Assistant Professor of Computer & Information Science | AI in Life Sciences Researcher | Educator | Writer | Engineer
I am a Computer, Data, and Information Scientist and Educator, currently serving 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 academic foundation was built through extensive research at the University of Texas at Austin (UT Austin) and the University of Arkansas at Little Rock (UA Little Rock), where I earned my Ph.D. in Computer and Information Science. My expertise is further anchored by professional positions at global organizations, including Intel and AbbVie.
My vision is to accelerate scientific discovery in the life sciences through the development of human-centered, sustainable, and ethical AI systems. My research group investigates data quality within foundation models, specifically as it applies to proteomics, drug discovery, and biomedical knowledge discovery. Our multimodal neuro-symbolic methodology integrates classical statistical rigor (Bayesian, Markovian) with modern architectures like Graph Neural Networks and Transformers to ensure both performance and interpretability.
My mission is to mentor the next generation of independent researchers through a collaborative, guild-based approach that prioritizes technical excellence and inclusive representation. I am dedicated to empowering underrepresented voices in computational sciences as a core component of my academic leadership.
My career is anchored in service and integrity. Beyond the laboratory, I contribute to public discourse on various topics and provide technical consulting on end-to-end data science lifecycles. I find balance through civic, social and spiritual engagement; whether through volunteer work, Islamic scholarship, hiking, writing, visiting museums, or the quiet ritual of a morning coffee.
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
