WSU Vancouver Mathematics and Statistics Seminar

WSU Vancouver Mathematics and Statistics Seminar (Fall 2019)


Welcome to the WSU Vancouver Seminar in Mathematics and Statistics! The Seminar meets on Wednesdays at 2:10-3:00 PM in VUB 222, unless mentioned otherwise. This is the Undergraduate Building (marked "N" in the campus map), where all Math and Stat faculty sit. The seminar is open to the public, and here is some information for visitors.

Students could sign up for Math 592 (titled Seminar in Analysis) for 1 credit. Talks will be given by external speakers, as well as by WSUV faculty and students. Contact the organizer Bala Krishnamoorthy if you want to invite a speaker, or to give a talk.

Seminars from previous semesters


Date Speaker Topic Slides
Aug 21 Organizational meeting
Aug 28 Nate May, WSUV Scheduling of ticket inspectors in Deutsche Bahn inter-city express trains

Abstract (click to read)

In many transit systems including the Duetsche Bahn Railway in Europe, passengers are legally required to purchase a ticket before riding the train, but are not physically forced to do so. Instead, train inspectors are deployed throughout the transit system to inspect passengers, and fine people who are riding the train illegally. With approximately 7 million people taking the train every day, it is crucial that inspectors are deployed in a manner that will maximize the number of passengers that are controlled.

This summer I had the opportunity to travel to Germany for the GRIPS program offered through IPAM, in order to try and model/solve this problem for the Deutsche Bahn Railway System. In this talk, I will discuss how my team and I approached the problem, and some of the preliminary results we obtained. I will also discuss my experiences with the IPAM GRIPS program, in case anyone is interested in participating in it next summer.

Sep  4 Nathan Baker, PNNL VPR Distinguished Lecture at 3 PM in VECS 125:
Accelerating Scientific Discovery through QIS

Abstract (click to read)

Quantum information science (QIS) is an emerging field with an immense potential to revolutionize various science and engineering applications involving quantum computing, communication, and quantum simulation. The talk will provide an overview of the QIS area, including its key applications, challenges, and opportunities, as well as key quantum related activities happening at PNNL.

We will join the lecture from Pullman via Zoom.

Sep 11 Casandra Kerr, Seb Baldevisio, U.S. Bank Model Risk Management at U.S. Bank slides
Sep 18 No seminar
Sep 25 Bala Krishnamoorthy, WSUV Robust feasibility of nonlinear systems using degree theory

Abstract (click to read)

We consider new approaches to characterize the robustness of solutions to a system of nonlinear equations. This problem arises in many applications such as the power grid and other infrastructure networks. We use techniques from algebraic topology (topological degree theory) to characterize the robustness margin of such systems of equations. We then cast the problem of checking for the specified conditions as a nonlinear optimization problem. Based on this formulation, we develop efficient computational techniques to estimate lower and upper bounds for the robustness margin.

A preprint is available on arXiv:1907.12206.

Sep 27 Juanjuan Fan, SDSU Math colloquium, 4:10–5 PM, VUB 311
Matching Methods for Observational Data with Small Group Sizes and Missing Covariates

Abstract (click to read)

In order to derive unbiased inference from observational data, matching methods are often applied to produce balanced treatment groups in terms of relevant background variables. Although many matching algorithms exit in the literature, most require a large control reservoir and can not deal with missing data. Random forest, averaging outcomes from many decision trees, is non-parametric in nature, can deal with missing data in the tree building process, and can produce more accurate and less model dependent estimates of propensity scores as well as a proximity matrix. In this study, iterative matching algorithms are developed in order to form balanced samples based on limited sample sizes for both groups. A R package implementing the proposed methods has also been developed. The proposed methods are applied to two data sets, arising from studies of autism spectrum disorder (ASD) and student success.

We will join by Zoom.

Oct   1 Carlos Castillo-Chavez, ASU Math colloquium, 4:10–5 PM, VUB 311
Role of social dynamics and evolution on the spread and control of infectious disease

Abstract (click to read)

I will revisit the field classical mathematical epidemiology starting from the contributions of Sir Ronald Ross in 1911. Extensions of Ross’ framework and recent applications will be discussed in the context of the study of the spread of influenza in the presence of cross-immunity as well disease dynamics in models that include social dynamics, in particular on those that incorporate the role of individual decisions. Examples will be provided from communicable diseases under scenarios that account for various modes of transmission.

We will join by Zoom.

Oct   9 Nate May, WSUV An Intuitive Approach to the UMAP Algorithm for Dimension Reduction

Abstract (click to read)

The aim of this talk will be to try and understand the UMAP algorithm (Uniform Manifold Approximation and Projection) from a "user's" perspective. Most of our effort will be spent trying to understand how the algorithm works intuitively, rather than understand the mathematical machinery underpinning the algorithm.

Oct 16 No seminar
Oct 23 Anna Ritz, Reed College Pathway Mining from Large Interaction Networks

Abstract (click to read)

Biological signaling pathways describe a series of molecular reactions that are initiated by an external signal and culminate in a specific cellular response (for example, a cell may grow, divide, move, or die). The wealth of publicly-available data containing signaling reactions provide an opportunity for automatically reconstructing such pathways using computational approaches. In this talk, I will describe network-based methods that automatically reconstruct signaling pathways from protein-protein interaction data. Our methods successfully reconstruct human signaling pathways with much higher precision and recall than several state-of-the-art graph algorithms. Further, our methods suggest experimentally testable hypotheses about canonical signaling. Extensions of this work have constrained pathway reconstructions by considering the spatial localization of interactions within the cell, and I will also describe our first steps in using ideas from pathway reconstruction to discover signaling pathways that are altered in cancer.

Nov   6 Enrique Alvarado, WSU
Nov 14 Kelly McConville, Reed College 4:10–5 PM
Regression Trees and Douglas Firs: Improving Federal Statistics with Machine Learning Methods

Abstract (click to read)

For survey stats practitioners, it sometimes feels like the sky is falling. Response rates are declining. Data collection costs are increasing. Federal budgets are shrinking. But, with the advent of big data and advancements in technology, a wealth of new data sources, such as satellite imagery, are available to supplement survey data. And, flexible, predictive models, such as regression trees and elastic net regression, can be an effective tool to combine these different data sources and to produce more efficient estimators. Drawing on my collaborations with the U.S. Bureau of Labor Statistics and the U.S. Forest Inventory and Analysis Program, we'll explore the utility of predictive models in survey estimation.

Nov 20 Matt Sottile, WSU and Noddle

Last modified: Tue Oct 08 15:19:18 PDT 2019