Jason L Causey Curriculum Vitae

Department of Computer Science,
Arkansas State University,
PO Box 9,
State University, AR 72467

Phone: 870-972-3978
Email: jcausey@astate.edu
Office: Arkansas Biosciences Institute, 206

Professional Preparation:

Jan. 2016 - Dec. 2017 — University of Arkansas at Little Rock — Little Rock, AR
PhD, Bioinformatics Advisor: Xiuzhen Huang, PhD
Dissertation: “Studying Low Complexity Structures In Bioinformatics Data Analysis Of Biological And Biomedical Data.”

Aug. 2001 - May 2003 — Arkansas State University — Jonesboro, AR
MS, Computer Science Graduate GPA: 4.0

Aug. 1998 - May 2001 — Arkansas State University — Jonesboro, AR
BS, Computer Science Graduated Cum Laude

Appointments:

Jul. 2023 - Present — Arkansas State University — Jonesboro, AR
Associate Professor, Dept. of Computer Science
Teaching interests: Core programming curriculum, data science, machine learning and cross-disciplinary ML applications. Research interests: Machine learning, trusted/explainable ML and artificial intelligence, with applications in areas such as Biomedical, Agricultural, and Industrial sectors.

Aug. 2018 - Jun. 2023 — Arkansas State University — Jonesboro, AR
Assistant Professor, Dept. of Computer Science

Jan. 2019 - Jun. 2024 — Arkansas State University — Jonesboro, AR
Associate Director, A-State / St. Bernards Translational Research Lab.

Jul. 2020 - Jun. 2024 — Arkansas State University — Jonesboro, AR
Associate Director, Center for No-Boundary Thinking (CNBT)

Jul. 2020 - Jun. 2024 — Arkansas State University — Jonesboro, AR
Division Lead, CNBT Division of Algorithms and Computational Methodology

Jan. 2021 - May 2022 — Arkansas State University — Jonesboro, AR
Interim Director, Data Science and Data Analytics Program

Aug. 2003 - Aug. 2018 — Arkansas State University — Jonesboro, AR
Instructor in Computer Science
Teaching primarily in the core programming competency area (including Structured Programming, Object-Oriented Programming, Data Structures, Operating Systems), including curriculum updates and preparation for each of these courses. Co-developed the Accelerated Programming remediation series (graduate level). Developed a new course: Web Application Development, which is now in regular rotation.

Aug. 2002 - May 2003 — Arkansas State University — Jonesboro, AR
Graduate Teaching Assistant (Computer Science)

Aug. 2001 - May 2002 — Arkansas State University — Jonesboro, AR
Graduate Teaching Assistant (Math)

Service and Synergistic Activities:

Served as Highlights Chair on the organizing committee for the 13^th^ ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB). (2022)

Serves as Lab Manager of the Bioinformatics Lab at Arkansas Biosciences Institute (ABI) at Arkansas State University. (2017 - present)

Creator and host of a weekly presentation and discussion forum “Code Friday” where students meet to present personal projects and discuss computer science and general technological topics in a positive, encouraging environment. (2014 - present)

Faculty Sponsor, Association for Computing Machinery student organization, Arkansas State University chapter. (2016 - present)

College of Engineering & Computer Science Curriculum Committee. (2019 - present)

Co-develops and instructs Graduate Assistant training workshops. (2016 - present)

Grants:

2022 - 2024 “LP: MoDaCoM-TL: Model and Data Compatibility Metric for Transfer Learning”, Data Analytics that are Robust and Trusted (DART), NSF EPSCOR, #23-EPS4-0030

2018 - 2020 “Develop Novel Informatics Algorithms for Lung Cancer Early Screening with CT Scans”, Arkansas Biosciences Institute, #200144

2021 “ABI Summer Internship (mentor)”, Arkansas Biosciences Institute

Publications:

Jacob D Washburn, José Ignacio Varela, Alencar Xavier, Qiuyue Chen, David Ertl, Joseph L Gage, James B Holland, Dayane Cristina Lima, Maria Cinta Romay, Marco Lopez-Cruz, Gustavo Campos, Wesley Barber, Cristiano Zimmer, Ignacio Trucillo Silva, Fabiani Rocha, Renaud Rincent, Baber Ali, Haixiao Hu, Daniel E Runcie, Kirill Gusev, Andrei Slabodkin, Phillip Bax, Julie Aubert, Hugo Gangloff, Tristan Mary-Huard, Theodore Vanrenterghem, Carles Quesada-Traver, Steven Yates, Daniel Ariza-Suárez, Argeo Ulrich, Michele Wyler, Daniel R Kick, Emily S Bellis, Jason L Causey, Emilio Soriano Chavez, Yixing Wang, Ved Piyush, Gayara D Fernando, Robert K Hu, Rachit Kumar, Annan J Timon, Rasika Venkatesh, Kenia Segura Abá, Huan Chen, Thilanka Ranaweera, Shin-Han Shiu, Peiran Wang, Max J Gordon, B K Amos, Sebastiano Busato, Daniel Perondi, Abhishek Gogna, Dennis Psaroudakis, C P James Chen, Hawlader A Al-Mamun, Monica F Danilevicz, Shriprabha R Upadhyaya, David Edwards, Natalia Leon, “Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates” Genetics, iyae195 (2024).
DOI: 10.1093/genetics/iyae195 URL: https://doi.org/10.1093/genetics/iyae195

Fared Farag, Trevis D. Huggins, Jeremy D. Edwards, Anna M. McClung, Ahmed A. Hashem, Jason L. Causey, Emily S. Bellis, “Manifold and spatiotemporal learning on multispectral unoccupied aerial system imagery for phenotype prediction” The Plant Phenome Journal, 7 e70006 (2024).
DOI: 10.1002/ppj2.70006 URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/ppj2.70006

Lake Noel*, Shelby Chun Fat*, Jason L. Causey*, Wei Dong, Jonathan Stubblefield, Kathryn Szymanski, Jui-Hsuan Chang, Paul Zhiping Wang, Jason H. Moore, Edward Ray, Xiuzhen Huang, “Sex classification of 3D skull images using deep neural networks” Scientific Reports, 14 13707 (2024).
DOI: 10.1038/s41598-024-61879-6 URL: https://doi.org/10.1038/s41598-024-61879-6
* Equal contribution.

Jason Causey*, Jonathan Stubblefield*, Jake Qualls*, Jennifer Fowler, Lingrui Cai, Karl Walker, Yuanfang Guan, Xiuzhen Huang, “An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19 1387-1392 (2022).
DOI: 10.1109/TCBB.2021.3085608
* Co-First authors.

Emily S. Bellis, Ahmed A. Hashem, Jason L. Causey, Benjamin R. K. Runkle, Beatriz Moreno-García, Brayden W. Burns, V. Steven Green, Timothy N. Burcham, Michele L. Reba, Xiuzhen Huang, “Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning” Frontiers in Plant Science, 13 (2022).
DOI: 10.3389/fpls.2022.716506

Jennifer Fowler, Jonathan Stubblefield, Jason Causey, Jake Qualls, Wei Dong, Hongmei Jiang, Karl Walker, Yuanfang Guan, Xiuzhen Huang, “Identify differentially expressed genes with large background samples” International Journal of Computational Biology and Drug Design, 14 411-428 (2021).
DOI: 10.1504/IJCBDD.2021.121615

Yao Yan, Thomas Schaffter, Timothy Bergquist, Thomas Yu, Justin Prosser, Zafer Aydin, Amhar Jabeer, Ivan Brugere, Jifan Gao, Guanhua Chen, Jason Causey, Yuxin Yao, Kevin Bryson, Dustin R. Long, Jeffrey G. Jarvik, Christoph I. Lee, Adam Wilcox, Justin Guinney, Sean Mooney, , “A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization” JAMA Network Open, 4 e2124946 (2021).
DOI: 10.1001/jamanetworkopen.2021.24946

Jonathan Stubblefield*, Mitchell Hervert*, Jason L. Causey*, Jake A. Qualls*, Wei Dong, Lingrui Cai, Jennifer Fowler, Emily Bellis, Karl Walker, Jason H. Moore, Sara Nehring, Xiuzhen Huang, “Transfer learning with chest X-rays for ER patient classification” Scientific Reports, 10 20900 (2020).
DOI: 10.1038/s41598-020-78060-4
* Co-First authors.

Jason Causey, Keyu Li, Xianghao Chen, Wei Dong, Karl Walker, Jake Qualls, Jonathan Stubblefield, Jason H. Moore, Yuanfeng Guan, Xiuzhen Huang, “Spatial Pyramid Pooling with 3D Convolution Improves Lung Cancer Detection” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1-1 (2020).
DOI: 10.1109/TCBB.2020.3027744

Jason L Causey, Jonathan Stubblefield, Tomonori Yoshino, Alejandro Torrico, Jake A Qualls, Xiuzhen Huang, “Arkansas AI-Campus Method for the 2019 Kidney Tumor Segmentation Challenge” Kidney and Kidney Tumor Segmentation Challenge (KiTS19) (2019).

Wei Zhou, Emily S. Bellis, Jonathan Stubblefield, Jason Causey, Jake Qualls, Karl Walker, Xiuzhen Huang, “Minor QTLs mining through the combination of GWAS and machine learning feature selection” bioRxiv, 712190 (2019).
DOI: 10.1101/712190

Jason Causey, Jake Qualls, Jason H. Moore, Fred Prior, Xiuzhen Huang, “CNNcon: A Quantitative Imaging Tool for Lung CT Image Feature Analysis” bioRxiv, 615492 (2019).
DOI: 10.1101/615492

Jason L. Causey, Cody Ashby, Karl Walker, Zhiping Paul Wang, Mary Yang, Yuanfang Guan, Jason. H. Moore, Xiuzhen. Huang, “DNAp: A Pipeline for DNA-seq Data Analysis” Scientific Reports — Nature Publishing Group, 8 6793 (2018).
DOI: 10.1038/s41598-018-25022-6

Jason L. Causey*, Junyu Zhang*, Shiqian Ma, Bo Jiang, Jake A. Qualls, David G. Politte, Fred Prior, Shuzhong Zhang, Xiuzhen Huang, “Highly accurate model for prediction of lung nodule malignancy with CT scans” Scientific Reports — Nature Publishing Group, 8 9286 (2018).
DOI: 10.1038/s41598-018-27569-w
* Co-First authors.

Bo Jiang*, Shiqian Ma*, Jason Causey*, Linbo Qiao, Matthew P. Hardin, Ian Bitts, Daniel Johnson, Shuzhong Zhang, Xiuzhen Huang, “SparRec: An effective matrix completion framework of missing data imputation for GWAS.” Scientific Reports — Nature Publishing Group, 6 35534 (2016).
DOI: 10.1038/srep35534 URL: https://www.nature.com/articles/srep35534
* Co-First authors.

Tabetha Osborn, Sindhu Kaimal, Jason Causey, William Burns, Scott Reeve, “Optical detection of explosives: spectral signatures for the explosive bouquet” Proceedings of SPIE, 7304 730419-730419-8 (2009).
DOI: 10.1117/12.818794

Posters and Presentations:

Jason L Causey, “Insights from Building EHR-based Predictive Models Without Accessing Patient Data” Presentation at 2022 Arkansas Biosciences Institute (ABI) Annual Research Symposium, Fayetteville, AR. (2022).

Jason L Causey, Jennifer Fowler, Jonathan Stubblefield, Emilio Soriano Chavez, “Workshop: AI Campus & No-Boundary Thinking: A Grassroots Model for Training the New Generation of Scientists” Workshop at ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB), Chicago, IL. (2022). URL: https://acm-bcb.org/2022/index.php

Jason L Causey, “Introducing the NBT Division for Algorithms and Computational Methodology” Presentation at No-Boundary Thinking Conference, Trinity University, San Antonio, TX. (2019).

John Clay Heern*, Jason L Causey, Jake A Qualls, Xiuzhen Huang, “Study the co-occurrence patterns of Metagenomics high-throughput sequencing data” Poster at NSF Research Experiences for Undergraduates (REU)Symposium, NSF, Alexandria, VA. (2018).
* Presentor.

Jason L Causey*, Xiuzhen Huang, “EAGER: New Approach: Early Diagnosis of Alzheimer’s Disease Based on Magnetic Resonance Imaging (MRI) via High-Dimensional Image Feature Identification” Presentation at NSF Smart and Connected Healthcare (SCH) PI Meeting, NSF. Alexandria, VA. (2018).
* Presentor.

Jason L. Causey, “Highly accurate model for prediction of lung nodule malignancy with CT scans” Poster at NCI QIN Meeting, NIH. (2017).

Other Research Activities:

2018-ongoing — Arkansas State University and St. Bernard's Translational Research Group
Arkansas State University
Research in the area of biomedical informatics with a focus on improving diagnosis, treatment, and patient care.

2010-2012 — SHADES Research group
Arkansas State University
Researching machine learning techniques, data visualization, interfaces,` and high-performance computing for the SHADES project.

2008-2009 — Laser Spectroscopy Group
Arkansas State University
Developed software used for calibrating spectra generated by high-resolution infrared laser spectrometry.

Research Interests:

Biomedical Imaging Analysis: I have published techniques for leveraging advanced machine learning and "deep learning" algorithms for extracting diagnostically relevant features from biomedical images and developing models capable of incorporating features derived from images with genomic, clinical, or demographic information. My participation in the 2016 Data Science Bowl (a lung cancer detection challenge) fostered new collaborations. I lead a team in the 2019 Kidney Tumor Segmentation Grand Challenge, where we placed 50th of over 800 participating teams.

Machine Learning on Genomic and Gene Expression Datasets: I participated in the 2017 Multiple Myeloma DREAM Challenge; our team placed 3rd place in final rankings out of 61 teams. The models and techniques we developed are now being used in ongoing research, including a collaboration with UAMS.

Multi-Modal Biomedical Data Analysis: I am currently working with researchers from St. Bernards Medical Center to combine traditional clinical information with features extracted from medical images (chest X-ray) for patient stratification in an emergency room setting. In addition, I am part of an ongoing project with UAMS where we are combining genomic data with cutting edge walking tests to better understand and classify outcomes in Parkinsons Disease patients.

Enhancing Reproducibility of Research: After developing a complex research software pipeline for a DNA sequencing project, I became interested in improving the tools available for researchers to collaborate, share, and reproduce bioinformatics and biomedical research. This experience resulted in a published paper detailing the "DNAp" pipeline as implemented in a Docker container. My work on both tool containerization and dataset versioning and distribution technologies continues, and I look forward to publishing additional contributions in the area soon.