אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

The potential for machine learning to revolutionize scientific and engineering research is immense, but its transformative power cannot be fully harnessed through the use of off-the-shelf tools alone. To unlock this potential, novel methods are needed to integrate physical models and constraints into learning systems, accelerate simulations, and quantify model prediction uncertainty. In this presentation, we will explore the opportunities and emerging tools available to address these challenges in the context of inverse problems, data assimilation, and reduced order modeling. By leveraging ideas from statistics, optimization, scientific computing, and signal processing, we can develop new and more effective machine learning methods that improve predictive accuracy and computational efficiency in the natural sciences. Short Bio: Professor of Statistics and Computer Science & Director of AI at the Data Science Institute, with a courtesy appointment at the Toyota Technological Institute at Chicago. Faculty lead of AI+Science Postdoctoral Fellow program. Prof. Willett completed her Ph.D. in Electrical and Computer Engineering at Rice University in 2005 and was an Assistant then tenured Associate Professor of Electrical and Computer Engineering at Duke University from 2005 to 2013. She was an Associate Professor of Electrical and Computer Engineering, Harvey D. Spangler Faculty Scholar, and Fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison from 2013 to 2018. Willett has also held visiting researcher or faculty positions at the University of Nice in 2015, the Institute for Pure and Applied Mathematics at UCLA in 2004, the University of Wisconsin-Madison 2003-2005, the French National Institute for Research in Computer Science and Control (INRIA) in 2003, and the Applied Science Research and Development Laboratory at GE Healthcare in 2002.