Thursdays, 4:30-5:30pm, Hunter East 920 or GC Room 6496 (currently hosted remotely; please email vrmartinez-at-hunter-dot-cuny-dot-edu for password)
HCAM was initiated by the late John Arthur Loustau, a former professor of the Mathematics and Statistics Department here at Hunter, with his then post-doc Emmanuel Asante-Asamani in 2018. John had an eclectic mix of mathematical interests, each of which he pursued with gusto and depth. In a career spanning nearly 50 years, he began his journey in Commutative Algebra, transitioned afterwards to Computer Science, then Numerical Analysis, and eventually into Mathematical Biology. John recounted once a vacation he took with his family to Reno long ago. His father, a hardware merchant who also did plumbing and electric work, had taken him to the School of Mining Engineering at University of Nevada and told John that he had once dreamt of enrolling there when he was younger. Nevertheless, as John fondly recalled,He was a fine applied mathematician.
In the spirit of John's open-mindedness and willingness to foster and maintain a diverse community of mathematics, HCAM hosts speakers across a wide range of disciplines, from both academia and industry. HCAM also showcases the work of rising graduates of the Applied Math MA program at Hunter and regularly hosts Applied Math MA alumni to share their post-graduate experiences with current students. If you are interested in giving a talk at this seminar, please contact me at vrmartinez-at-hunter-dot-cuny-dot-edu. Please note that this seminar is partially shared with the Nonlinear Analysis and PDEs seminar at the Graduate Center, so that some talks are hosted there instead.
Spring 2022 Schedule
February 24 Zoom (Recording)
Aseel Farhat (Florida State University, Department of Mathematics)
A short introduction to calculus of variations and its applications
I will give a short introduction to calculus of variations and the Euler-Lagrange equations and give some examples. I will also discuss some applications of calculus of variations in solving differential equations, such as the finite element method and the recently introduced physics-informed neural networks (PINN) algorithm.
March 3 Zoom (Recording)
Swati Patel (Oregon State University, Department of Mathematics)
Fitting macroparasitic disease transmission models to geostatistical prevalence data
In this talk, I will discuss applying a recently developed approach to estimate parameters of a disease transmission model for a group of macroparasites that infect an estimated 1.5 billion people worldwide. While the disease is widespread, its spread occurs on relatively local scales and the vulnerability of populations can vary from region to region. Hence, key epidemiological parameters of mechanistic transmission models vary across regions and understanding these differences is important for developing strategies to mitigate morbidity of the disease. We infer these parameters for 5183 distinct regional units across sub-Saharan Africa. Inferring these parameters is challenging since data is limited to relatively few points in space and time. Previously developed geostatistical maps use this limited data, along with socioeconomic and environmental indicators, to provide broad-scale distributional estimates of disease prevalence. Using a Bayesian statistical framework that employs an adaptive multiple importance sampling algorithm, we fit these geostatistical distributional data to a transmission model. We then use these parameterized transmission models to predict how various mitigation strategies will impact broad-scale disease prevalence.
March 10 Zoom (Recording)
Julie Simons (Cal State Maritime, Department of Mathematics and Statistics)
Models for Flagellar Motion in 3D
The motion of thin structures like cilia and flagella is vital for many biological systems. In this talk, we will use reproduction and sperm motility as a primary motivator for studying the motion of flagella in 3D fluid environments. Mathematically, we can model a flagellum as a curve in space and approximate the fluid environment as a Stokesian, inertialess world. Many models for flagellar motion in such settings have been developed over the span of many decades, starting with early works using 2D approximations. More recent advancements--technologically, mathematically and computationally--have allowed for exploration of motion in fully three-dimensional contexts and some surprising results. We will describe the mathematical framework for recent work involving the Method of Regularized Stokeslets and preferred curvature and then present results involving individual swimmers near surfaces, groups of swimmers, and cooperative swimmers. We hypothesize that some species of animals have developed cellular structures that enable sperm to swim faster and more efficiently, perhaps in response to sperm competition due to mating behavior.
Hiatus
March 24 Zoom (Recording)
Josh Hewitt (Duke University, Department of Statistical Science)
Modeling measurement and classification uncertainty in drone-based images used to estimate physical characteristics and shapes of whales
Drone-based imagery is increasingly used to measure the size and condition of marine mammals, among other species. Drones fly above a target animal, and a picture is taken. The camera's characteristics and altitude define a geometry problem that lets researchers compute the animal's size based on how big it appears in the image. But, the animal's size in the image and the altitude are observed with uncertainty. Uncertainty stems from image resolution and imperfect altimeters. Measurement errors can be estimated via a calibration study, where images are taken of references objects, whose exact length is known. We construct a hierarchical Bayesian model that uses calibration data to learn about measurement errors for several altimeters (i.e., laser-based and barometer-based), then yields posterior predictive distributions for the unknown measurements of the animals. The model's hierarchical form lets us estimate relationships between lengths and widths of whales, which is a proxy for health. We also estimate uncertainty for length-based estimates of a whale's maturity, and discuss extending the model to other animals, imaging problems, and measured quantities and relationships.
Hiatus
April 7 Zoom (Recording)
Isabel Scherl (University of Washington, Department of Mechanical Engineering)
Experimental Fluid Mechanics with Machine Learning
The ability to understand unsteady fluid flows is foundational to advancing technologies across fields. We use cutting edge data-driven methods (i.e. machine learning) to interpret and control unsteady fluid flows through experiments in the following three cases: 1. We use robust principal component analysis (RPCA) to improve flow-field data by leveraging global coherent structures to identify and replace spurious data points. In all cases, both simulated and experimental, we find that RPCA filtering extracts dominant coherent structures and identifies and fills in incorrect or missing measurements. 2. We optimize a two cross-flow (i.e. vertical-axis) turbine array using a hardware-in-the-loop approach and find that arrays with well-considered geometries and control strategies can outperform isolated turbines by up to 30%. 3. Using similar turbines, we create an experimental framework to more efficiently explore arrays' high-dimensional parameter space. Our data-driven approach allows us to model parameter spaces using sparse data. As a result, we are able to map turbine system dynamics with orders of magnitude fewer data points.
April 14 Zoom (Recording)
Le Mai Nguyen Weakley (Indiana University, High Performance Computing)
High Performance Computing by a Mathematician
Since the emergence of distributed systems and cluster computing capable of doing large scale parallel calculations, High Performance Computing (HPC) has been a cornerstone in scientific discoveries that require large scale simulations like weather and climate forecasting, astronomy, QCD, among a variety other disciplines that require such workflows. In today's data-driven world and with the introduction of hardware that allow for faster matrix operations, HPC has become a tool for researchers and machine-learning enthusiasts everywhere. This talk will give an overview of HPC and how the author went from pursuing their doctorate in mathematics to a career in High Performance Computing.
Spring Break
April 28 Zoom (Recording)
Zachary Simon (Lockheed Martin, Autonomy and Artificial Intelligence)
(Cancelled)
Hiatus
May 12 Zoom (Recording)
Caihua Chen, Yanlin Ou, Keven Calderon, Fardous Sabnur, Yana Mross (CUNY Hunter College)
Masters Projects Presentations
Please join us to support the presentations of your peers.
Current Seminar
Past Seminars