Chelluri Lecture Series

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The Chelluri Lecture series is offered in memory of Thyagaraju (Raju) Chelluri, who graduated magna cum laude from Cornell with a Bachelor's degree in mathematics in 1999. Raju was a brilliant student, a gifted scholar, and a wonderful human being who died on August 21, 2004 at the age of 26, shortly after completing all requirements for the Ph.D. in Mathematics at Rutgers University. He wrote a thesis called Equidistribution of the Roots of Quadratic Congruences under the supervision of H. Iwaniec and was awarded a Ph.D. posthumously.

The Chelluri Lecture Endowment was established in 2004 with support from family and friends of Thyagaraju (Raju) Chelluri. Each year, a distinguished mathematician will be invited to give the Chelluri Lecture.

Upcoming Lectures

The next lecture in the series is September 23rd, 2021 at 4:30 p.m. in room 251 Malott Hall.

Speaker:  David Bindel, Associate Professor of Computer Science and Director, Center for Applied Math

Title:  New Approaches to computing with kernels

Abstract:  There are many good methods for approximating smooth functions on an interval by interpolating data points, but only a few of these generalize well to smooth functions on a higher-dimensional space.  One such method involves splines, which take on the shape of a beam (for one dimension) or a plate (for two dimensions) that passes through the data points and otherwise is at equilibrium.  Computing with this approach involves a Greens function or kernel that relates a displacement of the surface at one point in space to forces experienced at another points in space.  The same style of approximation appears under different guises in many areas of applied mathematics, from the statistical modeling of spatial phenomena to the methods of modern machine learning.

Unfortunately, standard algorithms for choosing and fitting of kernel approximants scale cubically with the number of input data, and so are prohibitively expensive when there are more than a few thousand data points.  New algorithms are needed to address these problems in a scalable manner.  In this talk, we discuss recent work on such tools, with an emphasis on the interplay between the different perspectives coming from approximation theory, statistics, and numerical linear algebra.

 

A reception will follow at the A.D. White House at approximately 5:45 p.m.

More Information will be posted here as it becomes available. 

If you need accommodations to participate in this event, please contact Heather Peterson.

Previous Lectures in the Series