# Mathematics and Statistics Colloquium (April 20)

Speaker: Dr. Rebecca Willett (University of Chicago)

Time and location: Thursday, April 20th, 4:00-5:00pm, IES 110 (Tea/coffee beforehand at 3:30pm on BVM 5th floor)

Title: Machine learning and data assimilation in the natural sciences and engineering

Abstract: 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.

About the speaker: Dr. Willett is a 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. She is also the faculty lead of AI+Science Postdoctoral Fellow program. Prof. Willett’s work in machine learning and signal processing reflects broad and interdisciplinary expertise and perspectives. She is known internationally for her contributions to the mathematical foundations of machine learning, large-scale data science, and computational imaging. In addition to her technical contributions, Prof. Willett is a strong advocate for diversity in STEM and AI and has organized multiple events to support women in middle school, as undergraduate and graduate students, and as faculty members.

Speaker: Dr. Rebecca Willett (University of Chicago)

Time and location: Thursday, April 20th, 4:00-5:00pm, IES 110 (Tea/coffee beforehand at 3:30pm on BVM 5th floor)

Title: Machine learning and data assimilation in the natural sciences and engineering

Abstract: 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.

About the speaker: Dr. Willett is a 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. She is also the faculty lead of AI+Science Postdoctoral Fellow program. Prof. Willett’s work in machine learning and signal processing reflects broad and interdisciplinary expertise and perspectives. She is known internationally for her contributions to the mathematical foundations of machine learning, large-scale data science, and computational imaging. In addition to her technical contributions, Prof. Willett is a strong advocate for diversity in STEM and AI and has organized multiple events to support women in middle school, as undergraduate and graduate students, and as faculty members.