UNIVERSITY PARK, Pa. – Penn State faculty members and students are invited to a three-day virtual workshop from Dec. 14 to 16 on the mathematical basis of machine learning, a form of artificial intelligence that is widely used to advance science and operations in many industries and disciplines today. Penn State’s Center for Computational Mathematics and Applications is hosting the workshop, which is open to the public. Interested attendees must register by Dec. 7.
The workshop organizers hope to address fundamental questions related to machine learning, including how to understand machine learning’s successes in mathematically rigorous ways while taking into account theories on approximation and probability. The workshop seeks to expand applications of machine learning to new fields and areas by exploring whether it can be used to address known problems related to mathematics, including numerical analysis, density estimation, sampling methodology and uncertainty quantification.
“This is a forward-looking workshop, and we hope that attendees can leave this workshop with a better understanding of the potential for machine learning to address existing challenges in the mathematical sciences, which would have an impact in many more disciplines as a result,” said John Harlim, one of the workshop co-chairs, and a professor of mathematics and meteorology and atmospheric science, and faculty co-hire of Penn State’s Institute for Computational and Data Sciences, Penn State
The workshop is bringing together global cohort of prominent mathematicians from myriad institutions, including Stanford University, Massachusetts Institute of Technology, California Institute of Technology, Princeton, Penn State, the National University of Singapore, ETH Zurich, Peking University, Duke University, King Abdullah University of Science, and the Courant Institute, among others.
Anyone interested in participating or contributing related works through a zoom poster presentation should visit the Workshop on Mathematical Machine Learning and Application website.