## Seminars

## Applied Mathematics Colloquium - Steffen Borgwardt

Sept. 30, 2022

Steffen Borgwardt, Department of Mathematics, University of Colorado Denver Transitions between Clusterings Clustering is one of the fundamental tasks in data analytics and machine learning. In many situations, different partitions of the same data set become relevant. For example, different algorithms for the same clustering task may return dramatically different...

## Applied Mathematics Colloquium - Xudong Chen

Sept. 16, 2022

Xudong Chen; Department of Electrical, Computer and Energy Engineering; University of Colorado Boulder Structure Theory for Nonholonomic Ensemble Systems Ensemble control deals with the problem of using a common control input to simultaneously steer a large population (in the limit, a continuum) of individual control systems. In this talk, we...

## Applied Mathematics Colloquium - Farhad Pourkamali-Anaraki

Sept. 8, 2022

Farhad Pourkamali-Anaraki, Department of Mathematical and Statistical Sciences, University of Colorado Denver Evaluation of Classification Models in Limited Data Scenarios with Application to Additive Manufacturing Scientific observations and experiments provide valuable data to build machine learning (ML) models that reveal links between input variables and quantities of interest. Specifically, adopting...

## Applied Mathematics Colloquium - Daniel Larremore

Sept. 2, 2022

Daniel Larremore, Department of Computer Science, University of Colorado Boulder Estimating the Mitigation Potential of Screening Programs for Infectious Diseases The premise of screening programs for infectious diseases is that screening tests taken on the individual level have effects on population transmission. How can we estimate those effects? What data...

## Applied Mathematics Colloquium - George Karniadakis

April 8, 2022

Applied Mathematics Colloquium - George Karniadakis George Karniadakis, Department of Applied Mathematics, Brown University From Neural PDEs to Neural Operators: Blending data and physics for fast predictions Abstract: We will review physics-informed neural network and summarize available extensions for applications in computational mechanics and beyond. We will also introduce new...

## Applied Mathematics Colloquium - Alex Townsend

April 1, 2022

Alex Townsend, Department of Mathematics, Cornell University The art and science of low-rank techniques Matrices and tensors that appear in computational mathematics are so often well-approximated by low-rank objects. Since random ("average") matrices are almost surely of full rank, mathematics needs to explain the abundance of low-rank structures. We will...

## Applied Mathematics Colloquium - Habib Ammari

March 18, 2022

Habib Ammari, Department of Mathematics, Eidgenössische Technische Hochschule Zürich, Switzerland Functional analytic methods for discrete approximations of subwavelength resonator systems In this lecture, the speaker will review mathematical and computational frameworks to elucidate physical mechanisms for manipulating waves in a robust way at scales beyond the diffraction limit using subwavelength...

## Applied Mathematics Colloquium - Bard Ermentrout

March 4, 2022

Bard Ermentrout, Department of Mathematics, University of Pittsburgh Phase in Space: Spatiotemporal dynamics of nonlocally coupled oscillators The ability of neuroscientists to record large regions of the brain at high temporal resolution has demonstrated that neuronal oscillations are not synchronized, but rather, organized into spatio-temporal patterns such as plane- and...

## Applied Mathematics Colloquium - Lexing Ying

Feb. 25, 2022

Lexing Ying, Department of Mathematics, Stanford University Prony's method, analytic continuation, and quantum signal processing Prony's method is a powerful algorithm for identifying frequencies and amplitudes from equally spaced signals. It is probably not as well-known as it should have been. In the first part of the talk, we will...

## Applied Mathematics Colloquium - Nathan Kutz

Feb. 18, 2022

Nathan Kutz, Department of Applied Mathematics, University of Washington The Future of Governing Equations Machine learning and AI algorithms are transforming a diverse number of fields in science and engineering. This is largely due their success in model discovery which turns data into reduced order models and neural network representations...