As a data scientist and researcher, I bridge the gap between complex business problems and data solutions.

A man with a beard wearing a suit and purple tie against a dark background.

My data science career has spanned the healthcare, finance, and technology industries, with projects ranging from ad hoc statistical analysis to production-grade machine learning systems.

As a Ph.D. student specializing in deep learning-based computer vision and image compression, my goal is to build ethical and energy-conscious machine learning solutions to help autonomous systems better perceive the world around them.

If you’d like to learn more about my approach to technical problems, project design, or team development, please feel free to reach out.

Education

  • University of Missouri - Kansas City

    • Research Area: Multi-sensor fusion for deep learning-based image compression

    • Courses:

      • Computer Vision

      • Deep Learning

      • Compression

      • Algorithms

      • NLP

  • Kansas State University

    • Thesis: The Application and Interpretation of the Two-Parameter Item Response Model in the Context of Replicated Preference Testing

    • Courses:

      • Statistical Theory

      • Statistical Computing

      • Experimental Design

      • Consulting

      • Linear Models

      • Survival Analysis

  • Kansas State University

    • Research Programs:

      • Summer Institute for Training in Biostatistics (University of Pittsburgh)

      • Mount Holyoke College REU

    • Courses:

      • Statistics

      • Calculus

      • C++ Programming

      • Linear Algebra

      • Experimental Design

      • Real Analysis

      • Topology

      • Number Theory

      • Engineering Physics

Work Experience

    • LADDER

      • Summary: Device error remediation through a scalable system with multiple standalone components - includes anomaly detection for 15+ metrics, risk modeling for 4 failure types, risk attribution, and proactive remediation

      • Role: Architect, Project Manager, & Lead Developer

      • Tech: PySpark, Pandas, scikit-survival, Airflow, EMR

    • Smart DEX

      • Summary: Reimagining of device experience score from weighted averages to supervised user sentiment prediction

      • Role: Architect & Lead Developer

      • Tech: PySpark, PyTorch, EMR

    • Personas

      • Summary: Software/hardware usage clustering for HP device recommendation

      • Role: Architect & Project Manager

      • Tech: PySpark, MLlib, Airflow, EMR

    • Fleet Simulator

      • Summary: Suite of telemetry and failure models to simulate/optimize performance based on device specifications

      • Role: Architect, Project Manager, & Lead Developer

      • Tech: PySpark, Pandas, statsmodels, scikit-learn, hyperopt, matplotlib, Airflow, EMR

    • xlr8

      • Summary: Utilities for data science acceleration, including feature engineering, data I/O, and logging

      • Role: Architect & Lead Developer

      • Tech: PySpark, Pandas, AWS SDK, Keras, pymsteams

    • Algorithm Optimization

      • Summary: Research and experimentation on constrained 2-D Knapsack Problem for revenue optimization - discovered small adjustment worth $350k/year, identified future research paths for transformational improvements

      • Role: Lead Developer

      • Tech: Python, Redshift

    • Customer Targeting

      • Summary: Refreshed targeting system with improved conversion rates and efficiency - this required a paradigm shift in customer prioritization from predicted conversion likelihood to predicted effects of engagement

      • Role: Architect & Lead Developer

      • Tech: Python, SageMaker AutoPilot, Pandas, Redshift, Salesforce

    • Revenue Forecasting

      • Summary: Ensemble forecasting model to improve on manual budgeting process

      • Role: Architect & Lead Developer

      • Tech: XGBoost, Prophet, sktime, Pandas, Redshift

    • Cognitive Processing Engine

      • Summary: Data extraction from unstructured PDFs, using CNN-based clustering and object detection - projected to reduce operational costs by more than $1M per year

      • Role: Architect & Lead Developer

      • Tech: Python, Azure Cognitive Services, Keras, scikit-learn, tkinter, SQL Server

    • Customer Targeting

      • Summary: Tableau dashboard and analysis to prioritize customers with greatest predicted engagement impact

      • Role: Analyst

      • Tech: Tableau, SQL Server, R

    • Sales Assistant

      • Summary: Automated prospecting, web scraping, and data aggregation for pre-sales intelligence

      • Role: Lead Developer

      • Tech: SQL Server, Python, Salesforce

    • Affordable Care Act Utilization

      • Summary: Intervention analysis to investigate the effects of the ACA (prepared for the Clinton 2016 campaign)

      • Role: Developer

      • Tech: R, SQL Server

    • Executive-Level Claims Dashboard

      • Summary: Comprehensive claims and utilization dashboard for large healthcare organizations - this was Epic's first Tableau product offering and won an internal award for achievement

      • Role: Data Specialist (Simulated dataset for product demo)

      • Tech: Tableau, Excel

Instructional Experience

  • Kansas State University - Salina

    Advise on the structure and desired outcomes of the Machine Learning and Autonomous Systems program

  • Kansas State University - Salina

    Instructed two short courses comprising the Applied Data Science & Machine Learning Professional Certificate, designed to teach industry professionals the fundamentals of DS/ML

    • Introduction to Data Science - Data management, analysis, and feature engineering using SQL

    • Introduction to Machine Learning - Supervised and unsupervised learning with Python

  • Kansas State University - College of Business

    Led a series of workshops for students in the M.S. Data Analytics program

    • Machine Learning - Classification (2022)

    • Data Wrangling (2021)

    • Careers in Data Science (2020-2021)

    • Machine Learning - Forecasting (2020)

  • Kansas State University - Dept. of Statistics

    Advised university researchers on a wide variety of statistical topics through the Statistical Consulting Lab, including experimental design, repeated measures analysis, logistic regression analysis, and model interpretation

  • Kansas State University - Dept. of Statistics

    Designed curriculum, instructed, and tutored for Business and Economics Statistics I & II

  • Kansas State University

    Undergraduate-level tutoring for various mathematics and statistics courses, through the Scholars Assisting Scholars program, the Statistics Help Lab, and the K-State Athletic Department

  • Kansas State University - Choral Music Division

    Served as Lead Counselor for the highly selective Summer Choral Institute, designed to build leadership and musical skills in top-performing high school students

Toolbox

Traditional Data Science Methods

  • Linear Models (Polynomial, Logistic, Probit)

  • Tree-Based Models (Random Forest, XGBoost)

  • Classification (kNN, SVM)

  • Forecasting (ARIMA, LSTM, Decomposition)

  • Clustering (K-Means, Hierarchical, DBSCAN, GMM)

  • Anomaly Detection (Isolation Forest, Mahalanobis)

  • Dimensionality Reduction (PCA, t-SNE, Autoencoder)

  • Scoring (Predictive, Normalization, Weighted Avg)

  • Hypothesis Testing (t-test, ANOVA, Chi-Square)

  • Feature Importance (Shapley, LIME, Permutation)

  • Ensembles (Bagging, Boosting, Stacking, Blending)

Advanced Techniques

  • Deep Learning (CNN, RNN, LSTM, GAN, AE, Transformer)​​

  • Computer Vision (Detection, Tracking, Classification)

  • NLP (Classification, Sentiment, Topic Modeling)

  • Image Compression (DL-Based Codecs, JPEG, HEIF)

  • Risk Modeling (Cox Model, Kaplain-Meier Curve)

  • Ordinal Regression (GLM, CORAL)

  • Cardinality Reduction (Embedding, Encoding)

  • Optimization (Gradient Descent, Weighted Sum, TPE)

  • Data Augmentation (SMOTE, Image Transforms, Noise)

  • Recommendations (Clustering, Predictive Modeling)

  • Web Scraping (HTML, XPath, Regex)

Tech Stack

Python

Python

GitHub

GitHub

VS Code

VS Code

PyTorch

PyTorch

scikit-learn

scikit-learn

Keras

Keras

Jupyter

Jupyter

AWS

AWS

SageMaker

SageMaker

PySpark

PySpark

Pandas

Pandas

NumPy

NumPy

R

R

PostgreSQL

PostgreSQL

SQL Server

SQL Server

OpenCV

OpenCV

XGBoost

XGBoost

statsmodels

statsmodels

Tableau

Tableau

Airflow

Airflow

Jira

Jira

SciPy

SciPy

Prophet

Prophet

NLTK

NLTK

Arc

Arc

Obsidian

Obsidian

Figma

Figma

matplotlib

matplotlib

BeautifulSoup

BeautifulSoup

Selenium

Selenium