Mathematics Colloquium
The UL Lafayette Mathematics Colloquium is an ongoing seminar series that features speakers from other universities and from our department. The topics cover all areas of mathematics and statistics. We try to schedule an interesting mix of topics ranging from very applied to more abstract in nature. These lectures are open to all UL Lafayette students, faculty and community members for the purpose of fostering continued discussion and networking in the various areas of mathematics. Please contact Leonel Robert with questions or suggestions about the colloquium series.
Our colloquia are normally held on Thursday at 3:30 p.m. in room 208 of Maxim Doucet Hall. Some colloquia may be presented via zoom instead. Refreshments are served at 3:15 in room 208. To accommodate outside speakers, the colloquium is occasionally held on a different day of the week, e.g., Tuesday at 3:30, instead of Thursday.
Remember, our colloquium is open to the public and everyone who is interested is encouraged to attend.
Spring 2023 Schedule

10 January 2023 (Tuesday)
Graph Neural Networks for Disease Classification and Feature Selection
Tiantian Yang
Department of Biostatistics
Boston University
Abstract: Omics data, such as genomics, transcriptomics, proteomics, and metabolomics, have crucial roles in discovering disease pathways and predicting clinical outcomes. However, the large p and small n issue is a great challenge for building predictive models for omics data analysis. In addition, the sparsity of the biological network and the unknown correlation structures between features also bring more challenges. With the recent emergence of Graph Neural Network (GNN), incorporating known functional relationships over a graph could potentially solve these issues. However, those GNN methods only utilize graphs in either an external feature graph or a generated feature graph alone, which will limit the classification performance. We proposed an external and generated Graph Neural Network (engGNN) model to aggregate the information of feature data in both external and generated feature graphs. The engGNN model utilized not only external knowledge to construct a feature graph but also eXtreme Gradient Boosting (XGBoost) to build a directed feature graph. We conducted extensive simulations and real data applications to validate the performance of the engGNN model. Compared with other baseline deep learning and machine learning models, engGNN has demonstrated a good classification performance. Moreover, by extending the connection weights method to rank the importance of features, engGNN can select important features with a corresponding meaningful interpretation for real biomedical data. 
17 January 2023 (Tuesday)
Statistical Methodologies and Applications for Discrete Data in Climate and Microbiome Studies
Mo Li
Johns Hopkins University
Baltimore, Maryland
Abstract: There has been a remarkable resurgence in the need for novel statistical methodologies for analyzing discrete data, such as categorical divisions of the rainfall amount, and count in highthroughput sequencing studies. This talk covers two projects within this general framework. In the first project, we introduce a changepoint detection method for serially correlated categorical time series in climate studies. Climate time series usually contain sudden changes in their marginal distribution or correlation structure. A cumulative sum (CUSUM)type test is devised to test for a single changepoint in a correlated categorical data sequence constructed from a latent Gaussian process through clipping techniques. A sequential parameter estimation method is proposed to estimate the parameters in this model. The method is applied to a real categorized rainfall time series from Albuquerque, New Mexico. In the second project, we focus on the highdimensional microbiome count data. Bias is ubiquitous in microbiome sequencing studies and leads to invalid statistical conclusions even if the studies are welldesigned. We will discuss the bias generation process through the experimental and analysis pipelines. We utilize and modify a loglinear compositional model to understand the bias generation procedure in microbiome sequencing through the analysis of data from a mock community (i.e., a labgenerated bacterial mixture in which the true relative abundances are all known) and real microbiome data from tobacco samples. Finally, a novel semiparametric rankbased regression approach will be briefly discussed for the biasresistant modeling task. 
Monday 20 March 2023 (Monday 3:45 coffee, 4:00 talk)
An improved incremental SVD and its applications
Yangwen Zhang
Department of Mathematical Sciences
Carnegie Mellon University
Abstract: In 2002 an incremental singular value decomposition (SVD) was proposed by Brand to efficiently compute the SVD of a matrix. The algorithm needs to evaluate thousands or millions of orthogonal matrices and to multiply them together. Rounding error may destroy the orthogonality. Hence many reorthogonalization steps are needed in practice. In [Linear Algebra and its Applications 415 (2006) 20–30], Brand said: ``It is an open question how often this is necessary to guarantee a certain overall level of numerical precision.'' In this talk, we answer this question: by modifying the algorithm we can avoid computing the most of those orthogonal matrices and hence the reorthogonalizations are not necessary. We prove that the modification does not change the outcome of Brand's algorithm. We have successfully applied our improved scheme to snapshotbased POD model order reduction, time fractional PDEs and integrodifferential equations and time dependent optimal control problems. Numerical analysis and experiments are presented to illustrate the impact of our modified incremental SVD on these important problems. 
Wednesday 22 March 2023 (Wednesday 3:45 coffee, 4:00 talk)
Efficient Numerical Methods for the Nonlocal Laplacian
Zhaopeng Hao
Department of Mathematics
Purdue University
Abstract: Due to its extraordinary modeling capabilities, the nonlocal fractionalorder Laplacian has recently attracted increasing scientific and engineering attention. However, the nonlocal operators bring up new challenges in discretization and computation. To cope with difficulties, we discuss two efficient numerical methods, the spectral and currently popular deep neuralnetwork methods. We present optimal error estimates for the spectral methods based on the sharp prior regularity estimates. For the neural network methods, we provide convergence analysis. Finally, we demonstrate numerical examples to show their efficiency and accuracy. 
23 March 2023
The ADI, the LOD and the Operator Splitting  A New Western Story about Splitting Methods
Qin (Tim) Sheng
Department of Mathematics and
Center for Astrophysics, Space Physics and Engineering Research
Baylor University
Waco, TX
Abstract: Splitting methods have been used for solving a broad spectrum of mathematical problems. They are for the numerical solution to not only differential equations, but also statistical and optimization procedures. A splitting method separates the original mathematical model into several subproblems, separately computes the solution to each of them, and then combines all subsolutions to form an approximation of the solution to the original problem. A canonical example is splitting of diffusion and convection terms in a convectiondiffusion partial differential equation. The splitting idea generalizes in a natural way to problems with multiple operators too. In all cases, the computational advantage is that it is faster to compute the solution of the split components separately, than to compute the solution directly when they are treated together. However, this comes at the cost of an error introduced by the splitting, so strategies must be devised for controlling the error. This presentation introduces splitting methods via symbolic operations. It also surveys recent developments in the area. An interesting investigation is given in global error analysis of popular exponential splitting formulations. One recent development, adaptive splitting, deserves and receives special mention in this talk: it is a new splitting approach to collaborate with an otherwise parallel computational strategy.
This talk is suitable for all graduate students. It may be accessible to undergraduate students who have studied differential and integral calculus.
About the Speaker: Dr. Sheng received his PhD from the University of Cambridge under the supervision of Professor Arieh Iserles. After his postdoctoral research with Professor Frank T. Smith, FRS, in University College London, he joined National University of Singapore in 1990. Since then, Dr. Sheng was on the faculty of several major universities. He joined Baylor University in 2005. He is associated with the Mathematics Department and the Center for Astrophysics, Space Physics and Engineering Research (CASPER).
Dr. Sheng has been interested in splitting and adaptive numerical methods for solving linear and nonlinear partial differential equations. He is also known for the ShengSuzuki theorem in numerical analysis. He has published over 110 refereed journal articles as well several research monographs. His projects have been supported by the National Science Foundation, the Simons Foundation and the Department of Defense. He has been EditorinChief of the SCI journal, International Journal of Computer Mathematics, published by Taylor and Francis, London, since 2010. Dr. Sheng currently advises two doctoral students. 
30 March 2023
Discovering higher categories
Martina Rovelli
University Massachusetts Amherst
Amherst, Massachusetts
Abstract: Several algebraic structures of interest that we learn and study in math, such as groups or vector spaces, consist of endowing a set with one or more operations subject to axioms. There are many interesting phenomena in mathematical physics, algebraic geometry, and type theory that seem formalizable by one of such algebraic structures at first glance, although with a closer look the axioms are only met up to a "higher isomorphism". In order to accommodate those situations, one needs to acknowledge the presence of higher structures, and replace the role played by the usual equality relation with the relation induced by higher isomorphisms. In this talk we will describe a couple of different situations across math where the presence of higher structures is observable, discuss the complications that arise, and give an idea of the different approaches that can be taken to work with higher categories rather than ordinary categories.
Fall 2022 Schedule

25 August 2022
Bootstrap approximations to check binary regression models
Gerhard Dikta
FH Aachen, Campus Jülich
Dept. Medical Engineering and Technomathematics
Abstract: Suppose we observe a series of binary data along with explanatory variables and we suspect that these observations belong to a binary regression model.
To test this model assumption, we use KolmogorovSmirnov and Cramér von Mises like test procedures based on a cumulative residual process introduced by Stute. The associated pvalues are determined via a modelbased bootstrap procedure. The theoretical results are based on a joint work with van Heel and Braekers, and are also presented more generally in a textbook together with Scheer. Bootstrap Methods With Applications in R by Gerhard Dikta and Marsel Scheer.
In a final simulation study, we compare this approach with the HosmerLemeshow test in a logistic setup. The implementation in R is done with the R package bootGof developed for the book in a joint work with Scheer. 
1 September 2022
Contrasting Methods of Including Uncertainty: Philosophy and Practicalities
R. Baker Kearfott
University of Louisiana at Lafayette
Abstract: We present a broad elementary overview of uncertainty, from the point of view of models and applications. In particular, we point out where and how uncertainty occurs and why its study is important. We then present the underlying principles behind three different paradigms for handling uncertainty. We give hypothetical examples of use of each method, then contrast their strengths and weaknesses. Technical details are covered in courses, both undergraduate and graduate. If there is sufficient interest, technical details may be discussed in future colloquia or seminars. 
8 September 2022
Ecoevolutionary dynamics in preypredator networks applied to HIV immune escape
Cameron Browne
University of Louisiana at Lafayette
Abstract: Population dynamics and evolutionary genetics underly the structure of ecosystems. For example, during HIV infection, the virus escapes several immune response populations via resistance mutations at distinct epitopes (proteins coded in viral genome), precipitating a dynamic network of interacting virus and immune variants. I will talk about recent work to link virus population genetics with dynamics theoretically and through data to characterize their evolution. We analyze a resourcepreypredator differential equation network model to characterize the emergence of distinct stable equilibria and persistence of different diverse collections of virus and immune populations. Using binary sequences to code viral strain resistance to immune responses, we prove that bifurcations are determined and simplified by a certain evolutionary genetics measure of nonlinearity in the map from viral sequences to fitness (reproductive rate) landscape. The results generalize to decipher stability, structure and invasion of ecological networks based on the linear algebra of prey binary sequences encoding predation and fitness tradeoffs. Finally, numerical simulations and an interdisciplinary application to virusimmune data illustrate our ecoevolutionary modeling framework. 
15 September 2022
Regression Model under SkewNormal Error with Applications in Predicting Groundwater Arsenic Level in the Mekong Delta Region
Nabendu Pal
University of Louisiana at Lafayette
Abstract: Recently there has been some renewed interest in the skewnormal distribution (SND) because it provides a nice and natural generalization (in terms of accommodating skewed data) over the usual normal distribution. In this study we have used the SND error in a regression setup, discussed a step by step approach on how to estimate all the model parameters, and show how naturally the resultant SNDbased regression model can lead to a superior fitting to a given dataset. This generalization enhances the precision in predicting the future value of the response variable when the values of the independent (or input) variables are available. We validate the applicability of our proposed SNDbased regression model by using a recently acquired dataset from the Mekong Delta Region (MDR) of Vietnam which had necessitated this study from a public health perspective. Using the existing survey data our proposed model allows all the stakeholders to better predict the groundwater arsenic level at a site easily, based on its geographic characteristics, in lieu of costly chemical analyses, which can be very beneficial to developing countries due to their resource constraints. 
22 September 2022
The classification program for simple, nuclear, separable C*algebras
Leonel Robert
University of Louisiana at Lafayette
Abstract: I will describe the goals of the classification program described in the title, its history, and recent results arguably amounting to its complete resolution. 
29 September 2022
Career Panel Discussion
UL Lafayette AMS Graduate Student Chapter
University of Louisiana at Lafayette
Abstract: Panelists with varying levels of professional experience in industry will join us to share their career experiences, give advice on making and achieving career goals, and answer any questions you may have. The event is geared towards both undergraduate and graduate students.
Please join us for this casual event  light refreshments will be provided, and the event will be followed by a social hour at Olde Tyme Grocery. 
6 October 2022
No colloquium
UL Lafayette Fall Break 
13 October 2022
Singularity Formation in a Mean Field Neural Network
Phillip Whitman
University of Texas at Austin
Abstract: Idealized networks of integrateandfire neurons with impulselike interactions obey McKeanVlasov diffusion equations in the meanfield limit. These equations are prone to singularities: for a strong enough interaction coupling, the meanfield rate of interaction diverges in finite time with a finite fraction of neurons spiking simultaneously, thereby marking a macroscopic synchronous event. Characterizing these blowup singularities analytically is the key to understanding the emergence and persistence of spiking synchrony in meanfield neural models. Here, we introduce a delayed Poissonian variation of the classical integrateandfire dynamics for which blowups are analytically well defined in the meanfield limit. Albeit fundamentally nonlinear, we show that this delayed Poissonian dynamics can be transformed into a noninteracting linear dynamics via a deterministic time change. This formulation also reveals that the fraction of simultaneously spiking neurons can be determined via a selfconsistent, probabilityconservation principle about the timechanged linear dynamics. 
20 October 2022
MultiScale Structured Models of Infectious Disease Dynamics
Hayriye Gulbudak
University of Louisiana at Lafayette
Abstract: A current challenge for disease modeling and public health is to understand pathogen dynamics across infection scales from withinhost to betweenhost. Viral and immune response kinetics upon infection impact transmission to other hosts and feedback into populationwide immunity, all of which influence the severity, trajectory, and evolution of a spreading pathogen. In this talk, I will introduce structured partial differential equation models linking immunology and epidemiology in order to investigate coevolution of virus and host, multiscale data fitting, and impacts of dynamic host immunity from an individual to the whole population. We apply the models to vectorborne diseases, such as Rift Valley fever (RVF) and dengue (DENV), with immunological and epidemiological data. Using invasion dynamics analysis and multiscale numerical methods, we characterize different scenarios of virushost evolution and coexistence of viral strains under waning host crossimmunity. In the case of DENV, we recapitulate how intermediate levels of preexistent antibodies enhance infection within a host, and how to scale up to distributions of antibody levels among epidemiological classes in the host population to determine risk of severe DENV prevalence. These results have implications for optimal vaccination policy, and the modeling framework developed here is currently being applied to examine the emergence of COVID19 variants partially resistant to antibodies induced by host infection or vaccination. 
27 October 2022
Mathematical modeling of actin and myosin dynamics in the cell cortex during confined blebbased migration
Emmanuel AsanteAsamani
Clarkson University
Potsdam, New York
Abstract: The actin cortex is very dynamic during cell migration, permitting the rapid formation of leadingedge protrusions that facilitate the forward motion of cells. When cells move in confined environments, they can use excess intracellular pressure to detach and expand small regions of their membrane, completely disassemble the cortex beneath the protruded membrane (actin scar) and form a new cortex at the expanded membrane. This mode of migration, known as blebbing, has been observed in metastatic cancer cells moving in highly confined tumor environments, and may be used to evade cancer drugs that target nonblebbing modes of motility. The mechanism by which cells completely disassemble their cortex during blebbing is not fully understood and could shed light on the dynamic regulation of the actin cortex during cell migration. Recent experimental data in Dictyostelium discoideum cells reveals a local accumulation of myosin II, an essential motor protein capable on inducing contractile stress, in the actin scar prior to any significant disassembly of the cortex. Thus, our data suggests a role for the motor protein in cortex disassembly. In this talk, I will discuss our efforts to use mathematical models to explain the accumulation of myosin in the cortex during blebbing. In particular, I will present a minimal datadriven linear dynamical system (ODEs) of actinmyosin interaction whose parameters show that the accumulation is a result of an increase in the relative binding rate of myosin to the cortex. Stability analysis of the model and exploration of its parameter space reveals that the accumulation is robust to perturbations in the model parameters. I will also introduce a partial differential equation coupled with a system of ODEs that model the intracellular signaling pathway regulating the binding of myosin to the cortex. Numerical simulations of the model suggests that the increase in myosin’s binding rate is induced by the separation of the cell membrane from the cortex which occurs at the onset of blebbing. Together, our experiments and theory elucidate the regulation of cortex disassembly and more broadly shed light on how cells can use mechanical events, such as membrane separation, to regulate internal cell process during confined migration. 
3 November 2022 (Special Time 3:454:30)
Global dynamics of a cholera model with two nonlocal and delayed transmission mechanisms
XiangSheng Wang
Abstract: A nonlocal and delayed cholera model with two transmission mechanisms in a spatially heterogeneous environment is derived. We introduce two basic reproduction numbers; one is for the bacterium in the environment and the other is for the cholera disease in the host population. If the basic reproduction number for the cholera bacterium in the environment is strictly less than one and the basic reproduction number of infection is no more than one, we prove globally asymptotically stability of the infectionfree steady state. Otherwise, the infection will persist and there exists at least one endemic steady state. For the special homogeneous case, the endemic steady state is unique and globally asymptotically stable. Under some conditions, the basic reproduction number of infection is strictly decreasing with respect to the diffusion coefficients of cholera bacteria and infectious hosts. When these conditions are violated, numerical simulation suggests that spatial diffusion may not only spread the infection from the highrisk region to the lowrisk region but also increase the infection level in the highrisk region. 
10 November 2022
Algebraically, what can be said about the functions on a nonempty set S into itself; or do we need a left and a right distributive law?
Gary Birkenmeirer
Abstract: We discuss the evolution of algebraic systems motivated by the questions in the title of the talk. 
17 November 2022
On Zoom. contact Calvin Berry to request the link.
Direct Parallel in Time Solvers for Two Inverse PDE Problems
Jun Liu
Southern Illinois University Edwardsville
Abstract: In this talk, I will briefly introduce the diagonalizationbased parallel in time (PinT) algorithms, which show promising parallel efficiency for solving timedependent differential equations (ODEs and PDEs). However, such PinT algorithms were not applied to inverse PDE problems in literature. Within the framework of quasiboundary value regularization methods, we will present direct PinT solvers for solving two classical inverse PDE problems: Backward Heat Conduction Problem and Inverse Source Problem. The novel idea is to maneuver the flexibility of regularization methods for better structured linear systems that enable direct PinT solvers. The high efficiency of the proposed algorithms is illustrated by 1D and 2D numerical examples.
More technical details can be found in the paper: Fast parallelintime quasiboundary value methods for backward heat conduction problems and the arxiv preprint. For more PinT algorithms, I recommend the ParallelinTime website and also a report on related (iterative) ParaDIAG algorithms at arxiv. 
1 December 2022
This is a continuation of the colloquium of 11 November 2022
Algebraically, what can be said about the functions on a nonempty set S into itself; or do we need a left and a right distributive law?
Gary Birkenmeirer
Abstract: We discuss the evolution of algebraic systems motivated by the questions in the title of the talk.
Spring 2022 Schedule

20 January 2022
The BensonSolomon fusion systems
Justin Lynd
UL Lafayette
Abstract: A fusion system can be thought of as a "finite group at a prime p". After some historical remarks around the Classification of the Finite Simple Groups, I will explain what a pfusion system is. Most of the examples of fusion systems we know of come from finite groups, but there do exist many exotic examples, chiefly at odd primes. In fact, we know of just one infinite family of simple exotic fusion systems at the prime 2, the BensonSolomon fusion systems. In the second part of the talk, I will explain some coincidences that allow the BensonSolomon systems to exist and, time permitting, discuss some of what is known about them. 
10 March 2022
On Zoom. contact Leonel Robert to request the link
Coarse geometry and rigidity
Ilijas Farah
York University
Abstract: Coarse geometry is the study of metric spaces when one forgets about the small scale structure and focuses only on the large scale. For example, this philosophy underlies much of geometric group theory. To a coarse space one associates an algebra of operators on a Hilbert space, called the uniform Roe algebra. No familiarity with coarse geometry, operator algebras, or logic is required. After introducing the basics of coarse spaces and uniform Roe algebras, we will consider the following rigidity questions:
(1) If the uniform Roe algebras associated to coarse spaces X and Y are isomorphic, when can we conclude that X and Y are coarsely equivalent?
(2) The uniform Roe corona is obtained by modding out the compact operators. If the uniform Roe coronas of X and Y are isomorphic, what can we conclude about the relation between the underlying uniform Roe algebras (or about the relation between X and Y)?
The answers to these questions are fairly surprising. This talk is based on a joint work with F. Baudier, B.M. Braga, A. Khukhro, A. Vignati, and R. Willett. 
17 March 2022
On Zoom. contact Leonel Robert to request the link
Acoustic and quantum scattering on the line
Peter Gibson
York University
Abstract: The onedimensional Schrödinger equation, with its connection to quantum scattering experiments, is among the most intensively studied models in quantum mechanics. This talk presents a new approach to scattering theory motivated by applications in acoustic imaging. The central idea is a natural approximation scheme that leads to new theoretical and computational methods that allow one to analyze forward and inverse scattering problems beyond the scope of previous theory. In addition, the talk will describe some newly discovered mathematical connections between scattering theory, orthogonal polynomials and a remarkable Riemannian manifold. The talk is intended for a general audience; no background in mathematical physics is required. 
28 April 2022
On Zoom. contact Leonel Robert to request the link
Infinitely many signchanging solutions to a conformally invariant integral equation
Mathew Gluck
Towson University
Fall 2021 Schedule
We have a few speakers lined up already but still have room for more so let us know (contact Leonel Robert) if you want to speak or have a suggestion for a speaker. Check back for updates.

14 October 2021
On Zoom. contact Leonel Robert to request the link
Geometries of topological groups
Christian Rosendal
University of Maryland
Abstract: We will discuss how topological groups (of which Banach spaces are a particular example) come equipped with inherent geometries at both the large and small scale. In the context of Banach spaces, the ensuing study is part of geometric nonlinear analysis and we shall present various results and fundamental concepts dealing both with Banach spaces and more general topological groups appearing in analysis, dynamics and topology. 
15 October 2021 (1:00 pm Friday)
On Zoom. contact Leonel Robert to request the link
Bayesian jackknife empirical likelihood
Yichuan Zhao
Georgia State University
Abstract: Empirical likelihood is a very powerful nonparametric tool that does not require any distributional assumptions. Lazar (2003) showed that in Bayesian inference, if one replaces the usual likelihood with the empirical likelihood, then posterior inference is still valid when the functional of interest is a smooth function of the posterior mean. However, it is not clear whether similar conclusions can be obtained for parameters defined in terms of Ustatistics. We propose the socalled Bayesian jackknife empirical likelihood, which replaces the likelihood component with the jackknife empirical likelihood. We show, both theoretically and empirically, the validity of the proposed method as a general tool for Bayesian inference. Empirical analysis shows that the smallsample performance of the proposed method is better than its frequentist counterpart. Analysis of a casecontrol study for pancreatic cancer is used to illustrate the new approach. 
21 October 2021 (In person!)
Almost finite almost groups
Andrew Chermak
Kansas State University 
4 November 2021 (In Person!)
Accounting for Lack of Trust in Optional RRT Models
Sat Gupta
Professor and Head
Department of Mathematics and Statistics
University of North Carolina at Greensboro
Abstract: When conducting facetoface surveys containing sensitive questions, Social Desirability Bias (SDB) often leads to low response rate or worse, untruthful responding. Randomized Response Techniques (RRT) circumvent SDB by allowing respondents to provide scrambled responses. However, if respondents do not trust the RRT model, data accuracy will be compromised as shown and addressed in Young et al. (2019) and Lovig et al. (2021) for binary RRT models. Yet, no quantitative RRT model currently accounts for respondent lack of trust. We review the Young et al. (2019) and the Lovig et al. (2021) models, and propose an Optional Enhanced Trust (OET) Quantitative RRT model to mitigate respondent lack of trust by allowing additional noise to respondents who do not trust the basic Warner Additive Model. Using a combined measure of respondent privacy and model efficiency, we demonstrate both theoretically and empirically that the proposed OET model is superior to existing models.
This talk is based on joint work with Maxwell Lovig (University of Louisiana at Lafayette), Joia Zhang (University of Washington), and Sadia Khalil (Lahore College for Women University)
Spring 2021 Schedule

22 April 2021
on Zoom at 3:30 pm  social time at 3:00 pm contact Philip Hackney to request the link
Informative censoring
Gerhard Dikta
FH Aachen University of Applied Sciences
Based on an identifying Volterratype integral equation for a lifetime distribution F and randomly right censored observations, we solve the corresponding estimating equation by an explicit and implicit Euler scheme. Depending on the assumptions we make about the conditional expectation of the censoring indicator given the observation time, we derive the well know KaplanMeier and other established estimators of F under the explicit Euler scheme. Moreover, under the implicit Euler scheme, we obtain new presmoothed and semiparametric estimators of F. Some properties of the new semiparametric estimator are discussed and a real data application finalizes the presentation. 
15 April 2021
on Zoom at 3:30 pm  social time at 3:00 pm contact Philip Hackney to request the linkShowcase on Undergraduate Research
This week we have four presentations by some of our undergraduate mathematics majors
A Study of Natural Predator's Effectiveness at Controlling a Tick Population
Maxwell Lovig
(Faculty Advisors: Amy Veprauskas and Ross Chiquet)
Harvesting Strategies to Control Invasive Species
Madeleine Angerdina and Ian Bonin
(Faculty Advisors: Amy Veprauskas and Ross Chiquet)
Party Investment and Citizens' Willingness to Vote
Maxwell Reigner Kane
(Faculty Advisor: Ross Chiquet)
Realizing Finite Groups as Internal Automorphism Groups
Andrew Bayard
(Faculty Advisor: Justin Lynd) 
8 April 2021
on Zoom at 3:30 pm  social time at 3:00 pm contact Philip Hackney to request the link
Topology and Azumaya Algebras
Ben Williams
University of British Columbia
The algebra Mat_d(C) of d x d matrices over the complex numbers is a familiar one. A topological Azumaya algebra of degree d is a bundle of algebras, each isomorphic to the matrix algebra, i.e., it is a twisted family of matrix algebras. I will use the algebra structure of Mat_d(C) to produce a universal example of a topological Azumaya algebra: informally, an example that is as twisted as possible.
Classical Azumaya algebras are also twisted forms of matrix algebras, but here the twisting is purely algebraic rather than topological. They generalize central simple algebras over fields: a venerable area of study.
Topological Azumaya algebras are intimately related to “classical" Azumaya algebras. I will attempt to explain this, and show how you can use homotopy theory to produce examples of Azumaya algebras that behave very differently from central simple algebras. This talk is intended to be accessible to a general mathematical audience. 
25 March 2021
on Zoom at 3:30 pm  social time at 3:00 pm contact Philip Hackney to request the link
The hot spots conjecture can be false: Some numerical examples using boundary integral equations
Andreas_Kleefeld
Jülich Supercomputing Centre
Institute for Advanced Simulation
The hot spots conjecture is only known to be true for special geometries. It can be shown numerically that the hot spots conjecture can fail to be true for easy to construct bounded domains with one hole. The underlying eigenvalue problem for the Laplace equation with Neumann boundary condition is solved with boundary integral equations yielding a nonlinear eigenvalue problem. Its discretization via the boundary element collocation method in combination with the algorithm by Beyn yields highly accurate results both for the first nonzero eigenvalue and its corresponding eigenfunction which is due to superconvergence. Finally, numerical examples for easy to construct domains with up to five holes are provided which fail the hot spots conjecture as well. 
25 February 2021
on Zoom at 3:30 pm  social time at 3:00 pm contact Philip Hackney to request the link
Algebraic Ktheory for 2categories
Niles Johnson
Ohio State University Newark
Quillen recognized the higher algebraic Kgroups of a commutative ring R as homotopy groups of a certain topological space, BGL(R)^+. We review some of the basic definitions and computations via categorical algebra. We then describe how a 2categorical extension of this theory leads to a new model for K_3(R), together with more general applications. We will give a mild sampling of key technical details and close with some of the problems we're currently working on. The work we present is joint with GurskiOsorno, Fontes, and FontesGurski.
Fall 2020 Schedule

15 October 2020
Classification of purely infinite C*algebras
James Gabe
University of Southern Denmark
I will talk about the basics of the Elliott programme for classifying amenable (aka nuclear) C*algebras. I will focus on the case where my C*algebras are purely infinite. 
12 November 2020
on Zoom at 3:30 pm  social time at 3:00 pm contact Philip Hackney to request the link
Transfer systems and weak factorization systems
Angélica Osorno
Reed College
Ninfinity operads over a group G encode homotopy commutative operations together with a class of equivariant transfer (or norm) maps. Their homotopy theory is given by transfer systems, which are certain discrete objects that have a rich combinatorial structure defined in terms of the subgroup lattice of G. In this talk, we will show that when G is finite Abelian, transfer systems are in bijection with weak factorization systems on the poset category of subgroups of G. This leads to an involution on the lattice of transfer systems, generalizing the work of BalchinBearupPechRoitzheim for cyclic groups of squarefree order. This is joint work with Evan Franchere, Usman Hafeez, Peter Marcus, Kyle Ormsby, Weihang Qin, and Riley Waugh.
Spring 2020 Schedule

13 February 2020
Some applications of topology to the analysis of data
Jose Perea
Michigan State
Many problems in modern data science can be phrased as topological questions: e.g., clustering is akin to finding the connected components of a space, and tasks such as regression, classification and dimensionality reduction can be thought of as learning maps between structured spaces. I will describe in this talk how tools from classical algebraic topology can be leveraged for the analysis of complex data sets. Several illustrative examples will be provided, including applications to computer vision, machine learning and computational biology. 
20 February 2020
Equivalence relations and algebras of generalized matrices
HungChang Liao
University of Ottawa
To an equivalence relation one can naturally associate a collection of "generalized matrices". These matrices typically have infinitely many rows and columns, but otherwise form an algebra just like ordinary matrices. Since the fundamental work of F. Murray and J. von Neumann, the study of these algebras has evolved into a huge mathematical entity with applications to many other fields. A natural and important question is how these algebras interact with the original equivalence relations. We will give an introduction to some of the most important work regarding this question, with a focus on equivalence relations arising from group actions. The talk does not assume any background beyond (the ordinary) matrix algebras, a (tiny) bit of topology, and a (tiny) bit of measure theory. 
12 March 2020
The Wald Method versus The Score Method
Jie Peng
St Ambrose University
The Wald method and the score method are wellknown classical large sample methods of obtaining inference on parametric distributions. They are commonly used in hypothesis testing and interval estimation. In this talk, we describe these methods for finding confidence intervals and prediction intervals for the binomial, hypergeometric and Poisson distributions. For each distribution, we show via numerical comparison that the score method is better than the Wald method. The score method also provides satisfactory results even for somewhat small samples. Furthermore, We illustrate the construction of confidence intervals and prediction intervals using some practical examples.
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