Blending Data and Models: Kalman Based Approaches

Name: Blending Data and Models: Kalman Based Approaches
Start: 2020-09-10T15:00:00-04:00
End: 2020-09-10T16:00:00-04:00
Location: Zoom

Thursday, September 10, 2020 3pm to 4pm

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In 1960 Rudolph Kalman published a paper that has had a lasting and substantial impact on the use of data to improve the predictive capability of mathematical models: it has been cited around 35,000 times (Google Scholar, August 2020). In this talk, I will describe the methodology, highlight the wide range of application areas that it has impacted, and explain an interesting mathematical structure that has been developed to interpret it.

Event Details

Event Type

Colloquia / Seminar / Lecture

Series

Widely Applied Mathematics Seminars

Academics

Applied Mathematics

Audience

General Public

Speaker 1

Andrew Stuart, Professor of Computing and Mathematical Sciences, Caltech

Speaker Bio

Andrew Stuart has research interests in applied and computational mathematics and is interested, in particular, in the question of how to optimally combine complex mechanistic models with data. He joined Caltech in 2016 as the Bren Professor of Computing and Mathematical Sciences after 17 years as a professor of mathematics at the University of Warwick (1999--2016). Prior to that, he was on the faculty in the departments of Computer Science and Mechanical Engineering at Stanford University (1992--1999) and in the Mathematics Department at Bath University (1989--1992). He obtained his Ph.D. from the Oxford University Computing Laboratory in 1986 and held postdoctoral positions in mathematics at Oxford University and MIT from 1986 to 1989.

Host

L Mahadevan

Contact

Nick Grall

ngrall@seas.harvard.edu

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