Theses/Dissertations

Theses and Dissertations

 

The following students have graduated from the IDEA lab and have provided copies of their thesis or dissertation. In most cases, there is also the data and code used in their experiments. Click on the names below for their individual pages.

  • Amanda Burke (2024): Expert-Guided Machine Learning for Meteorological Predictions Across Spatio-Temporal Scales
  • Stuart Edris (2024): Evaluation of Flash Drought Identification with Machine Learning Techniques
  • Andrew Justin (2024): Explainable Frontal Boundary Predictions for Applications in Operational Environments 
  • Tobias Schmidt (2023): Gridded Hail Nowcasting using UNets, Lightning Observations, and the Warn-on-Forecast System
  • Chad Wiley (2023): Using Machine Learning to Improve the NSSL’s Warn-On-Forecast System’s Prediction of Thunderstorm Location
  • David Harrison (2022): Machine Learning Co-Production in Operational Meteorology. PhD Thesis, School of Meteorology, University of Oklahoma.
  • Katherine Avery (2020): Automated Location of Bird Roosts using NEXRAD Data and Image Segmentation. Master’s Thesis, School of Computer Science, University of Oklahoma.
  • Ryan Lagerquist (2020): Using Deep Learning to Improve Prediction and Understanding of High-Impact Weather. PhD Thesis, School of Meteorology, University of Oklahoma.
  • Amanda Burke (2019): Using Machine Learning Applications and HREFv2 to Enhance Hail Prediction for Operations. Master’s Thesis, School of Meteorology, University of Oklahoma.
  • William Booker (2019): Evaluating GAM-like Neural Network Architectures for Interpretable Machine Learning. Master’s Thesis, School of Computer Science, University of Oklahoma.
  • Khaled Jabr, MS (2018): Using Novelty Seeking Reward Evolution Strategies to Train Generative Adversarial Networks. Master’s Thesis, School of Computer Science, University of Oklahoma.
  • Taner Davis, MS (2018): Real-Time Gesture Recognition with Mini Drones. Master’s Thesis, School of Computer Science, University of Oklahoma.
  • David Harrison, MS (2018): Correcting, Improving, and Verifying Automated Guidance in a New Warning Paradigm. Master’s Thesis, School of Meteorology, University of Oklahoma.
  • Carmen Chilson, MS (2017): Automated Detection of Bird Roosts using NEXRAD Radar Data and Convolutional Neural Networks. Master’s Thesis, School of Computer Science, University of Oklahoma.
  • David Gagne, PhD (2016): Coupling Data Science Techniques and Numerical Weather Prediction Models for High-Impact Weather Prediction, PhD Dissertation, School of Meteorology, University of Oklahoma.
  • Ryan Lagerquist, MS (2016): Using Machine Learning To Predict Damaging Straight-Line Convective Winds, Master’s Thesis, School of Meteorology, University of Oklahoma.
  • Brittany Dahl, MS (2014): Sensitivity of Vortex Production to Small Environmental Perturbations in High-Resolution Supercell Simulations School of Meteorology, University of Oklahoma.
  • Thibault Lucidarme, MS (2014). Automatic Recognition of Supercells Thunderstorms in Numerical Weather Data. Master’s Thesis, School of Computer Science, University of Oklahoma.
  • Morgan Robertson, MS (2014). Learning Ensembles of Linear Gaussian Networks for Nonlinear and Spatial Data. Master’s Thesis, School of Computer Science, University of Oklahoma.
  • Andrew MacKenzie, MS (2013). An Automated, Multi-parameter Dryline Identification and Tracking Algorithm. Interdisciplinary Master’s Thesis, Graduate School, University of Oklahoma.
  • Carlos Sanchez, PhD (2013). A Risk and Trust Security Framework for the Pervasive Mobile Environment. PhD Dissertation, School of Computer Science, University of Oklahoma.
  • Xiaolei Yan, MS (2013). Reinforcement Learning Schedule for Heteorogenous Multi-Core Processors.Master’s Thesis, School of Computer Science, University of Oklahoma.
  • David Gagne, MS (2012). Machine Learning Enhancement of Storm Scale Ensemble Precipitation Forecasts. Master’s Thesis, School of Meteorology, University of Oklahoma.
  • Zachery Tidwell, MS (2012) Expert Move Prediction for Computer Go using Spatial Probability Trees. Master’s Thesis, School of Computer Science, University of Oklahoma.
  • Scott Hellman, MS (2012). Learning Ensembled Dynamic Bayesian Networks. Master’s Thesis, School of Computer Science, University of Oklahoma.
  • Rachel Shadoan, MS (2012). Visual Analysis of Higher-Order Conjunct Relationships in Multi-Dimensional Data using a Hypergraph Query System. Master’s Thesis, School of Computer Science, University of Oklahoma.
  • Christopher Utz, MS (2010). Learning Ensembles of Bayesian Network Structures Using Random Forest Techniques. Master’s Thesis, School of Computer Science, University of Oklahoma.
  • Nathaniel Troutman, MS (2010). Enhanced Spatiotemporal Relational Probability Trees and Forests. Master’s Thesis, School of Computer Science, University of Oklahoma.
  • Derek Rosendahl, MS (2008). Identifying Precursors to Strong Low-Level Rotation within Numerically Simulated Supercell Thunderstorms: A Data Mining Approach.Master’s Thesis, School of Meteorology, University of Oklahoma.