Machine Learning in Science & Engineering (MLSE) will demonstrate how data-driven approaches can help solve emerging challenges, and will showcase innovative thinking from a diverse range of scientific and technological disciplines. In this two day, virtual conference, representatives from academia, government, and industry will explore the future of science and engineering across 11 dedicated tracks.
William Dally, Chief Scientist & Senior VP of Research, Nvidia; Professor-Research, Computer Science & Electrical Engineering, Stanford University
Barbara Engelhardt, Associate Professor, Department of Computer Science, Princeton University
David W. Hogg, Group Leader, Flatiron Institute; and Professor of Physics & Data Science, Department of Physics, New York University
Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, and Statistical Science, Duke University
MLSE Tracks
Astronomy, Astrophysics, and Physics
Biology
Health Sciences
Chemistry, Chemical Engineering, and Materials Science
Computing Systems
Earth and Environmental Sciences
Mechanical Engineering, Engineering Mechanics, and Civil Engineering
Methods and Algorithms
Neuroscience
Quantum
Transportation
