Tuesday, July 18, 2023

Dr. Krithika Manohar is an Assistant Professor of Mechanical Engineering at the University of Washington. She works at the intersection of machine learning, dynamical systems and control, developing efficient data-driven algorithms for optimal sensor placement, forecasting, and smart manufacturing. She received her Ph.D. in Applied Mathematics at the University of Washington in 2018, followed by a Postdoctoral position and von Karman Instructorship at Caltech.

Her research leverages dimensionality reduction tools rooted in operator theory and manifold learning to discover physically meaningful features from data, and optimize sensors and actuators for downstream decision-making (sparse sensing). Target applications of sparse sensing optimization include fluid flow reconstruction, image recovery, and aircraft manufacturing. She is also interested in sparse sensing and forecasting in the context of partially observed multiscale systems, which commonly occur in fluid dynamics, materials science, and biology.

https://www.me.washington.edu/facultyfinder/krithika-manohar

Dr. Krithika Manohar is an Assistant Professor of Mechanical Engineering at the University of Washington. She works at the intersection of machine learning, dynamical systems and control, developing efficient data-driven algorithms for optimal sensor placement, forecasting, and smart manufacturing. She received her Ph.D. in Applied Mathematics at the University of Washington in 2018, followed by a Postdoctoral position and von Karman Instructorship at Caltech.

Her research leverages dimensionality reduction tools rooted in operator theory and manifold learning to discover physically meaningful features from data, and optimize sensors and actuators for downstream decision-making (sparse sensing). Target applications of sparse sensing optimization include fluid flow reconstruction, image recovery, and aircraft manufacturing. She is also interested in sparse sensing and forecasting in the context of partially observed multiscale systems, which commonly occur in fluid dynamics, materials science, and biology.

https://www.me.washington.edu/facultyfinder/krithika-manohar

Grant Rotskoff is an Assistant Professor of Chemistry at Stanford. He studies the nonequilibrium dynamics of living matter with a particular focus on self-organization from the molecular to the cellular scale. His work involves developing theoretical and computational tools that can probe and predict the properties of physical systems driven away from equilibrium. Recently, he has focused on characterizing and designing physically accurate machine learning techniques for biophysical modeling.

Prior to his current position, Grant was a James S. McDonnell Fellow working at the Courant Institute of Mathematical Sciences at New York University. He completed his Ph.D. at the University of California, Berkeley in the Biophysics graduate group supported by an NSF Graduate Research Fellowship. His thesis, which was advised by Phillip Geissler and Gavin Crooks, developed theoretical tools for understanding nonequilibrium control of the small, fluctuating systems, such as those encountered in molecular biophysics. He also worked on coarsegrained models of the hydrophobic effect and self-assembly. Grant received an S.B. in Mathematics from the University of Chicago, where he became interested in biophysics as an undergraduate while working on free energy methods for large scale molecular dynamics simulations. He is a recipient of the Department of Energy Early Career Award and a Google Research Scholar Award in Machine Learning.

https://chemistry.stanford.edu/people/grant-m-rotskoff

Grant Rotskoff is an Assistant Professor of Chemistry at Stanford. He studies the nonequilibrium dynamics of living matter with a particular focus on self-organization from the molecular to the cellular scale. His work involves developing theoretical and computational tools that can probe and predict the properties of physical systems driven away from equilibrium. Recently, he has focused on characterizing and designing physically accurate machine learning techniques for biophysical modeling.

Prior to his current position, Grant was a James S. McDonnell Fellow working at the Courant Institute of Mathematical Sciences at New York University. He completed his Ph.D. at the University of California, Berkeley in the Biophysics graduate group supported by an NSF Graduate Research Fellowship. His thesis, which was advised by Phillip Geissler and Gavin Crooks, developed theoretical tools for understanding nonequilibrium control of the small, fluctuating systems, such as those encountered in molecular biophysics. He also worked on coarsegrained models of the hydrophobic effect and self-assembly. Grant received an S.B. in Mathematics from the University of Chicago, where he became interested in biophysics as an undergraduate while working on free energy methods for large scale molecular dynamics simulations. He is a recipient of the Department of Energy Early Career Award and a Google Research Scholar Award in Machine Learning.

https://chemistry.stanford.edu/people/grant-m-rotskoff