Sam Otto

Sam Otto is an Assistant Professor in the Mechanical Engineering department at Cornell University. His research focuses on recent advances in machine learning and artificial intelligence, which offer promising tools to efficiently model, forecast, and control high-dimensional nonlinear systems such as fluid flows using data from experiments and high-fidelity simulations. Dr. Otto’s work addresses the need to establish the reliability of data-driven models before they can be applied in engineering contexts. His profile page provides additional information about his research and academic activities.

Samuel E. Otto is a researcher in applied analysis, dynamical systems, machine learning, and model reduction. His work focuses on developing data-driven methods for modeling and controlling complex systems. Otto's research includes the application of Koopman operators for estimation and control, as well as the use of autoencoder networks for learning dynamics. He has also explored model predictive control using interpolated Koopman generators and symmetry-promoting regularization for dimension reduction. His publications emphasize nonlinear systems, reduced-order modeling, and the integration of machine learning with traditional analysis techniques to improve predictive accuracy and control performance.