WSU Vancouver Mathematics and Statistics Seminar (Spring 2018) Welcome to the WSU Vancouver Seminar in Mathematics and Statistics! The Seminar meets on Wednesdays at 1:102 PM in VLIB 240. This is the building marked "H" in the campus map, and is near the Undergraduate building (marked "N") where all Math/Stat faculty have offices. 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 10  No seminar Science on Tap Lecture by Leslie New  
Jan 17  Angelica Osorno, Reed College 
Why should we care about category theory?
Abstract (click to read)One of the first mathematical concepts we learn as children is counting, and when we do so, we think of counting the number of elements in a specific set. Soon after, we forget about sets and we just consider the abstract numbers themselves. This abstraction simplifies many things, but it also makes us forget about some structure that we had when we were thinking about sets. That structure can be encoded by a category. In this talk we will describe certain concepts in category theory, and you will realize that in most of your mathematics classes you have been working with categories, you just didn't know about it. There will be plenty of examples that will show that category theory provides a unifying language for mathematics, and that many constructions are more naturally understood when they are seen through the categorical lens. 

Jan 24  Alex Dimitrov, WSU 
Modeling of perceptual invariances in biological sensory
processing
Abstract (click to read)A problem faced by all perceptual systems is natural variability in sensory stimuli associated with the same object. This is a common problem in sensory perception: Interpreting varied optical signals as originating from the same object requires a large degree of tolerance [1]. Understanding speech requires identifying phonemes, such as the consonant /g/, that constitute spoken words. A /g/ is perceived as a /g/, despite tremendous variability in acoustic structure that depends on the surrounding vowels and consonants [2]. A major goal of an object recognition problem then is the ability to identify individual objects while being invariant to changes stemming from multiple stimulus transformations. In an ongoing project [3], we are testing the hypothesis that broad perceptual invariance is achieved through specific combinations of what we term locally invariant elements. The main questions we would like to address are: 1. What are the characteristics of locallyinvariant units in sensory pathways? 2. How are biological locallyinvariant units combined to achieve broadly invariant percepts? 3. What are the appropriate mathematical structures with which to address and model these sensory processes? The mathematical aspects of the research involve an interesting combination of probability theory (a must in the study of biological sensory systems) and group theory, needed to characterize invariants and symmetries. 
video 
Jan 31  Nathaniel Saul, WSU  Snippets from the Joint Mathematics Meetings, 2018  
Feb 7  No seminar  
Feb 14  Zachary Robbins, PSU 
The parameterization of PPA formulas Using a SORTIEND
model for Harvard forest
Abstract (click to read)Spatiallyimplicit forest growth models, such as the perfect plasticity approximation (PPA), allow for the computationally efficient scaling of forest dynamics to the landscape scale, by using simplified mechanisms of individual tree competition. The parameterization and calibration of PPA using empirical data is challenging, limiting its applications in biogeochemistry and forest modeling. In contrast, the statistical methodology for parameterization of spatially explicit individualbased forest models, such as SORTIE, is well developed. In this work we parameterize the spatiallyimplicit PPA model by calibrating the spatiallyexplicit SORTIEND model using Harvard forest as a test site. Despite the two models using different tree competition mechanisms, both predicted similar biomass dynamics. Community composition diverged in the two models: between an Eastern hemlock dominated system in SORTIEND and a red maple dominated in PPA. This illustrates that the different competition mechanisms employed in spatiallyexplicit and implicit models can lead to different predictions of forest successions, and provides a method for an initial parametrization of PPA using SORTIEND which is sufficient for scaling of biomass dynamics, but requires further calibration for species dynamics. 

Feb 21 


Feb 28  Brandon Edwards, Intel 
The Curse of Dimensionality and Image Recognition
Abstract (click to read)Despite the high testaccuracy of state of the art image recognition models, optimization methods can be used to find "worstcase" images that are similar to test images from a human perspective, but for which the model fails its image recognition task. Though high testaccuracy would indicate that these images are rare, this phenomenon can be used by an adversary who will benefit from model failure and can alter either the physical space or image stream to match the worstcase image. For this reason, these worstcase images are known in the literature as "adversarial examples". I will give an overview of this space, and present recent results from another group that further highlight the connection between the existence of adversarial examples and the large dimensionality of the model input. 

Mar 7  WA State High School Regional Math Contest (no seminar)  
Mar 14  No seminar (Spring break)  
Mar 21  Andrew Bray, Reed College 
infer: A Framework for Tidy Statistical Inference
Abstract (click to read)The classical paradigm for null hypothesis significance testing has suffered from misapplication and misinterpretation for many years but it reached a fevered pitch when the American Statistical Association issued a statement on pvalues in 2014. In this talk we will consider an approach to formulating classical inference that is expressive of the underlying concepts. This approach is implemented in infer, a new package for the R statistical language. 