March Luncheon Meeting (Goleta) 3/13/19

Machine Learning in the Sciences and Engineering: Challenges and Opportunities

There have been many exciting developments recently in the fields of data science and machine learning. Natural Language Processing (NLP) and Image Processing (IP) are advancing rapidly. However, problems faced in science and engineering often have different requirements and inherent structures than those typically solved with NLP and IP. This talk will survey trends and emerging areas in machine learning, along with challenges and opportunities they provide in the sciences and engineering.

Dr. Paul J. Atzberger, Professor, UCSB Mathematics and UCSB Mechanical Engineering, studied mathematics at the Courant Institute at New York University where he received his PhD. Subsequently, he was a postdoctoral fellow at Rensselaer Polytechnic Institute. He joined the faculty at the University of California Santa Barbara in 2006. His research is in the areas of computation, numerical methods, stochastic analysis, and their applications in the sciences and engineering. He also has related research interests in data science and machine learning.