Fall 2021

Colloquium talks will take place on Fridays, 1:00pm - 2:00pm. Talks will be held in a hybrid in-person / virtual format. The in-person portion will be held in the Katharine Reed Cudahy Building, Room 401 on the Marquette University campus. Please address inquiries/suggestions to Dr. Ongie at gregory.ongie@marquette.edu. Those affiliated with Marquette University can join virtually by joining the "MSSC Department Colloquium" Team on Microsoft Teams.

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September 10 - Shane Hesprich (MU Research Computing Services)
High Performance Computing at Marquette

High Performance Computing (HPC) is a powerful tool used in almost every field from economics and engineering, to healthcare and business information. Raj, Marquette's freely available, centralized HPC resource, provides students and faculty the ability to utilize HPC for research and academic purposes. Understanding how to access and properly utilize this resource can be of great benefit to your career here at Marquette.

October 15 - Computational Sciences Summer Research Fellowship Presentation
Chase Sakitis (Marquette University)
A Formal Bayesian Approach to SENSE Image Reconstruction

In fMRI, capturing cognitive temporal dynamics is dependent upon the rate at which volume brain images are acquired. The sampling time for an array of spatial frequencies to reconstruct an image is the limiting factor. Multi-coil SENSE image reconstruction is a parallel imaging technique that has greatly reduced image scan time. In SENSE image reconstruction, complex-valued coil sensitivities are estimated once from a priori calibration images and used to form a “known” design matrix to reconstruct every image. However, the SENSE technique is highly inaccurate when the sensitivity design matrix is not positive definite. Here, we propose a formal Bayesian approach where prior distributions for the unaliased images, coil sensitivities, and variances/covariances are assessed from the a priori calibration image information. Images, coil sensitivities, and variances/covariances are estimated a posteriori jointly via the Iterated Conditional Modes maximization algorithm and marginally via MCMC using the Gibbs sampling algorithm. Since the posterior marginal distributions are available, hypothesis testing is possible. This Bayesian SENSE (BSENSE) model to reconstruct images is applied to realistically simulated fMRI data. This BSENSE model accurately reconstructs a single slice image as well as a series of slice images without aliasing artifacts and was used to produce magnitude-only task activation.

October 20 (Wednesday)Danny Smyl (University of Sheffield)
Some recent advances in inverse problems applied to NDE and SHM

The field of inverse problems, the mathematics of estimating and understanding causalities from effects (data), has taken massive strides in the past 20 years. Since the advent of high performance, probabilistic, and learned computation, inversion-based applications in nondestructive evaluation (NDE) and structural health monitoring (SHM) have become increasingly pervasive. In this seminar, we highlight some key contemporary advances in inverse problems applied to NDE and SHM. In this effort, we evidence recent developments in learned (direct) inversion, multi-state reconstruction, sensor optimization, highly dynamical spatial loading prediction, and finite element model error prediction/compensation.

October 29 - John Lipor (Portland State University)
Title TBD

Abstract TBD

November 5 - Jessi Cisewski-Kehe (UW Madison) - tentative
Title TBD

Abstract TBD

November 12 - Luke Mcguire (University of Arizona)
Title TBD

Abstract TBD

November 19 - Robert Krafty (Emory University)
Interpretable PCA for Multilevel Multivariate Functional Data

Many studies collect functional data from multiple subjects that have both multilevel and multivariate structures.

An example of such data comes from popular neuroscience experiments where participants' brain activity is recorded using modalities such as EEG  and summarized as power within multiple time-varying frequency bands within multiple electrodes, or brain regions. Summarizing the joint variation across multiple frequency bands for both whole-brain variability between subjects, as well as location-variation within subjects, can help to explain neural reactions to stimuli. This article introduces a novel approach to conducting interpretable principal components analysis on  multilevel multivariate functional data that decomposes total variation into  subject-level and replicate-within-subject-level (i.e. electrode-level) variation, and provides interpretable components that can be both sparse among variates (e.g. frequency bands) and have localized support over time within each frequency band. Smoothness is achieved through a roughness penalty, while sparsity and localization of components are achieved by solving an innovative rank-one based convex optimization problem with block Frobenius and matrix L1-norm based penalties. The method is used to analyze data from a study to better understand reactions to emotional information in individuals with histories of trauma and the symptom of dissociation, revealing new neurophysiological insights into how subject- and electrode-level brain activity are associated with these phenomena.

December 3 - Computational Sciences Summer Research Fellowship Presentation
Md. Fitrat Hossain (Marquette University)
Personalized mHealth Monitoring System for Veterans

Abstract TBD

December 10 - Computational Sciences Summer Research Fellowship Presentations
Jesse Adikorley (Marquette University)
Multivariate Functional Time Series Forecasting : Multivariate Functional Singular Spectrum Analysis approaches applied to "images and curves/remote sensing”

Sunil Mathew (Marquette University)
Model interpretability in terms of dropout in Neural Networks using Bayesian learning

Abstracts TBD

Previous Semesters

Spring 2021

Colloquium talks for Spring 2021 will be held virtually via Microsoft Teams. Please address inquiries/suggestions to Dr. Ongie at gregory.ongie@marquette.edu

May 7th (12pm CT) - Prof. Kaitlyn Muller, Department of Mathematics and Statistics, Villanova University

"Clutter Mitigation Techniques in Synthetic-Aperture Radar Imaging"

Abstract: In this talk we will discuss two different methods of addressing the problem of clutter in synthetic-aperture radar (SAR) imaging. SAR images are often used for the purpose of target detection and classification. Therefore it is necessary to produce images that display the target/object of interest clearly and in such a way that they are distinguishable from other objects present in the scene.  These other objects are referred to as clutter and they often scatter just as strongly as targets and can obscure the presence of targets in images. We will begin with the basics of radar/SAR imaging and discuss a common model for volume scattering clutter (i.e. foliage). We will then present two techniques to mitigate the presence of clutter in the images. First we will discuss the more common filtering techniques which require knowledge of the clutter statistics. Second we will discuss correlation imaging, a second order imaging technique, that in certain cases can provide clutter mitigation without a priori knowledge.

April 30th (3:30pm CT) - Prof. Kimia Ghobadi, Department of Civil and Systems Engineering, Johns Hopkins University

"Hospital Resource Optimization for COVID-19"

Abstract: The COVID-19 pandemic has created a significant strain on the healthcare systems since its start. As hospitals cope with the unknown demand and surges in the cases, critical resources like ICU beds have become scarce. Additional beds and field hospitals are considered to meet the increased demand, but simply expanding the capacity is not viable for all hospitals. Better utilization of the currently available capacity can improve access to resources, lower the burden to hospitals and staff, and lead to better patient care. To this end, we developed mathematical models that match the demand with available resources in a regional system of hospitals. Our robust mixed-integer linear models minimize the resource shortage while considering operational constraints and desirable allocation properties such as transfer sparsity, consistency, and locality. Our models can consider primary resources (e.g., beds) in addition to complementary resources (e.g., nurses). We have tested and validated our models on the first wave of the COVID-19 pandemic and the subsequent surges and are currently in use at the Johns Hopkins Health System hospitals. We expanded our models to all hospitals in the US and developed an interactive public website (https://covid-hospital-operations.com/) to help decision-makers on various levels to plan and use their bed resources.

April 9th (1pm CT) - Prof. Peter Hinow, Department of Mathematical Sciences, University of Wisconsin - Milwaukee.

"Automated Feature Extraction from Large Cardiac Electrophysiological Data Sets"

Abstract: A multi-electrode array-based application for the long-term recording of action potentials from electrogenic cells makes possible exciting cardiac electrophysiology studies in health and disease. With hundreds of simultaneous electrode recordings being acquired over a period of days, the main challenge becomes achieving reliable signal identification and quantification. We set out to develop an algorithm capable of automatically extracting regions of high-quality action potentials from terabyte size experimental results and to map the trains of action potentials into a low-dimensional feature space for analysis. Our automatic segmentation algorithm finds regions of acceptable action potentials in large data sets of electrophysiological readings. We use spectral methods and support vector machines to classify our readings and to extract relevant features. We show that action potentials from the same cell site can be recorded over days without detrimental effects to the cell membrane. The variability between measurements 24 h apart is comparable to the natural variability of the features at a single time point. Our work contributes towards a non-invasive approach for cardiomyocyte functional maturation, as well as developmental, pathological, and pharmacological studies.
This is joint work with Viviana Zlochiver, Stacie Kroboth (Advocate Aurora Research Institute), and John Jurkiewicz (graduate student at UWM).

March 26th (1pm CT) - Dr. Ben Freedman, Department of Mathematical and Statistical Sciences, Marquette University.

“On Weakly Nonlinear Boundary Value Problems on Infinite intervals.” 

Abstract: In this talk, we will analyze boundary value problems on infinite intervals subject to weakly nonlinear boundary conditions. For such problems, we provide criteria for the existence of solutions as well as a qualitative description of the behavior of solutions depending on a parameter. We investigate the relationship between solutions to these weakly nonlinear problems and the solutions to a set of corresponding linear problems.

March 19th (1pm CT) - Prof. Alex Konomi, Department of Mathematical Sciences, University of Cincinnati.

“Computer model emulation with high-dimensional functional output in large-scale observing system uncertainty experiments: An application to NASA’s Orbiting Carbon Observatory-2 (OCO-2) mission” 

Abstract: Observing system uncertainty experiments (OSUEs) have been recently proposed as a cost-effective way to perform probabilistic assessment of retrievals for NASA’s Orbiting Carbon Observatory-2 (OCO-2) mission. One important component in the OCO-2 retrieval algorithm is a full-physics forward model that describes the mathematical relationship between atmospheric variables such as carbon dioxide and radiances measured by the remote sensing instrument. This complex forward model is computationally expensive but large-scale OSUEs require evaluation of this model numerous times, which makes it infeasible for comprehensive experiments. To tackle this issue, we develop a statistical emulator to facilitate large-scale OSUEs in the OCO-2 mission with independent emulation. Within each distinct spectral band, the emulator represents radiances output at irregular wavelengths via a linear combination of basis functions and random coefficients. These random coefficients are then modeled with nearest-neighbor Gaussian processes with built-in input dimension reduction via active subspace and gradient-based kernel dimension reduction. The proposed emulator reduces dimensionality in both input space and output space, so that fast computation is achieved within a fully Bayesian inference framework.

March 12th (1pm CT) - Drs. Sarah Hamilton, Elaine Spiller, and Mehdi Maadooliat, Jay Pantone, and Greg Ongie, Department of Mathematical and Statistical Sciences, Marquette University.

A number of faculty members will be giving short (5-10 min) intro/summaries of their research areas. This is a great opportunity for students at all levels, as well as faculty, to learn about current MSSC research and to start new collaborations.


Spring 2020

Colloquium talks will be held in the Katharine Reed Cudahy Building, Room 401 at Cudahy Hall on the Marquette University campus. Please address inquiries/suggestions to Dr. Hamilton at sarah.hamilton@marquette.edu

March 6th (2 pm CT) - Dr. Peter Muller, Department of Mathematics and Statistics, Villanova University.

Abstract. Electrical impedance tomography (EIT) is an imaging modality that measures currents and voltages on the surface of a body to image the electrical conductivity within the body.  Image reconstruction in EIT is a severely ill-posed, nonlinear inverse problem.  In this talk, I will present two direct reconstruction methods based on complex geometrical optics solutions: Calderón's method and Nachman's D-bar method.  Both methods provide a point-wise reconstruction of the image. Calderón’s method is a linearized approach while the D-bar method solves the fully non-linear inverse problem.  I will present both methods and their ability to address clinical application concerns. (more information)

February 28th (2 pm CT) - Dr. Elaine Spiller, Department of Mathematical and Statistical Sciences, Marquette University.

Abstract. Geophysical natural hazards — storm surge, post-fire debris flows, volcanic flows and ash fall, etc. — impact thousands to millions of people annually. Yet the most devastating hazards, those resulting in loss of life and property, are often both geographically and temporally localized. Thus they are effectively rare events to those impacted. We will present methodology to produce probabilistic hazard maps that can rapidly be updated to account for various aleatoric scenarios and epistemic uncertainties. This hazard analysis utilizes statistical emulators to combine computationally expensive simulations of the underlying geophysical processes with probabilistic descriptions of uncertain scenarios and model parameters. The end goal is not a map, but a family of maps that represent how a hazard threat evolves under different assumptions or different potential future scenarios. Further, this approach allows us to rapidly update hazard maps as new data or precursor information arrives. (more information)

February 14th  (2 pm CT) - Drs. Daniel Rowe, Anne Clough, Sarah Hamilton, Naveen Bansal, Wenhui Sheng, Elaine Spiller, and Mehdi Maadooliat, Department of Mathematical and Statistical Sciences, Marquette University.

A number of faculty members will be giving short (5 min) intro/summaries of their research areas. This is a great opportunity for students at all levels, as well as faculty, to learn about current MSSC research and to start new collaborations.

February 3rd (1 pm CT) - Swati Patel, Department of Mathematics, Tulane University, On Dynamics for Maintaining Biological Diversity at Various Scales.

Abstract. One of the fundamental questions in ecology and evolutionary genetics is how is biological diversity maintained within and amongst populations. Classical nonlinear differential equations that capture population or genetic interactions have played an important role in developing biological theories on how diversity is maintained. As ongoing empirical investigations uncover the nuances of these interactions, they open the way for more sophisticated models and the need for expanding mathematical methods to analyze them. In this talk, I will develop two sets of multi-scale models, motivated by recent empirical evidence. The first couples differential equations that capture interactions amongst populations with variation within the population. At a finer scale, the second models specific protein-gene interactions that influence population-level traits. For both models, I will discuss new mathematical questions and analysis that provides insight into mechanisms that enable diversity at these various scales. (more information)

January 31st (1 pm CT) - Owen Lewis, Department of Mathematics, Florida State University, Electrodiffusion Mediated Maintenance of the Gastric Mucus Layer.

Abstract. Diffusion of charged particles, or electrodiffusion, plays an important role in many physiological systems including the human stomach. The gastric mucus layer is widely recognized to serve a protective function, shielding your stomach wall from the extreme acidity and digestive enzymes present in the stomach. However, there is still much debate regarding the control of electrodiffusive transport through the mucus layer. In this talk, I will discuss a mathematical description of electrodiffusion within a two-phase gel model of gastric mucus and the challenges associated with its analysis and numerical simulation. This model is used to investigate physiological hypotheses regarding gastric layer maintenance that are beyond current experimental techniques. (more information)

January 22nd (1 pm CT) Greg Ongie, Department of Statistics, University of Chicago, Rethinking regularization in modern machine learning and computational imaging.

Abstract. Optimization is central to both supervised machine learning and inverse problems in computational imaging. These problems are often ill-posed and some form of regularization is necessary to obtain a useful solution. However, new paradigms in machine learning and computational imaging necessitate rethinking the role of regularization, as I will illustrate with two examples. First, in the context of supervised learning with shallow neural networks, I will show how a commonly used form of regularization has a surprising reinterpretation as a convex regularizer in function space. This yields novel insights into the role of overparameterization and depth in learning with neural networks having ReLU activations. Second, I will discuss a novel network architecture for solving linear inverse problems in computational imaging called a Neumann network. Rather than using a pre-specified regularizer, Neumann networks effectively learn a regularizer from training data, outperforming classical techniques. Beyond these two examples, I will show how many open problems in the mathematical foundations of deep learning and computational imaging relate to understanding regularization in its many forms. (more information)

January 21st (1 pm CT) - Scott Hottovy, Department of Mathematics, United States Naval Academy, A simple stochastic model of tropical atmospheric waves.

As tropical storms go, you have probably heard of Hurricanes, Tropical Cyclones, El Niño, and La Niña. But you probably haven't heard of the Madden-Julian Oscillation (MJO). It is the major contributor to rainfall in tropical regions and influences the climate in Wisconsin regularly. Unlike Hurricanes and El Niño, the MJO is still not well understood. In an effort to understand the mechanisms of the MJO, I will describe a model building from a dynamically stationary "background" tropical rainfall model and coupling that to a tropical wave model. These models use Stochastic Differential Equations (SDE) and Stochastic Partial Differential Equations (SPDE) as the building blocks. In the "background" model, an SDE model is used which leads to characteristics of criticality and phase transitions. For the full model with waves, we use a continuous one-dimensional SPDE. Because of the simplicity of the models, we are able to solve many statistics exactly, or run fast numerical experiments. (more information)


Fall 2019

Colloquium dates and speakers for Fall 2019 - Unless specified, the talks will begin at 2:00pm CT in Room 401 at Cudahy Hall.

  • September 6th - Michael Albert, Department of Computer Science, University of Otago, New Zealand, Wilf-equivalence and Wilf-collapse.
  • September 27th - Guannan Wang, Department of Mathematics, College of William and Mary, Williamsburg, Simultaneous confidence corridors for mean functions in functional data analysis of imaging data
  • October 4th - Billy Herzberg, Department of MSSC, Marquette University, Improving EIT (Electrical Impedance Tomography) images using deep learning
  • October 11th - Marquette University Computational Sciences Summer Research Fellowship Talks: 
    • Nazmus SakibUnderstanding confounding medical interventions in Sepsis treatment: A step towards multi-parameter intelligent sepsis prediction in ICU.
    • Ziynet Nesibe Kesimoglu, Inferring competing endogenous RNA (ceRNA) interactions in cancer 
  • October 25th - Jordan Trinka, Department of MSSC, Marquette University, Milwaukee, Functional Singular Spectrum Analysis.
  • November 1st - Jacob R. Pichelmeyer, Mathematics Department, Kansas State University, Manhattan, KS.
  • November 8th - Andreas Hauptmann, Department of Mathematical Sciences, University of Oulu, Finland.
  • November 15th - Sunil Mathew, Joseph Coelho, Department of MSSC, Marquette University, Milwaukee, Computational Sciences Student Research Presentations.
  • November 22nd - Md Manzur Rahman, Paromita Nitu, Department of MSSC, Marquette University, Milwaukee, Computational Sciences Student Research Presentations.
  • December 6th - Rasha Atshan, Andrew Werra, Youming Wang, Wei Xu, Department of MSSC, Marquette University, Milwaukee, Applied Statistics Practica Summer Presentations.

Spring 2019

Fall 2018

Spring 2018

Fall 2017

Spring 2017