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 in critical Data Scientist and ML Engineer roles
- Led HPC and ML projects funded by NSF and DoD in excess of $1.4 million
- Strong expertise in architecture, design, development, of ML workflows
- Experience in scaling and automating data pipelines for ML applications using tools such as Docker, Kubernetes, SLURM, AWS, and GCP
Resume
Summary
J. Alex Hurt, PhD
Highly accomplished Data Scientist and AI expert with 8+ years of experience and a strong record of data-driven innovation. With 35+ publications in Machine Learning, HPC, and AI, I possess a proven ability to architect, develop, and deliver cutting-edge Data Science, ML, and HPC solutions to extract maximum value from large-scale, real-world datasets and drive actionable insights. With deep expertise in Data Science, AI/ML, Deep Learning, and High-Performance Computing, I am adept at leading complex data challenges and high-impact initiatives and delivering cutting-edge data solutions for real-world problems.
- Location: Columbia, Missouri or Remote
- Email: jalexhurt.phd@gmail.com
Experience
Principal Data Scientist
2025-Present
Remote
- Responsible for developing and implementing data-driven solutions across various business functions and collaborating with cross-functional teams to analyze complex data sets, derive actionable insights, and drive strategic decision-making.
- Duties including data exploration, enriching data to enhance suitability for AI/ML, and applying statistical and machine learning techniques to analyze large datasets and identify patterns using predictive models and deep learning algorithms.
Assistant Research Professor
2022-2025
University of Missouri, Columbia, Missouri
- Led HPC and ML Projects PI for NSF and DoD Funded Research Efforts in excess of $1.4 million in the ML and HPC domain focused on scaling data-intensive workloads and applying ML algorithms to real-world data.
- Technical Expertise Executed critical roles on over a dozen DoD AI/ML projects, including as Lead Data Architect and Technical Point of Contact for delivery of applied ML algorithms.
- Data Pipelines for ML Designed, developed, and maintained custom data pipelines and ML libraries used to perform large-scale experimentation of Computer Vision tasks using novel deep learning architectures.
- Automation and Scale Built frameworks to scale and automate ML and other data-intensive workflows using Docker and Kubernetes that are currently utilized by ML researchers around the country.
- Data Science Instructor Designed and built hands-on curriculum and taught graduate-level courses in Data Science and ML topics including Cloud Computing, Data Visualization, Supervised Learning, and Computer Vision.
- Mentor Mentored PhD Candidates in Computer Science, providing guidance and imparting practical knowledge and skills at a variety of technical levels.
Graduate Research Assistant
2018-2022
University of Missouri, Columbia, Missouri
- Performed and analyzed suites of experiments for ML applications on Satellite Imagery using frameworks such as PyTorch, Keras, and Tensorflow.
- Developed pipelines for Deep Learning experimentation in Applied ML projects.
- Worked as part of a small team to design, implement, and maintain a custom PostgreSQL Database to enable the creation of datasets used for Machine Learning Applications.
Graduate Teaching Assistant
2018-2022
MU Data Science and Analytics, Columbia, Missouri
- Developed hands-on course content for data-intensive courses such as Applied ML, Computer Vision, Advanced Database Analytics, and Advanced Statistics, and Advanced Data Analytics.
- Assisted instructors in delivering course content, and assisted students via office hours and one-on-one meetings.
Database Administrator and Webmaster
2016-2017
University of Missouri, Columbia, Missouri
- Designed, implemented, and maintained a full MySQL database and web application with no technical assistance for Plant Science Research Laboratory.
- 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
Key Skills
Programming
Machine Learning
Technologies
Publications
14
First Author Publications
39
Total Publications
8
Years of Publications
Selected Recent Publications
- Ouadou, Anes, Alshehri, Mariam, Hurt, J. Alex "SegConFormer: A New Deep Learning Architecture for Semantic Segmentation of Burned Areas in Satellite Images" IEEE Transactions on Geoscience and Remote Sensing (TGRS). Under Review.
- Cheng, Keli, Hurt, J. Alex, Mzurikwao, Deogratias, Kabona, George Richard, Kasubi, Mabula, Mhiche, Ambakisye, Oswald, Will, Scott, Grant "Using Geospatial AI for Large-Scale Automated Boma Mapping with Planet Earth Imagery" International Geoscience and Remote Sensing Symposium (IGARSS). Under Review.
-
Huangal, David, Hurt, J. Alex "Differential Morphological Profile Neural Networks for Semantic Segmentation" arXiv preprint arXiv:2509.04268. 2025.
DOI: 10.48550/arXiv.2509.04268 -
Hurt, J. Alex, Bajkowski, Trevor M., Scott, Grant J., Davis, Curt H. "Evaluation and Analysis of Deep Neural Transformers and Convolutional Neural Networks on Modern Remote Sensing Datasets" arXiv preprint arXiv:2508.02871. 2025.
DOI: 10.48550/arXiv.2508.02871 -
Ouadou, Anes, Huangal, David, Alshehri, Mariam, Scott, Grant, Hurt, J. Alex "Semantic Segmentation of Burned Areas in Sentinel-2 Satellite Imagery Using Deep Learning Transformer and Convolutional Attention Networks" IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2025.
DOI: 10.1109/JSTARS.2025.3584877
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