The Applied Mathematics and Statistics Colloquium takes place at 3 p.m. on Fridays in Chauvenet Hall room 143. Attendance is in person or via Zoom.  Please contact Jennifer Ryan at for further information and the Zoom link and password. 

View colloquium videos on YouTube.

Spring 2022

January 21Information session on organizations within AMS helping to make a difference
January 28Paul Martin, Applied Mathematics and Statistics, Colorado School of Mines
Solving Laplace's equation in a tube: how hard can it be?

The title problem arises in classical fluid dynamics, and in steady-state diffusion and wave problems. It is almost trivial when there is nothing in the tube apart from flowing fluid, but it becomes much more interesting when the tube contains an obstacle. A related problem is: if I send a wave down a tube, how much of it is reflected by the obstacle? I shall discuss properties of the solution, and methods for approximating the solution.

February 4Federico Municchi, Research Associate in Computational Fluid Dynamics, Colorado School of Mines
Combining phase field and geometric algorithms for the numerical simulation of multiphase flows

Phase field methods are gaining momentum in science and engineering to model multicomponent and multiphase systems thanks to their thermodynamically consistent formulation and the general smoothness of the resulting fields. In fact, they provide a framework to include complex physical processes (such as phase-change) and result in less spurious oscillations when dealing with surface tension, compared to other methods like the volume of fluid. The Cahn-Hilliard equation is the principal governing equation in the phase field method as it results from a minimization of the free energy functional and thus includes all the relevant physical phenomena such as phase-change and surface tension forces. However, its solution is not straightforward as it is a fourth-order non linear partial differential equation. A number of explicit methods have been proposed in literature together with an implicit mixed formulation. Segregated implicit algorithms are seldom used due to stability issues. In this work, we present a novel segregated algorithm for the solution of the Cahn-Hilliard equation based on the incomplete block Schur preconditioning technique. Performance and accuracy of the algorithm are compared against a block-coupled mixed formulation and the standard Volume Of Fluid method for a number of cases. We also illustrate several applications of the method to multiphase flows with phase change, where the Cahn-Hilliard equation is coupled with the Navier-Stokes equations and the energy conservation equation. In this circumstance, geometric algorithms are integrated with the phase field method to preserve the sharpness of the interface when required.
February 11Ebru Bozdag, Department of Geophysics, Colorado School of Mines
Journey to the center of the Earth and Mars: Seismology with big & small data and high-performance computing

Seismic waves generated by passive sources such as earthquakes and ambient noise are our primary tools to probe Earth's interior. Improving the resolution of the seismic models of deep Earth's interior is crucial to understand the dynamics of the mantle (from ~30 km to 2900 km depth) and the core (from 2900 km to 6371 km depth), which directly control, for instance, plate tectonics and volcanic activity at the surface, and the generation of Earth's magnetic field, respectively. Meanwhile, the detailed shallower crustal structure is essential for seismic hazard assessment, better modeling earthquakes and nuclear explosions, and oil and mineral explorations.

Advances in computational power and the availability of high-quality seismic data from dense seismic networks and emerging instruments offer excellent opportunities to refine our understanding of multi-scale Earth's structure and dynamics from surface to the core. We are at a stage where we need to take the full complexity of wave propagation into account and avoid commonly used approximations to the wave equation and corrections in seismic tomography. Imaging Earth's interior globally with full-waveform inversion has been one of the most extreme projects in seismology in terms of computational requirements and available data that can potentially be assimilated in seismic inversions. While we need to tackle computational and "big data" challenges to better harness the available resources on Earth, we have "small data" challenges on other planetary bodies such as Mars, where we now have the first radially symmetric models constrained by seismic waves generated by marsquakes as part of the Mars InSight mission. I will talk about advances in the theory, computations, and data in exploring multi-scale Earth's and Mars' interiors. I will also talk about our recent efforts to address computational and data challenges and discuss future directions in the context of global seismology.
February 18Eileen Martin, Colorado School of Mines
Moving less data in correlation- and convolution-based analyses

When analyzing the relationships between multiple streams of time-series data or between images, we often calculate crosscorrelations, convolutions or deconvolutions to explore potential time-lagged or space-lagged similarities between them. However, denser/larger sensor networks are leading to larger datasets, and naively calculating correlations or convolutions often requires significant data movement (quadratic, if naively looking at relationships between all data snapshots). This is particularly problematic in ambient noise interferometry, a method by which Green’s functions of a PDE system (such as the heat equation or a wave equation) are estimated by crosscorrelations across all sensors pairs in a dense sensor network recording randomly distributed sources of energy (heat sources or vibration sources). In this talk I will show some new algorithms to calculate array-wide correlations that take advantage of lossy data compression to reduce data movement and computational costs by performing crosscorrelations directly on compressed data. These methods can apply to crosscorrelation of any time-series data. Often, seismologists use the results of crosscorrelating ambient seismic noise as an input to a few types of array beamforming methods to characterize Earth materials (similar to beamforming used in wireless communications and astronomy). In fact, we can calculate the final beamforming results directly from the ambient seismic noise with new linear algorithms that only implicitly calculate crosscorrelations.
February 25Nancy Rodriguez, CU Boulder
March 4Graduate Student Colloquium

Title: Multiwavelets and Machine Learning-based Discontinuity and Edge Detection
Presenter: Soraya Terrab
Abstract: Spurious oscillations, such as Gibbs phenomenon, are artifacts that occur in numerical computation of PDEs that affect the accuracy of approximations and create non-physical effects. These oscillations need to be identified and eliminated in order to maintain physical relevance and accuracy in the numerical approximations. Identifying the nonphysical oscillations requires having reliable discontinuity detection methods. In this work, we take advantage of the theory behind multi-resolution wavelets analysis as well as machine learning to identify and limit troubled cells, or discontinuous cells, in the numerical approximation. By extracting the fine details through multi-resolution analysis in the multiwavelet approach, we can analyze the global information in the domain and apply theoretical thresholding and outlier detection to identify cells that are troubled. Additionally, we have trained classifiers on smooth and discontinuous data, enabling a machine learning solution to discontinuity detection.

The ideas from discontinuity detection are not limited to numerical solutions to PDEs; we can also apply these methods for the detection of edges in images. While typical edge detection methods include partial derivative operators, continuous wavelet or shearlet transforms, segmentation, or high-order and variable-order total variation, machine learning has only been recently explored as an edge detection tool for image processing [Wen et al. J. Sci. Comput. (2020)]. For this reason, we have been interested in using this imaging application to compare the multi-resolution wavelet and machine learning-based discontinuity detection methods in two-dimensional, static image data. In its simplest zero-degree multiwavelet construction, our discontinuity detection method results in a Haar wavelet-based detection of edges in images. We will present these initial results along with machine learning-based edge detection and will compare the two discontinuity detection approaches in computational cost and accuracy.


Title: Leveraging multiple continuous monitoring sensors for emission identification and localization on oil and gas facilities
Presenter: Will Daniels
Abstract: Methane, the primary component of natural gas, is a greenhouse gas with about 85 times the global warming potential of carbon dioxide over a 20-year timespan. This makes reducing methane emissions a vital tool for combatting climate change. Oil and gas facilities are a promising avenue for reducing emissions, as leaks from these facilities can be mitigated if addressed quickly. To better alert oil and gas operators to emission on their facilities, we developed a framework to identify when a methane emission is occurring and where it is coming from. This framework utilizes continuous monitoring sensors placed around the perimeter of the facility, but these sensors only observe ambient methane concentrations at their location and do not directly provide information about when and where an emission is occurring. Our framework turns these observations into a location estimate via the following steps. First, we identify spikes in the observations and perform local regression on non-spike data to estimate the methane background. Second, we simulate methane concentrations at the sensor locations from all potential leak sources separately. Third, we pattern match the simulated and observed concentrations, giving more weight to sources whose simulated concentrations more closely match observations. Finally, we synthesize this information across all sensors on the facility to provide a single location estimate with uncertainty. Here we discuss our framework in more detail and demonstrate its effectiveness under real-world conditions.
March 11Grad Student Colloquium

Title: The Radiative Transfer Equations: What they are, Why they are important, and How do we solve them?
Presenter: Alejandro Jaimes
Abstract: In this talk I will discuss the radiative transfer equations (RTE) in acoustic media. RTE describes the angular spatio-temporal distribution of energy density in scattering media, and has found applications in areas such as geophysics, acoustics, astrophysics, atmospheric sciences, and optics. RTE takes the form of integral partial differential equation which has motivated the development of numerical techniques such as discontinuous galerkin method and particle swarm optimization. I will first introduce the one-dimensional formulation of RTE and then generalize it to two and three dimensions. Through this generalization, I will discuss the complications that arise when dealing with 2 or 3-D scattering. I will then briefly discuss four standard approaches to solve RTE: spherical harmonics, discretization methods, iteration methods, and monte carlo techniques. I will show results of a numerical algorithm that I construct by mixing ideas of the iteration and discretization methods, and if time allows show some results of solving RTE through physics informed neural networks.
March 18Suzanne Sindi, UC-Merced
A Chemical Master Equation Model for Prion Aggregate Infectivity Shows Prion Strains Differ by Nucleus Size

Prion proteins are responsible for a variety of neurodegenerative diseases in mammals such as Creutzfeldt-Jakob disease in humans and “mad-cow” disease in cattle. While these diseases are fatal to mammals, a host of harmless phenotypes have been associated with prion proteins in S. cerevisiae, making yeast an ideal model organism for prion diseases. Most mathematical approaches to modeling prion dynamics have focused on either the protein dynamics in isolation, absent from a changing cellular environment, or modeling prion dynamics in a population of cells by considering the “average” behavior. However, such models have been unable to recapitulate in vivo properties of yeast prion strains including rates of appearance during seeding experiments.
The common assumption in prion phenotypes is that the only limiting event is the establishment of a stable prion aggregate of minimal size. We show this model is inconsistent with seeding experiments. We then develop a minimal model of prion phenotype appearance: the first successful amplification of an aggregate. Formally, we develop a chemical master equation of prion aggregate dynamics through conversion (polymerization) and fragmentation under the assumption of a minimal stable size. We frame amplification as a first-arrival time process that must occur on a time-scale consistent with the yeast cell cycle. This model, and subsequent experiments, then establish for the first time that two standard yeast prion strains have different minimally stable aggregate sizes. This suggests a novel approach (albeit entirely theoretical) for managing prion diseases, shifting prion strains towards larger nucleus sizes.
April 1Andee Kaplan, Colorado State University

Title: A Practical Approach to Proper Inference with Linked Data

Entity resolution (ER), comprising record linkage and de-duplication, is the process of merging noisy databases in the absence of unique identifiers to remove duplicate entities. One major challenge of analysis with linked data is identifying a representative record among determined matches to pass to an inferential or predictive task, referred to as the downstream task. Additionally, incorporating uncertainty from ER in the downstream task is critical to ensure proper inference. To bridge the gap between ER and the downstream task in an analysis pipeline, we propose five methods to choose a representative (or canonical ) record from linked data, referred to as canonicalization. Our methods are scalable in the number of records, appropriate in general data scenarios, and provide natural error propagation via a Bayesian canonicalization stage. In this talk, the proposed methodology is evaluated on three simulated data sets and one application — determining the relationship between demographic information and party affiliation in voter registration data from the North Carolina State Board of Elections. We first perform Bayesian ER and evaluate our proposed methods for canonicalization before considering the downstream tasks of linear and logistic regression. Bayesian canonicalization methods are empirically shown to improve downstream inference in both settings through prediction and coverage.
April 8Prof. Snigdhansu (Ansu) Chatterjee, Minnesota

Title: Nonparametric Hypothesis Testing in High Dimensions

High-dimensional data, where the dimension of the feature space is much larger than sample size, arise in a number of statistical applications. In this context, we present the generalized multivariate sign transformation, defined as a vector divided by its norm. For different choices of the norm function, the resulting transformed vector adapts to certain geometrical features of the data distribution. We obtain one-sample and two-sample testing procedures for mean vectors of high-dimensional data using these generalized sign vectors. These tests are based on U-statistics using kernel inner products, do not require prohibitive assumptions, and are amenable to a fast randomization-based implementation. Theoretical developments, simulated data and real data examples are discussed.
April 15Tammy Kolda, Mathematical Consultant

Title: Tensor Moments of Gaussian Mixture Models

Gaussian mixture models (GMMs) are fundamental tools in statistical and data sciences that are useful for clustering, anomaly detection, density estimation, etc. We are interested in high-dimensional problems (e.g., many features) and a potentially massive number of data points. One way to compute the parameters of a GMM is via the method of moments, which compares the sample and model moments. The first moment is the mean, the second (centered) moment is the covariance. We are interested in third, fourth, and even higher-order moments. The d-th moment of an n-dimensional random variable is a symmetric d-way tensor (multidimensional array) of size n x n x ... x n (d times), so working with moments is assumed to be prohibitively expensive in both storage and time for d>2 and larger values of n. In this talk, we show that the estimation of the model parameters can be accomplished without explicit formation of the model or sample moments. In fact, the cost per iteration for the method of moments is the same order as that of expectation maximization (EM), making method of moments competitive. Along the way, we show how to concisely describe the moments of Gaussians and GMMs using tools from algebraic geometry, enumerative combinatorics, and multilinear algebra. Numerical results validate and illustrate the numerical efficiency of our approaches.
April 22
April 29Ishani Roy, Serein

Title: Using Data to circumvent biases

Did you know that in 2013, the US Food and Drug Administration (FDA) recommended cutting the dose in half for women, but not men, after the results of driving simulation studies indicated women metabolise the drug at a slower rate. The FDA report came after 20 years of incorrect and dangerous prescribing of Ambien to women.

Action was taken only after more than 700 reports of motor vehicle crashes associated with Ambien use that put the lives of many women, their children and other drivers on the road at risk.

Biases not only affect recruitment, team morale and productivity it also affects how we design products and grow a business.

In this talk I will speak about how unconscious biases may affect inclusion and how data and research can be used to measure and monitor exclusion to circumvent biases.
Fall 2021
August 27Monique Chyba
Epidemiological modeling, and COVID-19 Heterogeneity in Islands Chain Environment

SARS-CoV-2 (COVID-19) has impacted not only health, but the economy and how we live daily life. On January 30, 2020 the World Health Organization (WHO) declared a global health emergency. COVID-19 was officially named on February 11, as it continued to spread across Asia and Europe. Mathematicians have found themselves at the front seat of this race against COVID-19. However, there is still a lot of unanswered questions and challenges regarding the outcome of several models as well as their limitations. It is unclear at this time if there is a "better" model, and while most of the challenges in epidemiological forecasting come from incomplete data and impossibility to model people's behavior, there is still the question of what model to use when and for what purpose. Throughout the current COVID-19 pandemic, most results and forecasting come from one model but not a combination. We consider what can be learned from running both compartment and agent based models side-by-side; taking and applying the best of each model using the measured data. We will also discuss the Hawaiian Islands are providing a unique opportunity to study heterogeneity and demographics in a controlled environment due to the geographically closed borders and mostly uniform pandemic-induced governmental controls and restrictions.
September 3Math and Social Justice: Sara Clifton, St. Olaf College
Modeling the leaky pipeline in hierarchical professions

Women constitute approximately 50% of the population and have been an active part of the U.S. workforce for over half a century. Yet women continue to be poorly represented in leadership positions within business, government, medical, and academic hierarchies. As of 2018, less than 5% of Fortune 500 chief executive officers are female, 20% of the U.S. Congress is female, and 34% of practicing physicians are female. The decreasing representation of women at increasing levels of power within hierarchical professions has been called the “leaky pipeline” effect, but the main cause of this phenomenon remains contentious. Using a mathematical model of gender dynamics within professional hierarchies and a new database of gender fractionation over time, we quantify the impact of the two major decision-makers in the ascension of people through hierarchies: those applying for promotion and those who grant promotion. We quantify the degree of homophily (self-seeking) and gender bias in a wide range of professional hierarchies and demonstrate that intervention may be required to reach gender parity in some fields. We also preview an in-progress effort to extend the model to quantify racial bias and homophily in professional hierarchies.

September 10Samy Wu Fung
Efficient Training of Infinite-Depth Neural Networks via Jacobian-Free Backpropagation

A promising trend in deep learning replaces fixed depth models by approximations of the limit as network depth approaches infinity. This approach uses a portion of network weights to prescribe behavior by defining a limit condition. This makes network depth implicit, varying based on the provided data and an error tolerance. Moreover, existing implicit models can be implemented and trained with fixed memory costs in exchange for additional computational costs. In particular, backpropagation through implicit depth models requires solving a Jacobian-based equation arising from the implicit function theorem. We propose a new Jacobian-free backpropagation (JFB) scheme that circumvents the need to solve Jacobian-based equations while maintaining fixed memory costs. This makes implicit depth models much cheaper to train and easy to implement. Numerical experiments on classification, CT reconstructions, and predicting traffic models are provided.
September 17AMS Graduate Student Colloquium
Dave Montgomery

Title: Parallelization of a Navier-Stokes solver for applications in extravascular injury modeling

Blood flow is governed by the incompressible Navier-Stokes equations, a set of non-linear equations that are regarded as computationally expensive to solve. Since the blood coagulation process happens over the time scale of tens of minutes, parallelization techniques are necessary to minimize overall computation time. We will present a method for decomposing the H-shaped extravascular injury domain so that the Navier-Stokes equations can be solved in parallel on multiple cores using distributed memory.
September 24Math and Social Justice: Emma Pierson (Microsoft Research) on "Data science for social equality"
October 1AMS Graduate Student Colloquium
Laura Albrecht

Title: A spatio-temporal model to estimate West Nile Virus cases in Ontario

Abstract: West Nile virus is the most common mosquito borne disease in North America and the leading cause of viral encephalitis. West Nile virus is primarily transmitted between birds and mosquitoes while humans are incidental, dead-end hosts. We develop a Poisson spatio-temporal model to investigate how human West Nile virus case counts vary with respect to mosquito abundance and infection rates, bird abundance, and other environmental covariates. We use a Bayesian paradigm to fit our model to data from 2010-2019 in Ontario, Canada.
October 8Research Open House

Doug Nychka: Deep learning a statistical model
Eileen Martin: Green’s function estimation with non-ideal noise
Samy Wu Fung: Solving High-Dimensional Optimal Control Problems with Deep Learning
Paul Martin: Generation of internal waves in the ocean
October 15AMS Graduate Student Colloquium
October 22Beth Malmskog, Colorado College

Colorado in Context: Using Mathematics to Detect and Prevent Gerrymandering in Colorado and Beyond

Gerrymandering is the process of manipulating the boundaries of electoral districts for political gain. This is considered by many to be deeply unfair, but it has been common practice in states across the country for more than 200 years. This talk will introduce a mathematical/statistical technique called ensemble analysis in the context of electoral boundaries, and describe how this perspective has become central to the national conversation about fair redistricting. I will share the big picture ideas, recent progress, and the work that our group is doing here in Colorado.
October 29Math and Social Justice: Veronica Ciocanel (Duke University) on "Analyzing Racial Equity and Bias of Federal Judges through Inferred Sentencing Records"
November 5Hannah Director, Mines

Title: Identification and Uncertainty of Sea Ice Leads

Abstract: Sea ice has substantial effects on the climate of Polar regions and the Earth overall. For example, open ocean tends to absorb heat from solar radiation while ice covered surfaces tend to reflect radiation. Large, narrow cracks in the ice’s surface, called leads, affect how sea ice grows and melts. Information about when and where leads form is needed to understand sea ice behavior and feedbacks between the ocean and atmosphere. To develop this understanding, scientists need an efficient way to identify sea ice leads in observational data and climate model output. For sea ice, remote sensing data and climate model output provide gridded fields showing the proportion of area in each grid box that is ice-covered. This granular identification, however, does not directly identify leads as distinct and coherent features. We introduce a likelihood-based method to efficiently identify sea ice leads from data of this form. Our method also provides uncertainty estimates of the presence and location of leads. We apply this identification method to high-resolution model output to assess the frequency of lead formation, structure of typical leads, and environmental conditions when leads form.
November 12Derek Onken, Eli Lilly

A Neural Network Approach for Real-Time High-Dimensional Optimal Control

Optimal control (OC) problems aim to find an optimal policy that control given dynamics over a period of time. For systems with high-dimensional state (for example, systems with many centrally controlled agents), OC problems can be difficult to solve globally. We propose a neural network approach for solving such problems. When trained offline in a semi-global manner, the model is robust to shocks or disturbances that may occur in real-time deployment (e.g., wind interference). Our unsupervised approach is grid-free and scales efficiently to dimensions where grids become impractical or infeasible. We demonstrate the effectiveness of our approach on several multi-agent collision-avoidance problems in up to 150 dimensions.
November 19Math and Social Justice: Jonathan Mattingly (Duke University) on "Fairness in Redistricting"

The US political system is built on representatives chosen by geographically localized regions. This presents the government with the problem of designing these districts. Every ten years, the US census counts the population and new political districts must be drawn. The practice of harnessing this administrative process for partisan political gain is often referred to as gerrymandering. How does one identify and understand gerrymandering? Can we really recognize gerrymandering when we see it? If one party wins over 50% of the vote, is it fair that it wins less than 50% of the seats? What do we mean by fair? How can math help illuminate these questions? How does the geopolitical geometry of the state (where which groups live and the shape of the state) inform these answers? For me, these questions began with an undergraduate research program project in 2013 and has led me to testify twice in two cases: Common Cause v. Rucho (that went to the US Supreme Court) and Common Cause v. Lewis. This work has partially resulted in the redrawing of the NC State Legislative district maps and NC congressional maps. The resulting new maps will be used in our upcoming 2020 elections. In the remedy phase of North Carolina v. Covington, Greg Herschlog from the Duke group addresses the question if attempts to satisfy the VRA alone explained the observed level political packing and cracking. This is a story of interaction between lawyer, mathematicians, and policy advocates. The legal discussion has been increasingly informed by the mathematical framework. And the mathematics has been pushed to better include to the policy. The back and forth has been important to find ways to effectively inform the policy makers and courts to the insite the analyses provide. The problem of understanding gerrymandering has also prompted the development of a number of new computational algorithms which come with new mathematical questions. The next round of redistricting analysis will necessarily need to be more refined and nuanced. There is also the opportunity to be less reactive. There are opportunities to try to influence the process by which new maps are drawn before turning to the courts. There is also the possibility to direct the conversation by showing the effect more fully considering factors such as communities of interest, incumbency or proposed procedural elements of laws. This presentation reflects joint work Gregory Herschlag and a number of other researchers including many undergraduates, graduate students, and a few high school students.
December 3Zachary Kilpatrick

How heterogeneity shapes the efficiency of collective decisions and foraging

Many organisms regularly make decisions regarding foraging, home-site selection, mating, and danger avoidance in groups ranging from hundreds up to millions of individuals. These decisions involve evidence-accumulation processes by individuals and information exchange within the group. Moreover, these decisions take place in complex, dynamic, and spatially structured environments, which shape the flow of information between group mates. We will present a statistical inference model for framing evidence accumulation and belief sharing in groups and some examples of how interactions shape decision efficiency in groups. Our canonical model is of Bayesian agents deciding between two equally likely options by accumulating evidence to a threshold. When neighbors only share their decisions with each other, groups comprised of individuals with a distribution of decision thresholds make more efficient decisions than homogeneous ones. We then turn our attention to specific examples of collective decision making in foraging animal groups like honey bees. For honey bees, spatial heterogeneity resulting from confinement to a hive bottlenecks communication, but creates an effective colony-level signal-detection mechanism whereby recruitment to low quality objectives is blocked. Heterogeneity in communication, on the other hand, hobbles the foraging efficiency of small groups.

Spring 2021
January 29Book Club
"Factfulness: 10 Reasons We’re Wrong about the World – and Why Things are Getting Better" (2018), Hans Rosling
Chapters 1-3
February 5Michelle McCarthy
Boston University

Title: Mathematical modeling of neuronal rhythms: from physiology to function

Abstract: Brain rhythms are a ubiquitous feature of brain dynamics, tightly correlated with neuronal activity underlying such basic functions as cognition, emotions, movement, and sleep. Moreover, abnormal rhythmic activity is associated with brain disfunction and altered brain states. Identifying the neuronal units and network structures that create, sustain and modulate brain rhythms is fundamental to identifying both their function and dysfunction in mediating behavioral output. Experimental studies of brain rhythms are limited by the inability to isolate large ensembles of neurons and their interconnections during active brain states. However, mathematical models have been used extensively to study network dynamics of the brain and to give insight into the determinants and functions of brain oscillations during various cognitive and behavioral states. Here I will give a brief introduction to the field of study of rhythmic brain activity and the mathematical formulations underlying biophysical neuronal network models. Existing mathematical models of brain development, sleep and neurodegenerative disease will be used to demonstrate how neuronal models of rhythmic dynamics can be used to explore the link between the brain physiology and functional network dynamics.
February 12Daniel Nordman
Iowa State

Title: Within-sample prediction of a number of future events

Abstract: The talk overviews a prediction problem encountered in reliability engineering, where a need arises to predict the number of future events (e.g., failures) among a cohort of units associated with a time-to-event process. Examples include the prediction of warranty returns or the prediction of the number of future product failures that could cause serious harm. Important decisions, such as a product recall, are often based on such predictions. Data, typically right-censored, are used to estimate the parameters of a time-to-event distribution. This distribution can then be used to predict the number of events over future periods of time. Because all units belong to the same data set, either by providing information (i.e., observed event times) or by becoming the subject of prediction (i.e., censored event times), such predictions are called within-sample predictions and differ from other prediction problems considered in most literature. A standard plug-in (also known as estimative) prediction approach is shown to be invalid for this problem (i.e., for even large amounts of data, the method fails to have correct coverage probability). However, a commonly used prediction calibration method is shown to be asymptotically correct for within-sample predictions, and two alternative predictive-distribution-based methods are presented that perform better than the calibration method.
February 19
Special Time of 1:00 PM
Olivia Prosper
University of Tennessee

Title: Modeling malaria parasite dynamics within the mosquito

Abstract: The malaria parasite Plasmodium falciparum requires a vertebrate host and a female Anopheles mosquito to complete a full life cycle, with sexual reproduction occurring in the mosquito. While parasite dynamics within the vertebrate host, such as humans, has been extensively studied, less is understood about dynamics within the mosquito, a critical component of malaria transmission dynamics. This sexual stage of the parasite life cycle allows for the production of genetically novel parasites. In the meantime, a mosquito’s biology creates bottlenecks in the infecting parasites’ development. We developed a two-stage stochastic model of the generation of parasite diversity within a mosquito and were able to demonstrate the importance of heterogeneity amongst parasite dynamics across a population of mosquitoes on estimates of parasite diversity. A key epidemiological parameter related to the timing of onward transmission from mosquito to vertebrate host is the extrinsic incubation period (EIP). Using simple models of within-mosquito parasite dynamics fitted to empirical data, we investigated factors influencing the EIP.
February 26Book Club
"Factfulness: 10 Reasons We’re Wrong about the World – and Why Things are Getting Better" (2018), Hans Rosling
Chapters 4-6
March 12Lise-Marie Imbert-Gerard
University of Arizona

Title: Wave propagation in inhomogeneous media: An introduction to Generalized Plane Waves

Abstract: Trefftz methods rely, in broad terms, on the idea of approximating solutions to Partial Differential Equation (PDEs) using basis functions which are exact solutions of the PDE, making explicit use of information about the ambient medium. But wave propagation problems in inhomogeneous media is modeled by PDEs with variable coefficients, and in general no exact solutions are available. Generalized Plane Waves (GPWs) are functions that have been introduced, in the case of the Helmholtz equation with variable coefficients, to address this problem: they are not exact solutions to the PDE but are instead constructed locally as high order approximate solutions. We will discuss the origin, the construction, and the properties of GPWs. The construction process introduces a consistency error, requiring a specific analysis.
March 19Ethan Anderes

Title: Gravitational wave and lensing inference from the CMB polarization

Abstract: In the last decade cosmologists have spent a considerable amount of effort mapping the radially-projected large-scale mass distribution in the universe by measuring the distortion it imprints on the CMB. Indeed, all the major surveys of the CMB produce estimated maps of the projected gravitational potential generated by mass density fluctuations over the sky. These maps contain a wealth of cosmological information and, as such, are an important data product of CMB experiments. However, the most profound impact from CMB lensing studies may not come from measuring the lensing effect, per se, but rather from our ability to remove it, a process called delensing. This is due to the fact that lensing, along with emission of millimeter wavelength radiation from the interstellar medium in our own galaxy, are the two dominant sources of foreground contaminants for primordial gravitational wave signals in the CMB polarization. As such delensing, i.e. the process of removing the lensing contaminants, and our ability to either model or remove galactic foreground emission sets the noise floor on upcoming gravitational wave science.

In this talk we will present a complete Bayesian solution for simultaneous inference of lensing, delensing and gravitational wave signals in the CMB polarization as characterized by the tensor-to-scalar ratio r parameter. Our solution relies crucially on a physically motivated re-parameterization of the CMB polarization which is designed specifically, along with the design of the Gibbs Markov chain itself, to result in an efficient Gibbs sampler---in terms of mixing time and the computational cost of each step---of the Bayesian posterior. This re-parameterization also takes advantage of a newly developed lensing algorithm, which we term LenseFlow, that lenses a map by solving a system of ordinary differential equations. This description has conceptual advantages, such as allowing us to give a simple non-perturbative proof that the lensing determinant is equal to unity in the weak-lensing regime. The algorithm itself maintains this property even on pixelized maps, which is crucial for our purposes and unique to LenseFlow as compared to other lensing algorithms we have tested. It also has other useful properties such as that it can be trivially inverted (i.e. delensing) for the same computational cost as the forward operation, and can be used for fast and exact likelihood gradients with respect to the lensing potential. Incidentally, the ODEs for calculating these derivatives are exactly analogous to the backpropagation techniques used in deep neural networks but are derived in this case completely from ODE theory.
March 26Book Club
"Factfulness: 10 Reasons We’re Wrong about the World – and Why Things are Getting Better" (2018), Hans Rosling
Chapters 7-9
April 9Andrew Zammit Mangion
University of Wollongong

Title: Statistical Machine Learning for Spatio-Temporal Forecasting

Abstract: Conventional spatio-temporal statistical models are well-suited for modelling and forecasting using data collected over short time horizons. However, they are generally time-consuming to fit, and often do not realistically encapsulate temporally-varying dynamics. Here, we tackle these two issues by using a deep convolution neural network (CNN) in a hierarchical statistical framework, where the CNN is designed to extract process dynamics from the process' most recent behaviour. Once the CNN is fitted, probabilistic forecasting can be done extremely quickly online using an ensemble Kalman filter with no requirement for repeated parameter estimation. We conduct an experiment where we train the model using 13 years of daily sea-surface temperature data in the North Atlantic Ocean. Forecasts are seen to be accurate and calibrated. We show the versatility of the approach by successfully producing 10-minute nowcasts of weather radar reflectivities in Sydney using the same model that was trained on daily sea-surface temperature data in the North Atlantic Ocean. This is joint work with Christopher Wikle, University of Missouri.
April 16Diogo Bolster
University of Notre Dame

Title: Incomplete mixing in reactive systems - from Lab to Field scale

Abstract: In order for two items to react they must physically come into contact with one another. In the lab we often measure reaction rates by forcing two species to continuously mix together. However, in real systems such forced mixing mechanisms may often not exist and so a natural question arises: How do we take measurements from our well mixed laboratory experiments and use them to make meaningful predictions at scales of interest? In this talk we propose a novel modeling framework that aims precisely to do this. To show its applicability we will discuss it as related to a few examples: (i) mixing driven reactions in a quasi-well-mixed systems (ii) mixing driven reactions in a porous column experiment and (iii) mixing in a highly heterogeneous aquifer with a broad range of velocity and spatial scales.

While this work was originally motivated by chemical reactions in porous media, the modeling framework is much more general than this and should be applicable to a broad range of problems. Also, the term reaction, as defined within our framework, can loosely be defined as an event where two items come together to produce something else; it is not in any way limited to purely chemical reactions.
April 23Kiona Ogle
Northern Arizon

Title: A Bayesian approach to quantifying time-scales of influence and ecological memory

Abstract: Many time-varying ecological processes are influenced by both concurrent and antecedent (past) conditions; in some cases, antecedent conditions may outweigh concurrent influences. The time-scales over which environmental conditions influence processes of interest (e.g., photosynthesis, carbon and water fluxes, tree growth, ecosystem productivity) are not well understood, motivating our development and application of the stochastic antecedent modeling (SAM) approach. The SAM approach is applied to ecological time-series data within a Bayesian statistical framework to quantify ecological memory. We use “memory” to broadly describe time-scales of influence, including the importance of antecedent conditions experienced at different times into the past, potentially revealing lagged responses. The coupled Bayesian-SAM approach, however, can lead to computational inefficiencies, and we describe reparameterization “solutions” to address such issues. To illustrate, we apply the approach to responses operating at distinctly different time-scales: annual tree growth (e.g., tree-rings widths) and sub-daily plant physiological responses (e.g., indices of stomatal behavior). Our Bayesian-SAM applications to tree growth in arid and semi-arid regions has identified particular seasons or months during which climatic conditions (e.g., precipitation or temperature) are most influential to subsequent tree growth; in many cases, conditions experienced 2-4 years ago continue to influence growth. The analysis has also revealed novel, multi-day lagged responses of plant physiological behavior to soil and atmospheric moisture conditions. In general, the Bayesian-SAM approach has demonstrated that ecological memory is an important process governing plant and ecosystem responses to environmental perturbations.
April 30Book Club
"Factfulness: 10 Reasons We’re Wrong about the World – and Why Things are Getting Better" (2018), Hans Rosling
Chapter 9-10 + Factfulness Rules of Thumb
Fall 2020
September 18Zachary J. Grant
Oak Ridge National Lab

Analysis and Development of Strong Stability Preserving Time Stepping Schemes

High order spatial discretizations with monotonicity properties are often desirable for the solution of hyperbolic partial differential equations. These methods can advantageously be coupled with high order strong stability preserving time scheme to accurately evolve solutions forward in time while preserving convex functionals that are satisfied from the design of the spatial discretization. The search for high order strong stability time- stepping methods with large allowable strong stability coefficient has been an active area of research over the last three decades. In this talk I will review the foundations of SSP time stepping schemes as in how to analyze a given scheme, and how to optimally build a method which allows the largest effective stable time step. We will then discuss some extensions of the SSP methods in recent years and some ongoing research problems in the field, and show some the need of the SSP property through simple yet demonstrative examples.
September 25Book Club
“Weapons of Math Destruction”
Chapter 1-4
October 2
October 16Minah Oh
James Madison University

Fourier Finite Element Methods and Multigrid for Axisymmetric H(div) Problems

An axisymmetric problem is a problem defined on a three-dimensional (3D) axisymmetric domain, and it appears in numerous applications. An axisymmetric problem can be reduced to a sequence of two-dimensional (2D) problems by using cylindrical coordinates and a Fourier series decomposition. Fourier Finite Element Methods (Fourier-FEMs) can be used to approximate each Fourier-mode of the solution by using a suitable FEM. Such dimension reduction is an attractive feature considering computation time, but the resulting 2D problems are posed in weighted function spaces where the weight function is the radial component r. Furthermore, the grad, curl, and div operators appearing in these weighted problems are quite different from the standard ones, so the analysis of such weighted problems requires special attention.

Multigrid is an effective iterative method that can be used to solve large matrix systems arising from FEMs. In this talk, I will present a multigrid algorithm that can be applied to weighted H(div) problems that arise after performing a dimension reduction to an axisymmetric H(div) problem. Theoretical results that show the uniform convergence of the multigrid V-cycle with respect to meshsize will be presented as well as numerical results.
October 30Book Club:
“Weapons of Math Destruction”
Chapters 5 - 7
November 6Mokshay Madiman
University of Delaware

Concentration of information for log-concave distributions

In 2011, S. Bobkov and the speaker showed that for a random vector X in R^n drawn from a log-concave density f=e^{-V}, the information content per coordinate, namely V(X)/n, is highly concentrated about its mean. The result demonstrated that high-dimensional log-concave measures are in a sense close to uniform distributions on the annulus between 2 nested convex sets (generalizing the well known fact that the standard Gaussian measure is concentrated on a thin spherical annulus). We present recent work that obtains an optimal concentration bound in this setting, using a much simplified proof. Applications that motivated the development of these results include high-dimensional convex geometry, random matrix theory, and shape-constrained density estimation.

The talk is based on joint works with Sergey Bobkov (University of Minnesota), Matthieu Fradelizi (Université Paris Est), and Liyao Wang.
November 20Ayaboe Edoh
Edwards AFRL

Balancing Numerical Dispersion, Dissipation, and Aliasing for Time-Accurate Simulations

The investigation of unsteady flow phenomena calls for the need to improve time-accurate simulation capabilities. Numerical errors responsible for affecting solution accuracy and robustness can be broadly categorized in terms of dispersion, dissipation, and aliasing. Their presence is a consequence of discretizing the continuous governing equations, and their impact may be felt at all scales (albeit to varying degrees). The task of constructing an effective numerical method may therefore be interpreted in terms of reducing the influence of these errors over as broad a range of scales as possible. Here, a concerted assembly of scheme components is chosen relative to a target aliasing limit. High-order and optimized finite difference stencils are employed in order to achieve accuracy; meanwhile, split representations for nonlinear transport terms are used in order to greatly improve robustness. Finally, tunable and scale-discriminant artificial-dissipation methods are incorporated for de-aliasing purposes and as a means of further enhancing both accuracy and stability. The proposed framework is motivated by the need to devise a numerical format capable of mitigating discretization effects in Large-Eddy Simulations.

December 4Book Club
“Weapons of Math Destruction”
Chapters 8-10
Spring 2020
January 24Mevin Hooten
Colorado State University
Runnning on empty: Recharge dynamics from animal movement data
February 14Mark Risser
Lawrence Berkeley National Laboratory
Bayesian inference for high-dimensional nonstationary Gaussian processes
February 21Donna Calhoun
Boise State University
A fully unsplit wave propagation algorithm for shallow water flows on GPUs
February 28Matthias Katzfuss
Texas A&M
Gaussian-Process Approximations for Big Data
March 20Nancy Rodriguez
April 3Dan Nordman
April 10Grady Wright
April 24 Feng Bao
Fall 2019
August 23Chris Elvidge
NOAA and Mines' Payne Institute of Public Policy
VIIRS Data Gems From the Nights
September 13Cynthia Phillips
Sandia National Laboratory
Advanced Data Structures for National Cyber Security
September 20Will Kleiber
University of Colorado - Boulder
Mixed Graphical-Basis Models for Large Nonstationary and Multivariate Spatial Data Problems
October 4Igor Cialenco
Illinois Institute of Technology
Adaptive Robust Control Under Model Uncertainty
October 18Tathagata Bandyopadhyay
Indian Institute of Management Ahmedabad
Inference Problems in Binary Regression Model with Misclassified Responses
October 25Daniel Forger
University of Michigan
Math, Music and the Mind; Analysis of the performed Trio Sonatas of J.S. Bach
November 8Daniel Larremore
University of Colorado - Boulder
Complex Networks & Malaria: From Evolution to Epidemiology
November 22Marisa Eisenberg
University of Michigan
December 3Russell Cummings
United States Air Force Academy
The DoD High Performance Computing Modernization Program’s Hypersonic Vehicle Simulation Institute: Objectives and Progress
-A Mechanical Engineering Seminar-
Spring 2019
January 25Steve Sain
Jupiter Intelligence
Data Science @ Jupiter
February 1Xingping Sun
Missouri State University
Kernel Based Monte Carlo Approximation Methods
February 8Mandy Hering
Baylor University
Fault Detection and Attribution for a Complex Decentralized Wastewater Treatment Facility
February 22Bailey K. Fosdick
Colorado State University
Inference for Network Regressions with Exchangeable Errors
March 8Radu Cascaval
University of Colorado - Colorado Springs
The Mathematics of (Spatial) Mobility
March 15Amneet Bhalla
San Diego State University
A Robust and Efficient Wave-Structure Interaction Solver for High Density Ratio Multiphase Flows
March 22Robert Lund
Clemson University
Stationary Count Time Series
April 5Hua Wang
Colorado School of Mines
Learning Sparsity-Induced Models for Understanding Imaging Genetics Data
April 26Wen Zhou
Colorado State University
Estimation and Inference of Heteroskedasticity Models with Latent Semiparametric Factors for Multivariate Time Series
May 3Olivier Pinaud
Colorado State University
Time Reversal by Time-dependent Perturbations
Fall 2018
August 31Michael Wakin
Colorado School of Mines
Modal Analysis from Random and Compressed Samples
September 14Michael Scheuerer
National Oceanic and Atmospheric Administration (NOAA)
Generating Calibrated Ensembles of Physically Realistic, High-Resolution Precipitation Forecast Fields based on GEFS Model Output
September 28Kathryn Colborn
CU Denver, Anschutz Medical Campus
Spatio-Temporal Modelling of Malaria Incidence for Early Epidemic Detection in Mozambique
October 12Philippe Naveau
Laboratoire des Sciences du Climat et de l'Environnement, IPSL-CNRS, France
Analysis of Extreme Climate Events by Combining Multivariate Extreme Values Theory and Causality Theory
October 26Carrie Manore
Los Alamos National Laboratory
Modeling Disease Risk with Social and Environmental Drivers and Non-traditional Data Sources
November 2Jon Trevelyan
Durham University, UK
Enriched Simulations in Computational Mechanics
November 9Sarah Olson
Worcester Polytechnic Institute
Modeling Cell Motility: From Agent Based Models to Continuous Approximations
November 30Elwin van't Wout
Pontificia Universidad Católica de Chile
Efficient Numerical Simulations of Wave Propagation Phenomena
December 7Bruce Bugbee
National Renewable Energy Laboratory (NREL)
Spring 2018
March 2Grant Brown
University of Iowa Biostatistics
Working with Approximate Bayesian Computation in Stochastic Compartmental Models
March 9Victoria Booth
University of Michigan Mathematics
Neuromodulation of Neural Network Dynamics
March 23Daniel Appelö
University of Colorado Applied Math
What’s New with the Wave Equation?
April 6Grad Student Showcase
April 20Jem Corcoran
University of Colorado Applied Math
A Birth-and-Death Process for the Discretization of Continuous Attributes in Bayesian Network Structure Recovery
May 4Ian Sloan
University of New South Wales Mathematics
Sparse Approximation and the Cosmic Microwave Background
Fall 2017
August 25Zachary Kilpatrick
University of Colorado Boulder, Department of Applied Mathematics
Evidence accumulation in changing environments: Neurons, organisms, and groups
September 8Lincoln Carr
Colorado School of Mines, Department of Physics
Many-Body Quantum Chaos of Ultracold Atoms in a Quantum Ratchet
September 22Joe Guinness
North Carolina State University, Department of Statistics
A General Framework for Vecchia Approximations of Gaussian Processes
October 13Eliot Fried
Okinawa Institute of Science and Technology, Mathematics, Mechanics, and Materials Unit
Shape Selection Induced by Competition Between Surface and Line Energy
October 20Arthur Sherman
National Institutes of Health
Diabetes Pathogenesis as a Threshold-Crossing Process
November 3Adrianna Gillman
Rice University, Department of Computational and Applied Mathematics
Fast Direct Solvers for Boundary Integral Equations
November 17Laura Miller
University of North Carolina at Chapel Hill, Departments of Mathematics and Biology
Using Computational Fluid Dynamics to Understand the Neuromechanics of Jellyfish Swimming
December 1AMS Graduate Student Showcase
Spring 2017
January 13Roger Ghanem
University of Southern California, Department of Aerospace and Mechanical Engineering
Uncertainty quantification at the interface of computing and everything else
Special joint colloquium with Department of Mechanical Engineering
January 27Wolfgang Bangerth
Colorado State University, Department of Mathematics
Simulating complex flows in the Earth mantle
February 10Chris Mast
Mercer, Actuary and Employee Benefits Consultant
Actuarial problems in employer-sponsored healthcare
February 24Natasha Flyer
National Center for Atmospheric Research, Computational Math Group
Bengt Fornberg
University of Colorado Boulder, Department of Applied Mathematics
Radial basis functions: Freedom from meshes in scientific computing
March 10Michael Sprague
National Renewable Energy Laboratory, Computational Science Center
A computational model for a dilute biomass suspension undergoing mixing and settling
March 24Randall J. LeVeque
University of Washington, Department of Applied Mathematics
Generating random earthquakes for probabilistic hazard assessment
Special joint colloquium with US Geological Survey
April 7Fred J. Hickernell
Illinois Institute of Technology, Department of Applied Mathematics
Think like an applied mathematician and a statistician
April 14Ian Sloan
University of New South Wales, School of Mathematics
How high is high dimensional?
April 21Mark Embree
Virginia Tech, Department of Mathematics
Using interpolatory approximations to learn from an instrumented building
April 28James A. Warren
National Institute of Standards and Technology, Material Measurement Laboratory
The Materials Genome Initiative: NIST, data, and open science
Special joint colloquium with Department of Metallurgical and Materials Engineering
May 5Jessica F. Ellis
Colorado State University, Department of Mathematics
The features of college calculus programs: An overview of the MAA two calculus projects' main findings
Fall 2016
September 2Stephen Becker
University of Colorado Boulder, Department of Applied Mathematics
Subsampling large datasets via random mixing
September 16Art Owen
Stanford University, Department of Statistics
Permutation p-value approximation via generalized Stolarsky invariance
September 30Stefan Wild
Argonne National Laboratory, Mathematics and Computer Science Division
Beyond the black box in derivative-free and simulation-based optimization
October 14Erica Graham
Bryn Mawr College, Department of Mathematics
Modeling physiological and pathological mechanisms in ovulation
October 28Jim Koehler
Google Boulder, Principal Statistician
Statistical methods supporting Google's ad business
November 11Dennis Cook
University of Minnesota, School of Statistics
An Introduction to envelopes: Methods for improving efficiency in multivariate statistics
December 2Howard Elman
University of Maryland, Department of Computer Science
Efficient computational methods for parameterized partial differential equations