Colloquia
Title: Mathematical Modeling of Drug Resistance in Cancer
Dr. Natalia Komarova
Department of Mathematics, UC San Diego, CA
When: Friday, November 8, 2024 at 2 pm
Where: Pickard Hall, Room 311
Abstract: Resistance to drugs is one of the most challenging problems in public health. In this talk, I will focus on drug resistance in cancer, which is often associated with the existence of resistant mutants in the evolving population of malignant cells. I will start by showing the type of stochastic modeling that has been used to quantify various aspects of resistance generation, including multiple-drug resistance, the role of cellular turnover, cellular quiescence, and the phenomenon of cross-resistance. I will compare and contrast treatment strategies such as cycling and simultaneous multi-drug treatment. I will then focus on a specific case of Chronic Lymphocytic Leukemia (CLL). This is the most common leukemia, mostly arising in patients over the age of 50. The disease has been treated with chemo-immunotherapies with varying outcomes, depending on the genetic make-up of the tumor cells. More recently, a promising tyrosine kinase inhibitor, ibrutinib, has been developed, which resulted in successful responses in clinical trials, even for the most aggressive chronic lymphocytic leukemia types. The crucial questions include how long disease control can be maintained in individual patients, when drug resistance is expected to arise, and what can be done to counter it. Computational evolutionary models, based on measured kinetic parameters of patients, allow us to address these questions and to pave the way toward a personalized prognosis and treatment.
Short Bio: Natalia Komarova holds an MS in Theoretical Physics from Moscow State University and a PhD in Applied Mathematics from University of Arizona. After being a Member at the Institute for Advanced Studies in Princeton (1999-2003), she became Assistant Professor at the Department of Mathematics at Rutgers, and then worked at UC Irvine from 2004 until 2024, when she joined UCSD. Komarova is interested in Applied Mathematics, and in particular, in Mathematical Biology, evolution, and modeling of complex social phenomena.
Hide Natalia Komarova Talk DetailsTitle: Dynamical Lie algebras
Dr. Bojko Bakalov
Department of Mathematics, North Carolina State University
When: Friday, November 1, 2024 at 2 pm
Where: Pickard Hall, Room 311
Abstract: Quantum computers are physical machines that process information using the principles of quantum mechanics, which in turn is underpinned by linear algebra. The talk will start with a review of Lie algebras (consisting of matrices under the operation of commutator) and their role in quantum mechanics. The dynamical Lie algebra (DLA) of a quantum system is defined as the Lie algebra obtained by taking all real linear combinations and nested commutators of the terms of the Hamiltonian. The significance of the DLA is that the time evolution of the system is given by elements of the associated Lie group. The DLA determines the set of reachable states of the system and its controllability, so it is relevant for designing quantum circuits. In this talk, I will present a classification of DLAs generated by 2-local Pauli interactions on spin chains and on arbitrary interaction graphs. I will also discuss applications of DLAs to variational quantum computing, including the problem of barren plateaus. The talk will be accessible to all mathematics graduate students; no prior knowledge of physics or quantum computing is assumed.
Short Bio: Originally from Bulgaria, Bojko Bakalov received his PhD from MIT and was a Miller Research Fellow at UC Berkeley before joining the NC State Math Department in 2003. Currently, he is the Director of Graduate Programs in Math and Applied Math, and has a leadership role in the NC State Quantum Initiative. Bakalov’s research interests include representation theory, quantum computing, mathematical physics, signal processing, and integrable systems. In 2006, he was awarded the Hermann Weyl Prize of the International Colloquia on Group Theoretical Methods in Physics, for original work of significant scientific quality in the area of understanding physics through symmetries. Bakalov’s research has been funded by the US Air Force, DOE, NSA, NSF, and the Simons Foundation.
Hide Bojko Bakalov Talk DetailsTitle: Cardiac hemodynamics and congenital heart disease: restoringnormal function
Sandra Rugonyi, Ph.D.
Oregon Health & Science University, Biomedical Engineering
Department, Portland, OR, USA
When: Friday, October 25, 2024 at 2 pm
Where: Pickard Hall, Room 311
Abstract: In the normal heart, intracardiac blood flow (hemodynamics) optimizes pumping efficiency by facilitating the motion of blood in and out of the heart and closure of valves. Abnormal blood flow patterns that occur due to heart disease can be detrimental: both due to decreasing cardiac efficiency, and to the sensitivity of heart cells to abnormal hemodynamic stresses, which could exacerbate pathological progression of heart disease. In this talk, we will focus on hypertrophic cardiomyopathy (HCM), a congenital heart disease characterized by thickening of the left ventricular wall (hypertrophy) that leads to altered cardiac flow patterns. We will discuss how computational modeling approaches can be used in the evaluation of HCM patients and the effect of novel myosin inhibitor drugs to treat HCM. This work is in collaboration with Dr. Ted Abraham (UCSF HCM Center of Excellence).
Short Bio: Sandra Rugonyi, Professor of Biomedical Engineering, Oregon Health & Science University (OHSU), Portland, OR, USA. Prof. Rugonyi has expertise in cardiovascular biomechanics and computational modelling. Her career started in Argentina, where she got an MS-equivalent degree in Nuclear Engineering from the Balseiro Institute. After working for a nuclear power plant and then a steel company, she moved to the USA, where she earned a PhD in Mechanical Engineering from MIT that focused on advanced numerical methods for fluid-structure interaction problems. In 2005 Dr. Rugonyi joined the Biomedical Engineering department at OHSU, and since then she has applied mechanical engineering principles to heart development and congenital heart disease. Dr. Rugonyi has contributed to fundamental understanding of hemodynamic regulation on heart formation.
Hide Sandra Rugonyi Talk DetailsTitle: Bayesian Inversion Using Level Sets in Diffuse Optical Tomography
Dr. Taufiquar Khan
University of North Carolina at Charlotte
When: Friday, September 27, 2024, from 2-4 pm
Where: Pickard Hall, Room 311
Abstract: In this talk, we will provide an overview of the ill-posed inverse problem in Diffuse Optical Tomography (DOT) at an introductory level. We will discuss several regularization approaches to solve the ill-posed inverse problem in DOT including deterministic, statistical, and machine learning. Then we will present our most recent work using Bayesian Inversion Using level sets for image reconstruction in DOT. The results of image reconstruction will be demonstrated using synthetic data for the recently proposed algorithm. This is joint work with Anuj Abhishek (Case Western Reserve University) and Thilo Strauss (Xi’an Jiaotong-Liverpool University).
Short Bio: Taufiquar Khan is currently a Professor and the Chair of the Department of Mathematics and Statistics, University of North Carolina at Charlotte (UNC Charlotte). He was a Professor and an Associate Director of Graduate Studies of the School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, USA, before joining UNC Charlotte. He is a recipient of the Humboldt Fellowship from Germany. His research interests include machine learning, inverse problems involving ordinary and partial differential equations. His present and past research have been supported through the NSF, DOD, Humboldt Foundation, and the industry.
Hide Taufiquar Khan Talk DetailsSeminars
Title: Boundary Problems In Rough Domains With Data in Weighted Morrey Spaces
Dr. Marcus Laurel
University of Texas at Arlington
When: Friday, October 11, 2024, from 3-4 pm
Where: Pickard Hall, Room 305
Abstract: The goal of this talk is to present a brief introduction to the method of layer potentials for solving boundary value problems on rough domains. Specifically, we work with the class of weakly elliptic, second-order, homogeneous, constant (complex) coefficient systems in Euclidean space. We use singular integrals of layer potential type, which themselves can be defined on the class of uniformly rectifiable sets, the geometric measure theoretic sharp analogue of Lipschitz images. This requires a Calderón-Zygmund theory that works in such rough geometries as well as on the function spaces we have in mind. Specifically, we consider boundary problems where the boundary datum is arbitrarily chosen from a Muckenhoupt-weighted Morrey space (an offshoot of the scale of Muckenhoupt-weighted Lebesgue spaces), in which integrals over balls are bounded by a uniform constant multiplied by a specific power of the radii of the balls. We will see the delicate interplay between harmonic analysis, functional analysis, and geometry that leads to a well-posedness result for the Dirichlet Problem. This is joint work with Professor Marius Mitrea (Baylor University).
Short Bio: Dr. Marcus Laurel received his Ph.D. in mathematics in 2024 from Baylor University under the guidance of Professor Marius Mitrea. He works on the confluence of geometry, harmonic analysis, and PDE. His interests lie in layer potential methods to solve boundary value problems for elliptic systems, as well as function space theory in rough geometric settings. He, with Prof. Mitrea, recently published a book titled *Weighted Morrey Spaces: Calderón-Zygmund Theory and Boundary Problem.* Currently, Dr. Laurel is an assistant professor of instruction at UT Arlington.
Hide Marcus Laurel Talk DetailsTitle: "Solving linear fractional differential equations with random non-homogeneous parts"
Dr. Laura Villafuerte
The University of Texas at Austin
When: Friday, October 4, 2024 from 2-4 p.m.
Where: Pickard Hall, Room 311
Abstract: Experimental data and algorithms for certain real-world phenomena have shown that fractional order derivatives provide more efficient modeling than integer order derivatives. In these scenarios, using fractional differential equations rather than integer-order differential equations to describe these phenomena seems more appropriate. In addition, to consider the uncertainty arising from measurement errors and the complexity of the phenomena analyzed, randomness is included in the differential equations through their coefficients, initial conditions, and non-homogeneous parts. In this work, we investigate mean square solutions for some families of fractional linear differential equations with random non-homogeneous parts. This approach is based on the mean square Caputo derivative. For the sake of generality, we assume that the initial conditions and coefficients of the equations are random variables satisfying certain mild conditions. For this class of equations, we construct a generalized power series solution by using the mean square Laplace transform. Then, assuming an exponential growth condition on the force term, we show its mean square convergence. As a consequence of the mean square convergence, the convergence of the two first statistical moments, mean and variance, is guaranteed. Several examples are discussed to compare the fractional and integer order random differential equations utilizing its first two moments.
Hide Laura Villafuerte Talk DetailsTitle: A Meta-analysis based Hierarchical Variance Model for Powering One and Two-sample t-tests
Jackson Barth, PhD
Assistant Professor, Department of Statistical Science, Baylor
University
When: Friday, September 20, 2024 from 3:30-4:20 pm
Where: Pickard Hall, Room 110
Abstract: Sample size determination (SSD) is essential in statistical inference and hypothesis testing, as it directly affects the accuracy and power of the analysis. We propose a SSD methodology for one and two-sample t-tests that ensures clinical relevance using a pre-determined unstandardized effect size. Our novel approach leverages Bayesian meta-analysis to account for the uncertainty surrounding the variance, a common issue in SSD. By incorporating prior knowledge from related studies via a Bayesian gamma-inverse gamma model, we obtain an informative posterior predictive distribution for the variance that leads to better decisions about sample size. For efficient posterior sampling, we propose an empirical Bayes approach, which is further combined with a quantile simulation approach to facilitate computation. Simulations and empirical studies demonstrate that our methodology outperforms other aggregate approaches (simple average, weighted average, median) in variance estimation for SSD, especially in meta-analyses with large disparity in sample size and moderate variance. Thus, it offers a robust and practical solution for sample size determination in t-tests.
Hide Jackson Barth Talk Details