Colloquia & Seminars

Colloquia & seminar talks are scheduled from 1:30pm - 2:45pm on Thursday each week and usually take place in DMSC 104, unless otherwise noted below. Speakers give 50-minute presentations on various mathematical and statistical topics.

If you would like to meet with a speaker, please contact math@unr.edu to schedule a meeting. To receive email announcements about future talks and events, please subscribe to our email list by sending an email to sympa@lists.unr.edu with a blank subject line and the main body 'subscribe mathstat-announce EmailAddress FirstName LastName'.

We look forward to your participation in our upcoming colloquia!

Colloquia and Seminar Talks Schedule
DateSpeakerInstitutionTitleRoom
Sep. 13, 2018 Agnieszka Wylomanska Wroclaw University of Science and Technology How to Model Data with Anomalous Diffusion Behavior?
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The classical financial models are based on the standard Brownian diffusion-type processes. However, in the exhibition of some real market data (like interest or exchange rates) we observe characteristic periods of constant values. Moreover, in the case of financial data, the assumption of normality is often unsatisfied. In such cases the popular Vasiček model, that is a mathematical system describing the evolution of interest rates based on the Ornstein-Uhlenbeck process, seems not to be applicable. Therefore, we propose an alternative approach based on a combination of the popular Ornstein-Uhlenbeck process with a stable distribution and subdiffusion systems that demonstrate such a characteristic behavior. The probability density function of the proposed process can be described by a Fokker-Planck type equation and therefore it can be examined as an extension of the basic Ornstein-Uhlenbeck model. We propose the parameter's estimation method and calibrate the subordinated Vasiček model to the interest rate data.

DMSC 104
Sep. 20, 2018 Wei Yang UNR Overview of UNR School of Community Health Sciences
Biostatistics Program: Faculty, Research Interests and Projects
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DMSC 104
Sep. 27, 2018 Daniel Lautzenheiser UNLV Generalized Apollonian Packings and Hausdorff Dimension
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In this talk, we discuss counting methods which admit rigorous upper and lower bounds on the Hausdorff (or Besikovitch) dimension of two generalized Apollonian circle packings. We find that the Hausdorff dimension of each packing is strictly greater than that of the Apollonian packing, supporting the unsolved conjecture that, among the many possible disk tilings of the plane, the Apollonian packing has the smallest possible residual set dimension. The obtained bounds are also consistent with calculated heuristic estimates.

DMSC 104
Oct. 4, 2018 Mark Colarusso University of South Alabama Gelfand-Zeitlin Integrable Systems: Where Linear Algebra, Geometry, & Representation Theory meet
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In the 19th century, physicists were interested in determining the conditions under which the equations of motion for a classical mechanical system could be found by integrating a finite number of times. Such a system was said to be completely integrable. Using symplectic geometry, we can generalize the notion of an integrable system beyond the realm of physics and into Lie theory and representation theory. Such "abstract" integrable systems can be used to geometrically construct infinite dimensional representations of Lie algebras.
In this talk, I will discuss a family of integrable systems, the Gelfand-Zeitlin systems that arise from purely linear algebraic data. For an n x n complex matrix X, we consider the eigenvalues of all the i x i submatrices in the top left hand corner of X. These are known as Ritz values and play an important role in numerical linear algebra. We will see how questions about Ritz values naturally lead to the construction of the Gelfand-Zeitlin integrable systems. I will explain results about the geometric properties of these systems and indicate how they answer questions of Parlett and Strang about Ritz values. I will also show how this research provides the foundation for the geometric construction of a category of infinite dimensional representations of certain classical Lie algebras using the theory of quantization.

DMSC 104
Oct. 11, 2018 Allison Moore UC Davis Band surgery along knots and site-specific recombination on
circular DNA
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Band surgery is a topological operation that transforms a
link into a new link. When the operation is compatible with the orientations of the links involved, it is called coherent band surgery, otherwise it is called non-coherent. While coherent band surgery is relatively well-understood, non-coherent band surgery is less predictable. We will classify all band surgery operations from the trefoil knot to the $T(2, n)$ torus knots and links, by way of a related three-manifold problem that we solve by studying the Heegaard Floer d-invariants under integral surgery along knots in the lens space $L(3,1)$. Band surgery on knots is of independent interest in the biological sciences; it is especially important in modeling the action of enzymatic complexes on circular DNA molecules. We will discuss how reconnection by site-specific recombinases is modeled by
band surgery on knots and links and give some insight as to why torus knots are of special relevance in this context. Parts of this project are joint work with Lidman and Vazquez.

DMSC 104
Oct. 18, 2018 Edward J Bedrick University of Arizona Data Reduction Prior to Interface: What are the Consequences of Using Principal Component Scores to Make Group Comparisons in a Student's T-Test or ANOVA?
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There has been a significant recent development of statistical methods for inference with high-dimensional data. Despite these developments, biomedical researchers and computational scientists often use a simple two-step step process to analyze high dimensional data. First, the dimensionality is reduced using a standard technique such as principal component analysis, followed by a group comparison using a t-test or analysis of variance. In this talk, I will try to untangle a number of issues associated with this approach, starting with the simplest but most vexing question (since this is left unstated) - what hypothesis is being tested? I will use a combination of approaches, including asymptotics, analytical construction of worst case scenarios, and simulation based on actual data to address whether this approach is sensible. Although asymptotics will consider a non-sparse setting, some discussion of implications in sparse problems will be given.

DMSC 104
Oct. 25, 2018 Brandon Koch School of Community Health Sciences, UNR A New Approach for Variable Selection in Causal Inference
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Estimating the causal effect of a binary intervention or action (referred to as a "treatment") on a continuous outcome is often an investigator's primary goal. Randomized trials are ideal for estimating causal effects because randomization eliminates selection bias in treatment assignment. However, randomized trials are not always ethically or practically possible, and observational data must be used to estimate the causal effect of treatment. Unbiased estimation of causal effects with observational data requires adjustment for confounding variables that are related to both the outcome and treatment assignment. Adjusting for all measured covariates in a study protects against bias, but including covariates unrelated to outcome may increase the variability of the estimated causal effect. Standard variable selection techniques aim to maximize predictive ability of a model for the outcome and are used to decrease variability of the estimated causal effect, but they ignore covariate associations with treatment and may not adjust for important confounders weakly associated to outcome. In this talk, I will discuss GLiDeR (Group Lasso and Doubly Robust Estimation), a novel variable selection technique for identifying confounders and predictors of outcome using an adaptive group lasso approach that simultaneously performs coefficient selection, regularization, and estimation across the treatment and outcome models. The selected variables and corresponding coefficient estimates are used in a standard doubly robust average causal effect estimator. I provide asymptotic results that show, for a broad class of data generating mechanisms, GLiDeR yields a consistent estimator of the average causal effect when either the outcome or treatment model is correctly specified. A simulation study shows that GLiDeR is more efficient than doubly robust methods using standard variable selection techniques and has substantial computational advantages over a recently proposed doubly robust Bayesian model averaging method. We illustrate our method by estimating the average causal treatment effect of bilateral versus single-lung transplant on forced expiratory volume in one year after transplant using an observational registry.

DMSC 104
Nov. 1, 2018 Huixi Li UNR Why is 1 + 1 greater than 1 + 2 in number theory?
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In additive number theory, 1 + 1 means the strong Goldbach conjecture, which states that every even integer greater than two can be written as the sum of two primes, while 1 + 2 means a theorem by Chen on representing large even integers as the sum of a prime and an almost prime. In the first part of the presentation, I will explain why 1 + 1 is greater than 1 + 2.
In 1931 Estermann proved a theorem that every sufficiently large integer is a sum of a prime and a square-free number. If the strong Goldbach's conjecture on even numbers and the Lemoine's conjecture on odd numbers are both true, then we know every integer greater than three is a sum of a prime and a square-free number with at most two prime divisors. In the second part of the presentation, I will talk about the result that every sufficiently large odd integer is a sum of a prime and a square-free integer with at most three prime divisors. Then together with Chen's theorem, we know every sufficiently large integer is a sum of a prime and a square-free number with at most three prime divisors, which improves the theorem by Estermann. We can think of this result as 1 + 3.

DMSC 104
Nov. 8, 2018 Noah Forman University of Washington TBA
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DMSC 104
Nov. 15, 2018 Dan Nakano University of Georgia TBA
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DMSC 104
Nov. 29, 2018 Swatee Naik NSF TBA
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DMSC 104
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