interested in the control of powered lower-limb rehabilitation and assistive robots that can restore locomotion for people with reduced mobility. The development of novel control frameworks that generalize across users and locomotion activities will be critical to improving the lives of nearly 2 million people living with limb loss, 6 million stroke survivors in the U.S., and our aging population. 

During my postdoc, I worked on developing a robust and adaptive model of a spring-loaded inverted pendulum capable of traversing terrains with varying slopes, including stairs of different inclinations. This model could serve as a model-based predictor for lower-limb wearable robots, enabling the assessment of  stability of generated motions. Concurrently, I collaborated with a PhD student to create machine learning models for an extensive predictor, capable of  forecasting swing leg motions during steady and transitional walking. During my PhD, I developed a theoretical control framework for lower-limb wearable robots that enables them to adapt to changing walking conditions while ensuring stability of the closed-loop dynamical system. 

By integrating the principles from three distinct research thrusts- models of legged locomotion, learning algorithms, and control theory - I would like to create a holistic framework for control design of lower-limb wearable robots.