Sam Otto joins Cornell from the University of Washington’s AI Institute in Dynamic Systems. Otto’s work sits at the intersection of machine learning and fluid mechanics, with a focus on developing data-driven models and algorithms that can predict and control complex systems like fluid flows. His research aims to make these models more reliable and efficient for use in practical engineering applications.
Otto’s research also delves into the theoretical aspects of scientific machine learning, exploring how physical principles – such as symmetry and smoothness – can be leveraged to enable more data-efficient learning. He hopes to accelerate the development of algorithms that can simulate and solve high-dimensional nonlinear systems in real-time.
“My work is about creating intelligent systems that can make better predictions about fluid flows and other complex phenomena,” says Otto. “Cornell’s emphasis on cutting-edge research and its world-class facilities provide an ideal environment to develop these next-generation tools.”