My research sits at the intersection of applied mathematics, machine learning, and real-world data. I'm drawn to problems where mathematical structure can extract meaningful information from complex systems - whether that's estimating physiological parameters from medical imaging, or forecasting demand patterns in large-scale contact center operations. I enjoy cross-disciplinary collaboration, as working with people from different fields often surfaces new ideas and sharpens the solution.

Forecasting for Contact Center Operations (Aspect)

At Aspect, I have been fortunate to work in multiple roles all aligning with research and development of algorithms and machine learning models for contact center forecasting. This work involves evaluating supervised learning models, transformers, and MLPs to identify the optimal architecture for forecasting contact center needs across large customer deployments. I processed and cleaned large-scale contact center datasets, configured and tuned hyperparameters using MLflow and Amazon SageMaker, and worked with a team to achieve an 3x improvement in forecast accuracy over prior manual processes. I also designed a multiple linear regression forecasting algorithm with user defined inputs - collaborating directly with customers to meet requirements and validate models against historical data.

Inverse Problems and Lung Modeling (PhD Research, Colorado State University)

My doctoral research focused on estimating airway resistance throughout the bronchial tree using an inverse problem approach and a mathematical model. Working with a multidisciplinary medical team, I collected mechanical ventilation and electrical impedance tomography (EIT) data from ICU patients, developed an asymmetric multi-compartment lung model, and used MATLAB's fmincon to solve the resulting parameter estimation problem. A Python script I developed mapped EIT image pixel locations to scaled time and volume vectors, bridging the imaging and modeling pipelines.

Papers

Estimation of airway resistance throughout the bronchial tree from mechanical ventilation output data with Emily Heavner, Jennifer L. Mueller, Kim McFann, Julie Dunn, Omar Alnachoukati, Corey Mohnike. Accepted to appear in Applied Mathematics for Modern Challenges (AMMC), September 2024.

An inverse problem and multi-compartment lung model for the estimation of lung airway resistance throughout the bronchial tree, Colorado State University Libraries, December 2022.

Assurance Arguments for the Non-graphically-inclined: Two Approaches with Michael C. Holloway, NASA/TM-2017-219650, July, 2017.

Podcast

Not Just the A-Word - Making AI Actually Work at Work, Aspect Podcast, September 2025.

Poster

Computational Estimation of Airway Resistance Throughout the Bronchial Tree, Joint Mathematics Meeting, Virtual, April 2022.

Investigating Airway Resistance Values in the Alveolar Tree using an Inverse Problem and a Dynamical System Lung Model, Conference on Modern Challenges in Imaging In the Footsteps of Allan MacLeod Cormack On the Fortieth Anniversary of his Nobel Prize, Tufts University, August 2019.

Talks

Bunnies, Arguments, Lungs, and Forecasts - Oh My!, Information Sharing Session, Aberdeen Proving Ground, November 2024.

An Inverse Problem and Multi-Compartment Lung Model for the Estimation of Lung Airway Resistance throughout the Bronchial Tree, Public Thesis Defense, CSU, October 2022.

Estimating Airway Resistance in the Bronchial Tree Using an Inverse Problem and an Asymmetric Multi-Compartment Lung Model , Guest Lecture in Math 676: Inverse Problems: a Data Science Perspective, Colorado State University, April 2022

Estimation of Airway Resistance Throughout the Bronchial Tree using an Inverse Problem and an Asymmetric Multi-Compartment Lung Model , Joint Mathematics Meeting, Virtual, April 2022.

Introduction to an Asymmetric Multi-compartment Lung Model and Parameter Estimation, Greenslopes Seminar, Colorado State University, April 2022.

Estimation of airway resistance throughout the bronchial tree from mechanical ventilation output data , Society for Mathematical Biology Annual Meeting, Virtual, June 2021.

Introduction to Inverse Problems and their Applicationsin the Medical Field , Greenslopes Seminar, Colorado State University, March 2020.

What is Regularization and Why Do We Care?, Data Science Seminar, Colorado State University, November 2019.

Inverse Problems and their Applications, Solving Problems in Applied Math Lab, Colorado State University, November 2019.

Derivation of Shallow Water Equations, Solving Problems in Applied Math Lab, Colorado State University, March 2019.

Introduction and Numerical Simulation of a Dynamical System Lung Model, SIAM Front Range Applied Math Conference , University of Colorado Denver, March 2019.

Lung Model Using Switched Dynamical System and EIT, Greenslopes Seminar, Colorado State University, October 2018.

Stochastic Traffic Flow Model Based on Hampton Roads, VA, Christopher Newport University, December 2016

Species Competition in a Lotka Volterra Model Using Netlogo , Christopher Newport University, March 2016