Accelerating Engineering with Machine Learning

Advances in machine learning are having exciting impacts in a variety of domains from neuroscience to chemistry to astronomy. Machine learning has been somewhat slow to interface with more traditional engineering domains such as mechanical, civil, and chemical engineering. Nevertheless, there is huge potential for ML to accelerate everything in the engineering design cycle: CAD, simulation, fabrication, and control. In this talk I will discuss some of the exciting work starting to happen at this interface—from deep generative modeling to differentiable physical simulation---and take a look forward at what might be possible.

Ryan Adams is a machine learning researcher and Professor of Computer Science at Princeton University. Ryan completed his Ph.D. in physics under David MacKay at the University of Cambridge, where he was a Gates Cambridge Scholar and a member of St. John's College. Following his Ph.D. Ryan spent two years as a Junior Research Fellow at the University of Toronto as a part of the Canadian Institute for Advanced Research. From 2011-2016, he was an Assistant Professor at Harvard University in the School of Engineering and Applied Sciences. In 2015, Ryan sold the company he co-founded, Whetlab, to Twitter and he spent three years in industry at Twitter and Google before joining the faculty at Princeton in 2018. Ryan has won paper awards at ICML, UAI, and AISTATS, received the DARPA Young Faculty Award and the Alfred P. Sloan Fellowship. He also co-hosted the popular Talking Machines podcast.