Spring 2026 Recipients:
- Edward McDugald
- Sheila Whitman
Lectures to be scheduled in Fall 2026!
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The *Al Scott Lecture* was instituted in remembrance of our late colleague Alwyn C. Scott (1931 – 2006) and his pioneering contributions to the field of nonlinear science. Al Scott, a native of Worcester, Massachusetts, obtained a doctorate in Electrical Engineering from MIT in 1961. During the early 1970’s his research interests led to important contributions to the then emerging field of soliton mathematics and nonlinear wave propagation. He became one of the leading figures in the new field of nonlinear science and a founding editor of Physica D, the first journal devoted to the study of nonlinear phenomena. He was very interested in the role of nonlinear dynamics in modeling biological systems and, in particular, its applications to neuroscience. In addition to many scholarly papers on a wide variety of topics he wrote several books on neuroscience and nonlinear science, and was the editor of the comprehensive Encyclopedia of Nonlinear Science published in 2005. He joined the faculty of the Mathematics Department at the University of Arizona in 1985 and became a member of the University’s Program in Applied Mathematics. He retired from the University in 2000. His many contributions to the life of both the Program in Applied Mathematics and the Department of Mathematics were characterized by a civilized and good-humored approach to academic life. He was particularly encouraging of graduate students and it is this characteristic that is the basis for the Al Scott Lecture.
The annually awarded honor is made to a senior student in the Program in Applied Mathematics as part of the Applied Mathematics colloquium series.
Speaker: Jesse Adams
Speaker: Kevin Gomez
Speaker: Jessica Pillow
Date: April 23, 2021 | 2PM
Title: Bayesian Spatially Varying Multi-Regularization Image Deblurring
Abstract: Many scientific experiments such as those found in astronomy, geology, microbiology, and X-ray radiography require the use of high-energy instruments to capture images. Due to the imaging system, blur and added noise are inevitably present. Oftentimes the captured images must be deblurred to extract valuable information. In the presence of noise, image deblurring is an ill-posed inverse problem in which regularization is required to obtain useful reconstructions. Choosing the appropriate strength of the regularization, however, is difficult. Moreover, many images contain some mixture of smooth features and edges which requires the use of multi-regularization, i.e., the type of regularization (total variation or Tikhonov) varies across the image. We address these two issues by formulating the image deblurring problem within a hierarchical Bayesian framework, varying both the strength of the regularization, as well as the regularization type across the image. In this way, both the image and the strength of the regularization, which varies across the image, are described by a hierarchical posterior distribution which we can sample by Markov chain Monte Carlo (MCMC), in particular Gibbs samplers that make use of conditional distributions for efficient sampling. We compute the means of the image and parameter samples for simulated test images, and we compare our results with existing non-spatially-varying Bayesian methods to show that our new method both increases the quality and decreases the error of the image reconstruction.
Speaker: Hannah Kravitz
Speaker: Bill Fries
Speaker: Brian Bell
Date: April 26, 2024 | 3PM
Title: Wooly Graphs : Mathematics of knitting
Abstract: This talk will build a careful foundation for modeling knit objects using graphs in order to explore equivalences between knittable objects and well known graph structures and build theory for the complexity of determining knittability of graph structures. This discussion will also relate knitting properties with the topological genus of graphs and explore restrictions of general graph algorithms which are uniquely motivated by the knitting process. This foundation will be used to represent real-world knitting patterns and illustrate considerations of visualizing such patterns through force-directed graph layout algorithms.
Speaker: Criston Hyett
Date: April 28, 2025 | 4PM
Title: Lagrangian Reduced-Order Models of Turbulence in the Age of Machine Learning
Abstract: The pervasive nature and high computational cost of turbulence hinder the design and control of a wide array of engineered systems. Moreover, despite significant progress, several fundamental aspects of turbulence phenomenology lack a complete description. Given the high computational cost of fully resolving turbulence, reduced-order models are essential to efficiently account for turbulent effects. Our work leverages physics-informed machine learning (PIML) in the Lagrangian frame, which offers unique access to fundamental physical processes, to advance both the methodology of model-order reduction and phenomenological understanding. In this talk, I will introduce essential aspects and modeling challenges of turbulence, and present our recent work on PIML approaches to Lagrangian turbulence. I will highlight unique challenges and insights in applying machine learning to turbulence datasets, and will conclude with modern perspectives on the problem and promising future directions of research.