WSU Vancouver Mathematics and Statistics Seminar

WSU Vancouver Mathematics and Statistics Seminar (Spring 2019)

Welcome to the WSU Vancouver Seminar in Mathematics and Statistics! The Seminar meets on Wednesdays at 1:10-2:00 PM in VMMC 23, unless mentioned otherwise. This is the Multimedia Classroom Building (marked "J" in the campus map), which is next to the Undergraduate Building (VUB, marked "N") where all Math/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
Jan   9 Organizational meeting
Jan 16 Andrew Fowler, Nuance and OHSU Improved inference and autotyping in EEG-based brain computer interface typing systems

Abstract (click to read)

The RSVP Keyboard is a typing system for people with severe physical disabilities, specifically those with locked-in syndrome (LIS). It uses signals from an electroencephalogram (EEG) combined with information from an n-gram language model to select letters to be typed. One characteristic of the system as currently configured is that it does not keep track of past EEG observations, i.e., observations of user intent made while the user was in a different part of a typed message. We present a principled approach for taking all past observations into account, and show that this method results in a 20% increase in simulated typing speed under a variety of conditions on realistic stimuli. We also show that this method allows for a principled and improved estimate of the probability of the backspace symbol, by which mis-typed symbols are corrected. Finally, we demonstrate the utility of automatically typing likely letters in certain contexts, a technique that achieves increased typing speed under our new method, though not under the baseline approach.

Jan 23
Jan 31 Caroline Uhler, MIT SIAM PNW Seminar at 3 PM:
From Causal Inference to Gene Regulation

Abstract (click to read)

A recent break-through in genomics makes it possible to perform perturbation experiments at a very large scale. The availability of such data motivates the development of a causal inference framework that is based on observational and interventional data. We first characterize the causal relationships that are identifiable from interventional data. In particular, we show that imperfect interventions, which only modify (i.e., without necessarily eliminating) the dependencies between targeted variables and their causes, provide the same causal information as perfect interventions, despite being less invasive. Second, we present the first provably consistent algorithm for learning a causal network from a mix of observational and interventional data. This requires us to develop new results in geometric combinatorics. In particular, we introduce DAG associahedra, a family of polytopes that extend the prominent graph associahedra to the directed setting. We end by discussing applications of this causal inference framework to the estimation of gene regulatory networks.

Feb   6
Feb 13
Feb 20
Feb 27
Mar   6 HS Math contest (no seminar)
Mar 13 Spring break (no seminar)
Mar 20
Mar 27 Daniel Taylor-Rodriguez, Portland State U. A Bayesian nonparametric multiple testing procedure for comparing several treatments against a control

Abstract (click to read)

We propose a Bayesian nonparametric strategy to test for differences between a control group and several treatment regimes. Many of the existing tests for this type of comparison are based on the differences between location or scale parameters. In contrast, our approach identifies differences across the entire distributions, avoids strong modeling assumptions over the distributions for each treatment, and accounts for multiple testing through the prior distribution on the space of hypotheses. The proposal is compared to other commonly used hypothesis testing procedures under simulated scenarios.

Last modified: Tue Jan 08 23:34:18 PST 2019