|2020 Fall (Online) Colloquium and Teaching Seminar Schedule|
|September 30||Colloquium||Padmanabhan Seshaiyer (George Mason University)|
|October 21||Colloquium||Marion Campisi (San Jose State University)|
|November 11||Colloquium||Hadi Susanto (University of Essex)|
|November 18||Colloquium||Jessica Ellis Hagman (Colorado State University)|
|December 2||Colloquium||Luis Sordo Vieira (University of Florida, Department of Medicine)|
Colloquium December 2
Mathematics as a tool for data integration
Luis Sordo Vieira
University of Florida
Recent advances in technology has led to the acquisition of multimodal biological data measuring biological phenomena at various spatiotemporal scales. In this talk, we will discuss how mathematical modeling can be used as a tool for data integration using use cases from respiratory infections, cancer biology, and psychiatry.
Colloquium November 18
Attending to diversity, equity, and inclusion within introductory college mathematics programs
Jessica Ellis Hagman
Assistant Professor, Colorado State University
For the past near decade, my research team and I have focused our research on understanding what characteristics of college calculus programs support student success, and have disseminated our findings to help improve college calculus education across the country. In more recent years, I have re-analyzed our data and learned that we missed a critical and cross cutting characteristic for real success: diversity, equity, and inclusion practices. In this talk, I will share what I mean by these practices in relation to college introductory mathematics programs and discuss specifically what this can look like in relation to mathematics course placement and other topics of interest at SHSU. Further, I will situate these conversations within a broader discussion of motivations for attending to and increasing diversity in STEM.
Colloquium November 11
Pattern formation and homoclinic snaking
Professor, University of Essex
Pattern formation is the developmental process of visible, orderly outcomes of self-organisation. Patterns are ubiquitous in nature and they can appear through the process of multiple hysteresis of localised solutions that form a 'snaking' existence curve in the parameter space, known as a homoclinic snaking. The ‘snaking’ bifurcation diagram has been widely observed in numerous natural applications. In this talk, I will review some of our recent works on the snaking of localised patterns in spatially continuous and spatially discrete systems. In particular, I am going to discuss and show that the mechanisms behind the formation of homoclinic snaking in the systems are different.
Colloquium October 21
Analysis of partisan gerrymandering tools in advance of the US 2020 census
Department of Mathematics, San Jose State University
Over the last decade, mapmakers have precisely gerrymandered political districts for the benefit of their party. In response, political scientists and mathematicians have more extensively investigated tools to quantify and understand the mathematical structure of redistricting problems. Two primary tools for determining whether a particular redistricting plan is fair are partisan-gerrymandering metrics and stochastic sampling algorithms. In this talk I will talk about advantages and limitations of these methods, as well as the legislative and judicial contexts in which these problems exist.
Colloquium September 30
Research and education in computational mathematics for solving real-world problems arising from COVID-19
Professor & Assoc. Dean for Academic Affairs, George Mason University
In this talk, we will discuss how research and education programs can be developed around computational mathematics that will not only help solve several multidisciplinary applications such as those related to COVID-19 but also will help to train the next generation STEM (Science, Technology, Engineering and Mathematics) workforce to solve real-world challenges. Specifically, we will present examples of real-world challenges related to infectious diseases modeled via coupled system of differential equations and present methodologies to solve them using deep learning frameworks. We will also describe how research focus can be integrated with education programs where the primary goal will be to engage students to apply well-developed research concepts in computational mathematics. Incorporating concepts into new or existing inter-disciplinary computational mathematics courses, mentoring students at the graduate, undergraduate and high school level on research projects as well as enhancing pedagogical practices for teachers through professional development workshops will also be discussed.