Spring 2025
Seminar on Analysis and Applications
Nguyen Lam (Memorial University of Newfoundland): Uncertainty Principles: Best constants, Optimizers and the stability
Organizer: Hung Phan, email: hung_phan@uml.edu
February 24, 11 a.m. - Noon, Room: Southwick Hall 350W
Abstract: The Heisenberg uncertainty principle, which is a fundamental result in quantum mechanics, and related inequalities such as the hydrogen and Hardy uncertainty principles, belong to the family of geometric inequalities known as the Caffarelli-Kohn-Nirenberg inequalities. In this talk, we discuss some recent results about the optimal uncertainty principles, Caffarelli-Kohn-Nirenberg inequalities, and their quantitative stability.
Seminar: Working on What (WOW)
Julio Enrique Castrillon Candas (Boston University): Statistical computational mathematics for complex models: Applications to machine learning, protein interactions and Alzheimer’s disease
Organizer: Emily Gunawan, email: emily_gunawan@uml.edu
Abstract: With the advent of massive data sets and high dimensional complex stochastic models appearing in many scientific and engineering fields, novel approaches to prediction, data and statistical analysis, machine learning, and optimization under uncertainty are needed. The merging of statistics, stochastics, computational applied math, and high performance computing has opened the door to solving these and many related complex problems. Computational prediction, inference, optimization and machine learning form the basis for critical decisions in areas of engineering and science such as infrastructure/disaster management, drug discovery, remote sensing and medical diagnosis, among many others. By adapting to the nature of their uncertainty these problems can be solved in a more optimal and accurate manner. My specific interests lie in mathematically rigorous functional analysis for random fields, Machine Learning (ML), Uncertainty Quantification (UQ), stochastic Partial Differential Equations, non-linear stochastic networks and large scale computational statistics spanning pure and applied mathematics with high performance computing. Complementing the research are several funded high impact applications such as power systems, protein interactions, deforestation detection and Alzheimer's disease subtyping.
The speaker's website
March 19, 11 a.m. - Noon, Room: Southwick Hall 350W
Abstract: tba.
Seminar: Working on What (WOW)
Jianping Pan (Arizona State University): Polynomials from Schubert calculus via diagrams
Organizer: Emily Gunawan, email: emily_gunawan@uml.edu
April 7, 11 a.m. - Noon, Room: Southwick Hall 350W
Abstract: Polynomials are powerful tools in many fields, for example, representation theory, geometry, and topology. Understanding the combinatorics arising from polynomials may reveal important information in problems from these fields. I will talk about several families of polynomials arising from Schubert calculus, which originated in enumerative geometry. I will discuss some combinatorial models related to these polynomials, including (K)-Kohnert diagrams and snow diagrams. These discrete objects, along with operations defined on them, exhibit rich combinatorial properties. They can be used to simplify computations and extract algebraic and geometric information within these polynomials.