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Spring 2018

Ricela Feliciano-Semidei
University of Montana – PhD Candidate

Understanding Conditional Probability with the Monty Hall Problem

Conditional probability is an important concept that is widely used in social sciences, natural sciences, health sciences and business. There is a high need to understand how students learn conditional probability and to develop effective teaching interventions to improve their learning experiences. This study developed a teaching intervention that incorporated the Monty Hall problem into a game learning model. Through the implementation of the teaching intervention in a statistics introductory course at college level, researchers investigated students’ conceptions on conditional probability. The impact of the teaching module on students’ learning was examined through pretest and posttest. Researchers identified the Law of Large Numbers as a key concept required prior to learning conditional probability. Findings from the study had implications to improve the teaching and learning of conditional probability.

Monday, January 29, 2018 at 3:00 p.m. in Math 103
Refreshments at 4:00 p.m. in Math Lounge 109

Michael Dorff – Brigham Young University

Analytic functions, harmonic functions, and minimal surfaces

Complex-valued harmonic mappings can be regarded as generalizations of analytic functions and are related to minimal surfaces which are beautiful geometric shapes with intriguing properties. In this talk we will provide background material about these harmonic mappings, discuss the relationship between them and minimal surfaces, present some new results, and pose a few open problems.

Monday, February 5, 2018 at 3:00 p.m. in room 103
Refreshments at 4:00 p.m. in Math Lounge 109

Vanni Noferini – University of Essex

Localization results for indefinite eigenvalue problems

Sylvester's law of inertia states that the number of positive, zero or negative eigenvalues of a matrix is invariant under congruence, and the same is true for pencils when at least one matrix is definite (and both are allowed to undergo independent congruences). Nothing was known thus far for indefinite pencils, and almost nothing for nonlinear problems. I will present new results of ours in this area, including inertia-based lower and upper bounds for the number of eigenvalues in a real interval. This talk is based on joint work with Yuji Nakatsukasa (University of Oxford).

Friday, February 16, 2018 at 3:00 p.m. in Math 103
Refreshments at 4:00 p.m. in Math Lounge 109

Matthias Chung – Department of Mathematics at Virginia Tech.

Computational Challenges of Inverse Problems

Inverse problems are omnipresent in many scientific fields such as systems biology, engineering, medical imaging, and geophysics. The main challenges toward obtaining meaningful real-time solutions to large, data-intensive inverse problems are ill-posedness of the problem, large parameter dimensions, and/or complex model constraints. This talk discusses computational challenges of inverse problems by exploiting a combination of tools from applied linear algebra, parameter estimation and optimization, and statistics. For instance, for large scale ill-posed inverse problems, approximate solutions are computed using a regularization method that solves a nearby well-posed problem.  Oftentimes, the selection of a proper regularization parameter is the most critical and computationally intensive task and may hinder real-time computations of the solution. We present a new framework for solving ill-posed inverse problems by computing optimal regularized inverse matrices. We further discuss randomized Newton and randomized quasi-Newton approaches to efficiently solve large linear least-squares problems, where the very large data sets present a significant computational burden (e.g., the size may exceed computer memory or data are collected in real-time). In this framework, randomness is introduced as a means to overcome computational limitations, and probability distributions that can exploit structure and/or sparsity are considered. We will present numerical examples, from deblurring, tomography, and machine learning to illustrate the challenges and our proposed methods.

Monday, March 5, 2018 at 3:00 p.m. in Math 103
Refreshments at 4:00 p.m. in Math Lounge 109

Zhuang Niu – University of Wyoming

The classification of C*-algebras

A C*-algebra is an algebra of bounded linear operators acting on a Hilbert space, closed under adjoint operation and closed under the norm topology. Prototype examples include the algebra of n by n matrices and the algebra of continuous function on a compact Hausdorff space, and in general, C*-algebras arise naturally in the studies of dynamical systems, mathematical physics, group theory, and representation theory, etc. In this talk, I will give an overview of the recent progress on the classification of C*-algebras using the K-theory information.

Monday, March 12, 2018 at 3:00 p.m. in Math 103
Refreshments at 4:00 p.m. in Math Lounge 109

Dr. Edray H Goins – Purdue University
Colloquium & Reception honoring Dr. Gloria Hewitt

Yes, Even You Can Bend It Like Beckham

In the 2002 film by Gurinder Chadha, character Jesminder 'Jess' Bhamra states "No one can cross a ball or bend it like Beckham'' in a reference to the international soccer star's ability to cause the ball to swerve. In 2010, French researchers Guillaume Dupeux, Anne Le Goff, David Quere and Christophe Clanet published a paper in the New Journal of Physics detailing both experimental and mathematical analyses of a spinning ball in a fluid to show that it must follow a spiral.

In this talk, we give an overview of their discussion by reviewing the Navier-Stokes equation in a Serret-Frenet coordinate system. This talk is dedicated to the memory of Angela Grant and her love of mathematics in sports.

Monday, March 19, 2018
Colloquium at 3:00 p.m. in Math 103
Reception at 4:00 p.m. in Dell Brown Room in Turner Hall

Wend Werner – University of Muenster in Germany

Monday, April 2, 2018 at 3:00 p.m. in Math 103
Refreshments at 4:00 p.m. in Math Lounge 109

Saskia J. C. E. van Boven
Radboud Docenten Academie

Monday, April 9, 2018 at 3:00 p.m. in Math 103
Refreshments at 4:00 p.m. in Math Lounge 109

Nhan Nguyen
University of Montana – PhD Candidate

Monday, April 16, 2018 at 3:00 p.m. in Math 103
Refreshments at 4:00 p.m. in Math Lounge 109

Atish Mitra – Montana Tech

Monday, April 23, 2018 at 3:00 p.m. in Math 103
Refreshments at 4:00 p.m. in Math Lounge 109

Department of Mathematical Sciences Awards Ceremony

Thursday, April 26, 2018 at 4:00-5:00 p.m.
The Dell Brown Room in Turner Hall
Refreshments at 3:30 p.m.

Ellie Bayat Mokhtari
University of Montana – PhD Candidate

Monday, April 30, 2018 at 3:00 p.m. in Math 103
Refreshments at 4:00 p.m. in Math Lounge 109

Available Date

April 30