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. 
slides 
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. 

Mar 28  No seminar  
Apr 4  Bala Krishnamorthy and Nathaniel Saul, WSUV 
Robustness of Solutions to Systems of Quadratic Equations AND A Stitchfix for Mapper 

Apr 11  Olga Rumyantseva, WSUV 
Forest Canopy Stability in the Perfect Plasticity Approximation (PPA) Model
Abstract (click to read)The Perfect Plasticity Approximation (PPA) is system of equations that predicts the largescale dynamics of forest stands. The model is computationally efficient and is employed to scaling of vegetation dynamics and carbon and nutrient cycles using parameter values and functional forms of individual tree species. The model includes the system of McKendrick–von Foerster partial differential equations (one for every tree species) and an integral equation (the PPA equation). The McKendrick–von Foerster equation is a conservation lawbased firstorder hyperbolic partialdifferential equation similar to the advection or transport equations that can be solved using the method of characteristics; however, the nonlinear PPA integral equation presents a substantial challenge. In this presentation we will discuss structural stability of the PPA model, including the observed tendency of trees with particular shapes to produce unstable canopies. We will introduce analytic conditions of canopy stability an arbitrary crown shapes. 

Apr 18  Noah Doss, WSUV 
PickUp Parity: An Exploration of Optimal Partitioning for Competitive Team Activities
Abstract (click to read)For situations in which a population of individuals may be divided into groups, a complete graph with weighted edges captures the complete system of pairing decisions and their value to the individuals. I will discuss how assigning particular discrete weights and participating in "graph pruning", among other methods, provides strategies for creating "fair teams", defined as groups of relatively even total skill rating, while also paying attention to teammate preference. Focusing on cliques of thresholded subgraphs, and contracted edges derived from the stable marriage algorithm, I will approach the problem of practically maximizing a participant responsedependent utility function and balancing the need for team equity with the desire of individuals to participate alongside favored or satisfactory teammates. 

Apr 25  Adam Erickson, WSUV 
Toward the efficient approximation of energetic and
biogeochemical processes in terrestrial biosphere
models: nextgeneration forest models
Abstract (click to read)Over the past 80 years, forest models have progressed from empirical linear models to physiological process models to hybrids of both. Early empirical models simulated the growth and yield of pure evenaged forest stands. Four decades later, individualbased multispecies physiological gap models emerged with JABOWA and FORET. Despite reasonable fidelity to Moore's Law, efforts to upscale gap models remain limited by algorithmic and parametric complexity, with existing solutions relying on sampling strategies. Following model reduction techniques demonstrated in LANDSIM, a new class of model emerged blending empirical and physiological components. Such hybrid models include the two popular cohort models, LANDISII and SortiePPA. While the former is based on species life history strategies and the CENTURY model, the latter is based on phototropism and crown plasticity combined with recent biogeochemistry models. Here, I discuss approximations used in LANDISII and SortiePPA. I also discuss combining the PPA cohort model with a bigleaf biogeochemistry model in the latest version of SortiePPA, known as SortiePPABGC, intended to inform future terrestrial biosphere models. Finally, I discuss the development of the forestmodels Python library, designed to provide a unified interface to forest biogeochemistry models, including helper functions for data acquisition, parameter estimation, and model intercomparison. This is joint work with Robert Scheller, Nikolay Strigul, and Melissa Lucash. 