J. Alex Hurt, PhD

About

Machine Learning Engineer and Data Scientist

  • Location: Columbia, Missouri or Remote
  • Education: PhD Computer Science with AI/ML Graduate Certificate
  • Research Emphasis: Machine Learning & High Performance Computing
  • Email: jalexhurt.phd@gmail.com
  • Resume: Available below or as PDF

Research Highlights

  • Built custom Kubernetes Python Library to scale ML experimentation and reduce wall clock time from 90 days to less than 1 week
  • More than eight years experience in academic research serving Data Scientist and ML Engineer roles
  • Led HPC and ML projects funded by NSF and DoD in excess of $1.4 million
  • Designed, developed, and delivered applied ML algorithms to DoD sponsors

Resume

Download as PDF

Summary

J. Alex Hurt, PhD

Since 2017, I have worked as a Machine Learning and High Performance Computing researcher, utilizing state-of-the-art technologies to optimally leverage large amounts of data as well as enable and accelerate real-world applications of Machine Learning. With over eight years of experience and more than thirty-five publications, I am capable of performing in critical data science and machine learning roles

Experience

Assistant Research Professor

2022-Present

Center for Geospatial Intelligence - Columbia, Missouri

  • Led HPC and ML Projects: Served as Principal Investigator on NSF and DoD-funded AI/ML and HPC Projects
  • Delivered Containerized Solutions: Performed critical roles on several ML projects for DoD Sponsors, including serving as primary Technical Point of Contact for delivery of applied ML algorithms
  • Data Pipelines for ML: Designed, developed, and maintained custom data pipelines and libraries used on multiple projects to perform Computer Vision tasks using novel deep learning architectures on overhead imagery
  • Automation and Scale: Led regionally funded efforts to scale and automate machine learning and other data intensive workflows
  • Data Science Instructor: Developed and taught graduate-level courses in Data Science and Machine Learning topics: Cloud Computing, Data Visualization, Supervised Learning, and Computer Vision

Graduate Research Assistant

2018-2022

Center for Geospatial Intelligence - Columbia, Missouri

  • Deep Learning: Performed and analyzed suites of experiments for applications of ML on Satellite Imagery using frameworks such as PyTorch, Keras, and Tensorflow.
  • Geospatial Data Framework: Part of a team that designed, implemented, and maintained a geospatial data framework to aid in the creation of Remote Sensing datasets for training Deep Learning models.

Graduate Teaching Assistant

2018-2019

MU Data Science and Analytics - Columbia, Missouri

  • Helped develop course material for several courses included geospatial data analytics and big data analytics
  • Assisted students with course material

Database Administrator and Webmaster

2016-2017

Pires' Research Lab - Columbia, Missouri

  • Designed, implemented, and maintained a full MySQL database and web application with no technical assistance
  • Responsibilities included requirements elicitation, design, development, UAT, and all other parts of the Software Development life cycle

Web Development Intern

2016-2017

The Boeing Company - St. Louis, Missouri

  • Designed, developed, deployed, and maintained a JavaScript Web Application for estimating labor costs to the company
  • Part of a two person development team utilizing an Agile Methodology and responsible for all aspects of the Software Development Life Cycle including deployments to cloud based infrastructure using a continuous integration pipeline
  • Updated a customizable web application for customers to view and modify statistics on various Boeing products
  • Designed, developed, and tested various functionalities and corrected defects within the application.

Education

Doctor of Philosophy in Computer Science

2018-2022

University of Missouri - Columbia

  • Dissertation Title: Increasing Compulsory Shape Bias in Deep Neural Networks with Differential Morphology for Classification and Detection in Remote Sensing Imagery
  • Graduate Certificate: Artificial Intelligence and Machine Learning
  • FastTrack Program: Dual enrollment in Undergraduate/Graduate Schools

Bachelor of Science in Computer Science

2015-2018

University of Missouri - Columbia

  • Minor: Math
  • Summa Cum Laude
  • GPA: 3.98
  • Engineering Ambassador: Representative for College of Engineering
  • Leader of Machine Learning Special Interest Group
  • Member of Upsilon Pi Epsilon Honor Society

Skills

Programming

Python 3

C/C++

SQL

CUDA

LaTeX

pl/pgSQL


Machine Learning

PyTorch

MMDetection

Ultralytics

Sci-Kit Learn

Tensorflow

Keras


Technologies

Git

Docker

GitLab CI/CD

Linux

Ubuntu, Debian, Fedora, CentOS, Arch

Kubernetes

PostgreSQL

Jupyter

Google Cloud

AWS

Spark

SLURM

Publications

14

First Author Publications

36

Total Publications

8

Years of Publications

  • Hurt, J. Alex, Bajkowski, Trevor, Scott, Grant J., Davis, Curt. "Evaluation of Deep Neural Transformers on Modern Remote Sensing Datasets" IEEE Journal on Selected Topics in Applied Remote Sensing (JSTARS). under review.
  • Ouadou, Anes, Huangal, David, Alshehri, Mariam, Scott, Grant J., Hurt, J. Alex, "Semantic Segmentation of Burned Areas in Sentinel-2 Satellite Imagery Using Deep Learning Transformer and Convolutional Attention Networks". IEEE Journal on Selected Topics in Applied Remote Sensing (JSTARS). under review.
  • Hurt, J. Alex, Ouadou, Anes, Alshehri, Mariam, Scott, Grant J., "Scaling Deep Learning Research with Kubernetes on the NRP Nautilus HyperCluster" arXiv. November 2024. DOI: 10.48550/arXiv.2411.12038
  • Hurt, J. Alex, Scott, Grant J., Weitzel, Derek, Zhu, Huijun. "Adventures with Grace Hopper AI Super Chip and the National Research Platform" arXiv. October 2024. DOI: 10.48550/arXiv.2410.16487
  • Bajkowski, Trevor, Hurt, J. Alex, Scully, Christopher, Keller, James, Carley, Samantha, Scott, Grant, Price, Stanton. "Comparing Visual Co-registration Methods for UAV and Satellite RGB Imagery with Semantic Filtering of Key Points." SPIE 2024: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications. May 2024. DOI: 10.1117/12.3013504
  • Galusha, Aquila, Keller, James M., Hurt, J. Alex, Teaney, Brian, Talbott, Marie, Scott, Grant J. "Calibration for Multi-algorithm Fusion of Object Detection in MWIR and LWIR." 2024 Military Sensing Symposia. February 2024.
  • Hurt, J. Alex, Popescu, Ilinca, Davis, Curt H., Scott, Grant J. "Anthropogenic Object Localization: Evaluation of Broad-Area High-Resolution Imagery Scans Using Deep Learning in Overhead Imagery" Sensors 23, no. 18: 7766. 2023. DOI 10.3390/s23187766
  • Hurt, J. Alex, Bajkowski, Trevor, Scott, Grant J., Davis, Curt H. "Overhead Object Detection with Channel Attention for High Resolution Multi-Spectral Satellite Imagery and DMP-extracted Shape Features." 2023 Applied Imagery and Pattern Recognition (AIPR). September 2023. DOI: 10.1109/AIPR60534.2023.10440699
  • Gaines, Timothy, Hurt, J. Alex, Boyle, Camden, Maschmann, Matthew, Keller, James, Scott, Grant J., Price, Stanton. "Towards Automated Nanoenergetic Reaction Characterization with Computational Vision." 2023 Applied Imagery and Pattern Recognition (AIPR). September 2023. DOI: 10.1109/AIPR60534.2023.10440709
  • Hurt, J. Alex, Davis, Curt H., Scott, Grant J. "Hybrid Differential Morphological Profile Enabled Faster R-CNN for Object Detection in High-Resolution Remote Sensing Imagery." 2023 International Geoscience and Remote Sensing Symposium. July 2023. DOI: 10.1109/IGARSS52108.2023.10281424
  • Ouadou, Anes, Huangal, David, Hurt, J. Alex, Scott, Grant J. "Semantic Segmentation Of Burned Areas in Sentinel-2 Satellite Images using Deep Learning Models." 2023 International Geoscience and Remote Sensing Symposium. July 2023. DOI: 10.1109/IGARSS52108.2023.10282323
  • Bajkowski, Trevor, Hurt, J. Alex, Dale, Jeffrey, Keller, James, Scott, Grant J., Price, Stanton. "Comparing hand-crafted and learned key-point feature extraction for co-location of sequential low-altitude UAS video frames." SPIE 2023: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications. May 2023. DOI: 10.1117/12.2663586
  • Gaines, Timothy, Boyle, Camden, Hurt, J. Alex, Maschmann, Matthew R., Keller, James M., Scott, Grant J., Price, Stanton R. "Towards Automatic Characterization of Nano-Energetic Material Response to Directed Energy." SPIE 2023: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications. May 2023. DOI: 10.1117/12.2663877
  • Galusha, Aquila, Keller, James M., Hurt, J. Alex, Alvey, Brendan, Talbott, Marie, Scott, Grant J. "Multi-modality, Multi-algorithm Fusion for Object Detection in MWIR and LWIR." 2023 Military Sensing Symposia. February 2023.
  • Hurt, J. Alex, Keller, James, Scott, Grant J. "Evolutionary Learning of Differential Morphological Profile Structure for Shape Feature Enabled Faster R-CNN" 2022 IEEE World Congress on Computational Intelligence. July 2022. DOI: 10.1109/IJCNN55064.2022.9892177
  • Bajkowski, Trevor, Hurt, J. Alex, Davis, Curt H, Scott, Grant J. "Classification of an 8-band Multi-Spectral Dataset using DCNNs with Weight Initializations derived from pre-trained RGB Networks" 2022 International Geoscience and Remote Sensing Symposium. July 2022. DOI: 10.1109/IGARSS46834.2022.9884492
  • Bajkowski, Trevor M., Hurt, J. Alex, Huangal, David, Dale, Jeffery, Keller, James, Scott, Grant J., Price, Stanton R. "Evaluating Visuospatial Features for Tracking Hazards in Overhead UAS Imagery." 2021 Applied Imagery Pattern Recognition Workshop October 2021. DOI: 10.1109/AIPR52630.2021.9762206
  • Hurt, J. Alex, Bajkowski, Trevor, Scott, Grant J. "Improved classification of high resolution remote sensing imagery with Differential Morphological Profile Neural Network." 2021 International Geoscience and Remote Sensing Symposium. July 2021. DOI: 10.1109/IGARSS47720.2021.9553057
  • Hurt, J. Alex, Scott, Grant J., Huangal, David, Dale, Jeffery, Bajkowski, Trevor M., Keller, James, Price, Stanton R. "Differential Morphological Profile Neural Network for Maneuverability Hazard Detection in Unmanned Aerial System Imagery." Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III April 2021. DOI: 10.1117/12.2585843
  • Bajkowski, Trevor M., Hurt, J. Alex, Huangal, David, Dale, Jeffery, Keller, James, Scott, Grant J., Price, Stanton R. "Accumulating confidence for deep neural network object detections and semantic segmentations in sequential UAS imagery through spatiotemporal feature correspondences generated from SfM techniques." Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III April 2021. DOI: 10.1117/12.2585905
  • Dale, Jeffery, Bajkowski, Trevor M., Hurt, J. Alex, Huangal, David, Earle, Nelson, Keller, James, Scott, Grant J., Price, Stanton R. "Towards an explainable AI adjunct to deep network obstacle detection for multisensor vehicle maneuverability assessment." Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III April 2021. DOI: 10.1117/12.2585906
  • Hurt, J. Alex, Huangal, David, Davis, Curt H., Scott, Grant J. "Enabling Machine-Assisted Visual Analytics for High-Resolution Remote Sensing Imagery with Enhanced Benchmark Meta-Dataset Training of NAS Neural Networks." IEEE Big Data 2020. December 2020. DOI: 10.1109/BigData50022.2020.9378199
  • Bajkowski, Trevor M., Hurt, J. Alex, Scott, Grant J., Davis, Curt H. "Extending Deep Convolutional Neural Networks from 3-color to Full Multispectral Remote Sensing Imagery." IEEE Big Data 2020. December 2020. DOI: 10.1109/BigData50022.2020.9378086
  • Bajkowski, Trevor M., Huangal, David, Hurt, J. Alex, Dale, Jeffrey J., Keller, James, Scott, Grant J., Price, Stanton. "Spatiotemporal Maneuverability Hazard Analytics from Low-Altitude UAS Sensors." 2020 Applied Imagery Pattern Recognition Workshop. October 2020. DOI: 10.1109/AIPR50011.2020.9425160
  • Scott, Grant J., Hurt, J. Alex, Yang, Alex, Islam, Muhammad Aminul, Anderson, Derek T., Davis, Curt H. "Differential Morphological Profile Neural Network for Object Detection in Overhead Imagery." 2020 International Joint Conference on Neural Networks (IJCNN). July 2020. DOI: 10.1109/IJCNN48605.2020.9207387
  • Hurt, J. Alex, Huangal, David, Dale, Jeffery, Bajkowski, Trevor, Keller, James, Scott, Grant J., Price, Stanton. "Maneuverability hazard detection and localization in low-altitude UAS imagery." Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II. April 2020. DOI: 10.1117/12.2557609
  • Huangal, David, Dale, Jeffery, Hurt, J. Alex, Bajkowski, Trevor, Keller, James, Scott, Grant J., Price, Stanton. "Evaluating deep road segmentation techniques for low-altitude UAS imagery." Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II. April 2020. DOI: 10.1117/12.2557610
  • Hurt, J. Alex, Huangal, David, Davis, Curt H., Scott, Grant J. "A Comparison of Deep Learning Vehicle Group Detection in Satellite Imagery." IEEE Big Data 2019. December 2019. DOI: 10.1109/BigData47090.2019.9006415
  • Cannaday, Alan B., Chastain, Raymond L., Hurt, J. Alex, Davis, Curt H., Scott, Grant J., Maltenford, A.J. "Decision-Level Fusion of DNN Outputs for Improving Feature Detection Performance on Large-Scale Remote Sensing Image Datasets." IEEE Big Data 2019. December 2019. DOI: 10.1109/BigData47090.2019.9006502
  • Yang, Alex, Hurt, J. Alex, Veal, Charlie T., Scott, Grant J. "Remote Sensing Object Localization with Deep Heterogeneous Superpixel Features." IEEE Big Data 2019. December 2019. DOI: 10.1109/BigData47090.2019.9006120
  • Hurt, J. Alex, Scott, Grant J., Davis, Curt H. "Comparison of Deep Learning Model Performance Between Meta-Dataset Training Versus Deep Neural Ensembles." 2019 International Geoscience and Remote Sensing Symposium. July 2019. DOI: 10.1109/IGARSS.2019.8898596
  • Veal, Charlie, Yang, Alex, Hurt, J. Alex, Islam, Muhammad Aminul, Anderson, Derek T, Scott, Grant J., Havens, Timothy C., Keller, James M., Tang, Bo. "Linear Order Statistic Neuron." 2019 IEEE International Conference on Fuzzy Systems. June 2019. DOI: 10.1109/FUZZ-IEEE.2019.8858802
  • Hurt, J. Alex, Scott, Grant J., Anderson, Derek T., Davis, Curt H. "Benchmark Meta-Dataset of High-Resolution Remote Sensing Imagery for Training Robust Deep Learning Models in Machine-Assisted Visual Analytics." 2018 Applied Imagery Pattern Recognition Workshop. October 2018. DOI: 10.1109/AIPR.2018.8707433
  • Nivin, Tyler W., Scott, Grant J., Hurt, J. Alex, Chastain, Raymond L., Davis, Curt H. "Exploring the Effects of Class-Specific Augmentation and Class Coalescence on Deep Neural Network Performance Using a Novel Road Feature Dataset." 2018 Applied Imagery Pattern Recognition Workshop. October 2018. DOI: 10.1109/AIPR.2018.8707406
  • Scott, Grant J., Hurt, J. Alex, Marcum, Richard A., Anderson, Derek T., Davis, Curt H. "Aggregating Deep Convolutional Neural Network Scans of Broad-Area High-Resolution Remote Sensing Imagery." 2018 International Geoscience and Remote Sensing Symposium. July 2018. DOI: 10.1109/IGARSS.2018.8519300
  • Scott, Grant J., Hagan, Kyle C., Marcum, Richard A., Hurt, J. Alex, Anderson, Derek T., Davis, Curt H. "Enhanced Fusion of Deep Neural Networks for Classification of Benchmark High-Resolution Image Datasets." IEEE Transactions on Geoscience and Remote Sensing. June 2018. DOI: 10.1109/LGRS.2018.2839092

Presentations

Research Presentations

Conferences provide opportunities to share research accomplishments, learn about ongoing research, and network with colleagues across academia, industry, and government. I have been fortunate to present my research at conferences around the world.

  • "Hybrid Differential Morphological Profile Enabled Faster R-CNN for Object Detection in High-Resolution Remote Sensing Imagery." at 2023 International Geoscience and Remote Sensing Symposium. July 2023.
  • "Evolutionary Learning of Differential Morphological Profile Structure for Shape Feature Enabled Faster R-CNN" at 2022 IEEE World Congress on Computational Intelligence. July 2022.
  • "Differential Morphological Profile Neural Network" at Department of Electrical Engineering and Computer Science Seminar Series. March 2022.
  • "Improved Classification of High Resolution Remote Sensing Imagery with Differential Morphological Profile Neural Network" at 2021 International Geoscience and Remote Sensing Symposium. July 2021.
  • "Differential Morphological Profile Neural Network for Maneuverability Hazard Detection in UAS Imagery." at SPIE Defense + Commercial Sensing, 2021. April 2021.
  • "Enabling Machine-Assisted Visual Analytics for High-Resolution Remote Sensing Imagery with Enhanced Benchmark Meta-Dataset Training of NAS Neural Networks." at IEEE Big Data 2020. December 2020.
  • "Maneuverability hazard detection and localization in low-altitude UAS imagery." at SPIE Defense + Commercial Sensing, 2020. April 2020.
  • "A Comparison of Deep Learning Vehicle Group Detection in Satellite Imagery." at IEEE Big Data 2019. December 2019.
  • "Comparison of Deep Learning Model Performance Between Meta-Dataset Training Versus Deep Neural Ensembles." at 2019 International Geoscience and Remote Sensing Symposium. July 2019.
  • "Benchmark Meta-Dataset of High-Resolution Remote Sensing Imagery for Training Robust Deep Learning Models in Machine-Assisted Visual Analytics" at 2018 Applied Imagery Pattern Recognition Workshop. October 2018.

Technical Presentations

As part of my role in accelerating data-intensive workflows, I have also led tutorials and workshops showing researchers and data scientists how to scale their workflows using tools like Docker and Kubernetes

  • "Short Course: Accelerating Data Science Workflows with Kubernetes" at 2025 Symposium on Data Science and Statistics. April 2025.
  • "Short Course: Building Containerized Applications for Data Science" at 2025 Symposium on Data Science and Statistics. April 2025.
  • "Building User Bases: Examples from CC* Awardees" at The Sixth Annual National Research Platform Workshop (6NRP). January 2025.
  • "Tutorial: Expanding AI/ML Coursework on Your Campus with Jupyter Notebooks Powered by NRP" at The Sixth Annual National Research Platform Workshop (6NRP). January 2025.
  • "Computer Vision and Automation on the National Research Platform" at Guest Lecturer: CMP SC 8275. April 2024.
  • "The Great Plains Network and GP-ENGINE" at The Fifth Annual National Research Platform Workshop (5NRP). February 2024
  • "With Big Data Comes Big Compute: Scaling Machine Learning onto Public and Commercial Clouds with Kubernetes." at 2023 IEEE International Conference on Big Data. December 2023.
  • "Introduction to Kubernetes" at Great Plains Network Annual Meeting. June 2023.
  • "Migrating Deep Learning Research to NRP" at Great Plains Network Annual Meeting. June 2023.
  • "Software Containerization with Docker" at 2023 Data Science and Analytics Executive Week. March 2023.
  • "Kubernetes Workshop: Utilizing the NRP Nautilus HyperCluster" at 2023 MOREnet Technical Summit. February 2023.
  • "Accelerating Deep Learning Research with the NSF NRP Nautilus HyperCluster" at The International Conference for High Performance Computing, Networking, Storage, and Analysis. November 2022.
  • "Scaling Research with the NSF Nautilus HyperCluster: A Tutorial and Case Study" at Department of Electrical Engineering and Computer Science Seminar Series. November 2022.

Contact

Have additional questions about my resume? Want to collaborate? Following up from a conference? Feel free to reach out to me at my email below!

Location:

Columbia, Missouri or Remote