He graduated with a B.S. in Mathematics from the California Institute of Technology and received his PhD in Mechanical and Aerospace Engineering from Princeton University. Currently, his research interests include data-driven modeling and control of complex dynamical systems using machine learning, compressed sensing, uncertainty quantification, and equation-free modeling; Flow control of unsteady aerodynamics, transport phenomena, and turbulent mixing enhancement; Adaptive and robust control techniques for energy optimization and conversion. He is a Data Science Fellow with the eScience Institute at UW.