Title: Modeling Zoonotic Infectious Diseases from Wildlife to Humans Speaker: Prof. Linda J. S. Allen, P. W. Horn Distinguished Professor Emeritus Texas Tech University Abstract: Zoonotic infectious diseases are diseases transmitted from animals to […]
Title: Solving the Race in Backgammon Speaker: Prof. Arthur Benjamin Smallwood Family Professor of Mathematics Harvey Mudd College Abstract: Backgammon is perhaps the oldest game that is still […]
Title: Exploiting metric structure for more accurate classification Speaker: Mike Izbicki, Department of Mathematical Sciences, Claremont McKenna College Abstract: Classification problems often have many semantically similar classes. For example, the famous ImageNet dataset […]
Title: Interrupted Time Series Models for Assessing Complex Health Care Interventions Maricela Cruz, PhD Assistant Investigator Biostatistics Unit Kaiser Permanente Washington Health Research Institute Abstract: Assessing the impact of complex interventions on measurable health outcomes is a growing concern in health care and health policy. According to the 2018 Annual Review of Public Health, interrupted time […]
Title: Quantitative Approaches to Social Justice Prof. Chad Topaz (he/him/his) Co-Founder and Executive Director of Research, QSIDE Institute Professor of Mathematics, Williams College Abstract: Civil rights leader, educator, and investigative […]
Title: Topic Models, Methods, and Medicine Speaker: Prof. Jamie Haddock (Harvey Mudd College) Abstract: There is currently an unprecedented demand for efficient, quantitative, and interpretable methods to study large-scale (often […]
Abstract: This talk discusses multiple methods for clustering high-dimensional data, and explores the delicate balance between utilizing data density and data geometry. I will first present path-based spectral clustering, a novel approach which combines a density-based metric with graph-based clustering. This density-based path metric allows for fast algorithms and strong theoretical guarantees when clusters concentrate […]
Abstract: Classification is a fundamental task in data science and machine learning, and in the past ten years there have been significant improvements on classification tasks (e.g. via deep learning). […]
In this talk, we present reduced order models (ROMs) for turbulent flows, which are constructed by using ideas from large eddy simulation (LES) and variational multiscale (VMS) methods. First, we […]
Abstract: The system of shallow water equations and related models are widely used in oceanography to model hazardous phenomena such as tsunamis and storm surges. Unfortunately, the inherent uncertainties in […]
Abstract: I will overview the following different wave phenomena in integrable nonlinear wave equations: (1) universal patterns in the dynamics of fluxon condensates in the semi-classical limit; (2) […]
TBA
