Events
Molena Nguyen, NC State, Take-away Impartial Combinatorial Games on Hypergraphs and their related Geometric and Discrete Structures
SAS 1220In a Take-Away Game on hypergraphs, two players take turns to remove the vertices and the hyperedges of the hypergraphs. In each turn, a player must remove either only one vertex or only one hyperedge. When a player chooses to remove one vertex, all of the hyperedges that contain the chosen vertex are also removed.…
Di Qi, Purdue University, Statistical reduced-order models and machine learning-based closure strategies for turbulent dynamical systems
ZoomThe capability of using imperfect statistical reduced-order models to capture crucial statistics in complex turbulent systems is investigated. Much simpler and more tractable block-diagonal models are proposed to approximate the complex and high-dimensional turbulent dynamical equations using both parameterization and machine learning strategies. A systematic framework of correcting model errors with empirical information theory is…
Jonathan Zhu, Princeton, Waists, widths and symplectic embeddings
ZoomWaists and widths measure the size of a manifold with respect to measures of families of submanifolds. We’ll discuss related area estimates for minimal submanifolds, as well as applications to quantitative symplectic camels. Zoom invitation is sent to the geometry and topology seminar list. If you are not on the list, please, contact Peter McGrath…
Ivan Yotov, University of Pittsburgh, A nonlinear Stokes-Biot model for the interaction of a non-Newtonian fluid with poroelastic media
ZoomA nonlinear model is developed for fluid-poroelastic structure interaction with quasi-Newtonian fluids that exhibit a shear-thinning property. The flow in the fluid region is described by the Stokes equations and in the poroelastic medium by the quasi-static Biot model. Equilibrium and kinematic conditions are imposed on the interface. A mixed Darcy formulation is employed, resulting…
Doctoral Exam: Lindsey Farris, NC State, Finite Dimensional Nilpotent Leibniz Algebras and Isomorphic Maximal Subalgebras
ZoomAdvisor Ernest Stitzinger, contact for Zoom access
Doctoral Exam: Timothy Reid, NC State, Probabilistic Numerical Linear Solvers
ZoomAdvisor Ilse Ipsen (ipsen@ncsu.edu contact for Zoom access)
Doctoral Exam: Erica Swain, NC State, C_n^1 Geometric Crystal corresponding to Dynkin node i=n for n =2,3,4 and its ultradiscretization
ZoomAdvisor Kailash Misra (misra@ncsu.edu contact for Zoom access)
Doctoral Exam: Isaac Sunseri, NC State, Design and Sensitivity Analysis of Inverse Problems Governed by Partial Differential Equations
ZoomAdvisor Alen Alexanderian (alexanderian@ncsu.edu, contact for Zoom access)
Daniel Sanz-Alonso, University of Chicago, Department of Statistics and CCAM, Finite Element and Graphical Representations of Gaussian Processes
SAS 4201Gaussian processes (GPs) are popular models for random functions in computational and applied mathematics, statistics, machine learning and data science. However, GP methodology scales poorly to large data-sets due to the need to factorize a dense covariance matrix. In spatial statistics, a standard approach to surmount this challenge is to represent Matérn GPs using finite…
Doctoral Exam: Yvonne Niyonzima, NC State, Application of Mathematical Modeling in Toxicology and Human Immunodeficiency Virus
ZoomAdvisor Hien Tran (tran@ncsu.edu, contact for Zoom access)
Mikhail Karphukin, Caltech, Eigenvalues of the Laplacian and min-max for the energy functional
SAS 4201The Laplacian is a canonical second order elliptic operator defined on any Riemannian manifold. The study of optimal upper bounds for its eigenvalues is a classical problem of spectral geometry going back to J. Hersch, P. Li and S.-T. Yau. It turns out that the optimal isoperimetric inequalities for Laplacian eigenvalues are closely related to…
Doctoral Exam: Benjamin Hollering, NC State, Computational and Combinatorial Techniques for Phylogenetic Algebraic Geometry
ZoomAdvisor Seth Sullivant (smsulli2@ncsu.edu, contact for Zoom access)
Juan Carlos, Centro de Modelización Matemática, Ecuador, Bilevel learning for inverse problems
ZoomIn recent years, novel optimization ideas have been applied to several inverse problems in combination with machine learning approaches, to improve the inversion by optimally choosing different quantities/functions of interest. A fruitful approach in this sense is bilevel optimization, where the inverse problems are considered as lower-level constraints, while on the upper-level a loss function based…
Doctoral Exam: Michael Merrit, NC State, Multifidelity Global Sensitivity Analysis for Complex Problems
ZoomAdvisor Pierre Gremaud, contact for Zoom access
Gloria Mari Beffa, University of Wisconsin, Discrete Geometry of Polygons and Soliton Equations
SAS 4201In this talk we will discuss the connection between invariant evolutions of polygons and completely integrable discrete systems via polygonal geometric invariants. We will give examples and show how some open problems for bi-Hamiltonian structures of discrete systems were made easier and solved using this correspondence. If time allows we will discuss some open problems.…
Doctoral Exam: Robert DeYeso, NC State, Obstructing Exceptional Surgeries using Immersed Curves
ZoomAdvisor Tye Lidman, contact for zoom link
Kamala Dadashova, NC State, Parameter subset selection for a mathematical model of antibody therapies for neurological diseases
SAS 1220A significant challenge in the development of drugs to treat central nervous system (CNS) disorders is to attain sufficient delivery of antibodies across blood-brain barriers (BBB). Since not all antibodies can pass through BBB, it is crucial to understand antibody exposure in the CNS quantitatively to construct drug characteristics and identify proper dosing regimens. We…
Doctoral Exam: Cashous Bortner, NC State, Identifiability Analysis of Two Families of ODE Models
ZoomAdvisor Seth Sullivant, contact for Zoom access
Alexey Miroshnikov, Discover Financial Services, Wasserstein-based fairness interpretability framework for machine learning models
SAS 4201The objective of this talk is to introduce a fairness interpretability framework for measuring and explaining the bias in classification and regression models at the level of a regressor distribution. In our work, we measure the model bias across sub-population distributions in the model output using the Wasserstein metric. To properly quantify the contributions of…