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 VUB 221, unless mentioned otherwise. This is the Undergraduate Building (marked "N" in the campus map), 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.

slides
Jan 23 Michael Jordan, UC Berkeley (MBI seminar) Machine Learning: Dynamical, Economic and Stochastic Perspectives
Jan 31 Caroline Uhler, MIT SIAM PNW Seminar at 3 PM in VUB 221:
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.

Join on GotoMeeting.

Feb   6 Adam Erickson, WSUV Validation of a simple biogeochemistry variant of SORTIE-PPA in two temperate forests using the Erde modeling framework

Abstract (click to read)

While vegetation model development has accelerated over the past three decades, less progress has been made in software interfaces that bind models and data. Such interfaces become necessary as model complexity increases, making models more cumbersome for new users to parameterize, run, and analyze. Furthermore, model wrappers may provide new modeling capabilities, such as Bayesian optimization. Toward this end, we have developed a simple geoscientific simulation model API and toolkit in R and Python known as the Earth-systems Research and Development Environment (Erde). While Erde is primarily intended for vegetation models, its data structures and algorithms are applicable across a range of geoscientific modeling domains.

We demonstrate an application of Erde with a simple biogeochemistry variant of SORTIE-PPA known as PPA-SiBGC. The PPA-SiBGC model combines the Perfect Plasticity Approximation with explicit above-and-below-ground biogeochemical pools and simple flux models. We parameterized, ran, and validated PPA-SiBGC at two research forests in the Eastern United States: (1) Harvard Forest, Massachusetts (HF-EMS) and (2) Jones Ecological Research Center, Georgia (JERC-RD). We assessed model fitness using these temporal metrics: net ecosystem exchange, aboveground net primary production, aboveground biomass, C, and N, belowground biomass, C, and N, soil respiration, and, species total biomass and relative abundance. Without applying any optimization, we found that a simple biogeochemistry variant of SORTIE-PPA was able to outperform an established forest landscape model (LANDIS-II NECN) across the metrics tested. While LANDIS-II NECN showed better NEE fit, PPA-SiBGC demonstrated better overall correspondence to field data for both sites (HF-EMS: \(\overline{R^2} = 0.73, +0.07\), \(\overline{RMSE} = 4.68, -9.96\); JERC-RD: \(\overline{R^2} = 0.73, +0.01\), \(\overline{RMSE} = 2.18, -1.64\))

Feb 13 No seminar
Feb 20 3-Minute Thesis Contest prep
Feb 28 Rebecca Doerge, Carnegie Mellon Math Colloquium at 4:10 PM in VECS 309:
The Future of Statistical Bioinformatics and Genomics in the Automated World of Agriculture

Abstract (click to read)

World population is expected to reach 9.6 billion by 2050, and crop yields are not keeping pace fast enough to avoid widespread food shortages. Modern molecular breeding programs are extremely effective, and can generate annual yield increases of around 4%. However, it is not scalable due to both the human labor and expertise required. Employing robots in agriculture has great potential to address chronic issues that are challenging food systems in the developing world. While quantitative trait locus (QTL) mapping associates molecular with variation in traits, there are new challenges for the statistical bioinformatics and genomics communities in how data are collected, stored and analyzed with respect to understanding relationships between genetic composition and phenotype. Using automation, the expectation is a dramatic increase, factor of 100+, in the number of individuals in the breeding population that can be evaluated. Artificial intelligence based technologies and analysis allow rapid evaluation, and thus make molecular breeding adaptive to environmental and other changes. Automated phenotyping is a transformational technology that can be applied worldwide.

Mar   6 Nathaniel Saul, WSUV Stitch-fix for Mapper

Abstract (click to read)

Mapper is a powerful construction from topological data analysis that is designed for the analysis and visualization of multivariate data. We investigate a method for stitching a pair of mappers together, and study a topological notion of information gain as well as a general measure of correlation during such a process. We are inspired by the ideas of stepwise regression for model selection and of scatterplot matrices for visualization, and introduce a topological analogue of the scatterplot matrix for the mapper, and study the degree of topological correlation between filter functions.

This is joint work with Bei Wang and Bala Krishnamoorthy.

Mar 13 Spring break (no seminar)
Mar 20 Andreas Wild, Intel labs Loihi - Intel's neuromorphic computing platform

Abstract (click to read)

Loihi is a massively parallel, event-driven, in-memory hardware architecture with on-chip learning for accelerating energy-efficient neural network computations which has been developed by Intel Labs' Neuromorphic Computing Lab. Possible application areas span from small edge devices, autonomous driving and robotics to the cloud.

This talk will give an introduction to neuromorphic computing, the Loihi architecture and provide an overview over our latest algorithmic research results to stimulate discussion on potential application areas.

Mar 21 Viktoria Taroudaki, EWU. Math Colloquium at 4:10 PM in VECS 309:
Using optimization and statistics to improve images and acoustical signals

Abstract (click to read)

Images that imaging devices record and sounds that microphones record are corrupted by blur and noise. In this talk, we will see how a filtering method can help eliminate the effects of blur and noise and under what conditions. The results of the filtering method will be compared in both the two- and the one-dimensional cases and we will see one uncertainty quantification measure.

Apr   3 Nella Ludlow, WSU EECS Quantum computing
Apr 10
Apr 17
Apr 24 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: Wed Mar 20 16:20:18 PDT 2019