Assistant Professor of Computer & Information Science | AI in Life Sciences Researcher | Educator | Writer | Engineer
I am a Computer, Data and Information Scientist and an Educator serving currently as an Assistant Professor at Texas Woman's University (TWU) and the Principal Investigator on an active $600K collaborative National Science Foundation (NSF) grant. My graduate studies spanned 8 years, including a brief leave of absence, at the University of Texas at Austin (UT Austin) and the University of Arkansas at Little Rock (UA Little Rock), where I earned a Ph.D. in Computer and Information Science in 2022. Additionally, I have gained professional research experience at major organizations, including Intel and AbbVie.
My research focuses on the critical roles of data quality and data engineering in AI, especially the quality of training data in foundation models. In addition, I am interested in the application of AI in the life sciences, especially data-driven proteomics for drug discovery, literature mining with knowledge graphs, cognitive psychology in health information seeking, and knowledge extraction from medical images. I develop algorithms rooted in statistical approaches (Bayesian, graphical, and Markovian models) and modern computational methods (graph theory, representation learning, and foundation models).
My vision is to accelerate and automate scientific discovery in the life sciences by developing human-centered, sustainable, and ethical AI systems that efficiently integrate, clean, and mine complex scientific data. My mission is to mentor students into independent researchers through a guild-based, inclusive, rigorous, and hands-on approach, and to empower underrepresented groups in the computational, data, and information sciences.
My teaching philosophy centers on building strong foundations in data science and AI through rigorous training in mathematics, statistics, and programming, alongside a deep understanding of algorithms and data structures.
Beyond the lab, I am committed to civic and spiritual engagement. I actively serve the scientific community through peer reviewing and editorial roles, while dedicating time to philanthropic and faith-based organizations. I also contribute to public discourse on societal issues and provide technical consulting on end-to-end software engineering and data science lifecycle management.
On a personal level, I find joy in hiking, visiting libraries and museums, the ritual of a morning coffee, and building the foundation for my future children.
Core Skills
News
October 2024: Currently teaching Foundations of Data Science and Fundamentals of Informatics.
August 2025: Awarded $210K/$600K NSF Grant (CISE/CCF) as Principal Investigator.
October 2025: Published new paper in Frontiers in Bioinformatics on a novel approach to modeling tokens in protein sequences.
Current Projects
ProtGram-DirectGCN
A novel method for protein-protein interaction prediction using graph convolutional neural networks. This work leverages the primary structure of proteins to infer global dense residue transition graphs, enabling more accurate predictions.
Experience
2023 - Current
Assistant Professor of Computer Science
Texas Woman’s University, Denton, TX
2023
Assistant Professor of Computer Science
Southern Arkansas University, Magnolia, AR
2022 - 2023
Postdoctoral Research Associate
University of North Texas, Denton, TX
2021 - 2022
Research Assistant
University of Arkansas at Little Rock, Little Rock, AR
2019 - 2020
Teaching Assistant
The University of Texas at Austin, Austin, TX
2018 - 2019
Research Assistant
The University of Texas at Austin, Austin, TX
2018
Research Assistant
Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX
2017 - 2018
Research Fellow
The University of Texas at Austin, Austin, TX
2014 - 2017
Research Assistant
University of Arkansas at Little Rock, Little Rock, AR
2011 - 2013
Graduate Assistant
Arkansas Tech University, Russellville, AR
Previous Professional Roles
Software Engineering & Research Internships (2008 - 2020)
AbbVie, Intel, Arkansas.gov, Orange Telecom, Vodafone, Giza Systems
Publications
For a complete list, please visit my profiles on Google Scholar and ResearchGate.
Journal Articles
Inferred global dense residue transition graphs from primary structure sequences enable protein interaction prediction via directed graph convolutional neural networks
Ebeid IA, Tang H, and Gu P (2025). Front. Bioinform. 5:1651623.
View PaperMedGraph: A semantic biomedical information retrieval framework using knowledge graph embedding for PubMed
Ebeid, I. A. (2022). Frontiers in Big Data, 5.
View PaperConference Papers
Adapting a Segmentation Foundation Model for Medical Image Classification
Gu, P., Tang, H., Ebeid, I. A., et al. (2025). IEEE CBMS 2025.
Graph-based hierarchical record clustering for unsupervised entity resolution
Ebeid, I. A., Talburt, J. R., & Siddique, M. A. S. (2022). ITNG 2022.
View PaperGet In Touch
I'm always open to discussing new research, projects, and collaboration opportunities. Feel free to reach out.
C: 512 921 1311 | O: 940 898 2165
Texas Woman’s University | MCL 412 | Denton, TX 76204
