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Venkat Chandrasekaran is an applied mathematician whose research interests span optimization and information sciences. His specific areas of focus include convex optimization, statistical inference, inverse problems, graphs, combinatorial optimization, applied algebraic geometry, and approximation theory. His research group investigates the mathematical foundations of these topics, aiming to develop principled methods and applications across science and engineering domains. With a strong educational background, Venkat obtained his Bachelor of Arts and Bachelor of Science degrees from Rice University in 2005, followed by a Master of Science from the Massachusetts Institute of Technology in 2007. He completed his Ph.D. in 2011 and served as an Assistant Professor at Caltech from 2012 to 2017 before being promoted to Professor in 2017. In 2024, he was named the Tomiyasu Professor. Venkat has been actively involved in teaching, offering courses such as Great Ideas in Data Science and Mathematical Optimization.
California Institute of Technology • Pasadena, CA
Teaching and conducting research in Electrical Engineering and Applied Mathematics.
California Institute of Technology • Pasadena, CA
Engaged in teaching and research activities focusing on optimization and mathematical sciences.
Most Caltech graduate programs are PhD-only. GRE General and Subject tests vary by department; many have made them optional or no longer accept them.