Islam Akef Ebeid, Ph.D.
Assistant Professor of Computer & Information Science | NSF Principal Investigator | AI in Life Sciences Researcher | National Security Advocate | Writer | Educator | Engineer
I am dedicated to advancing leadership in critical and emerging technologies. My work at the intersection of AI and the Life Sciences addresses high-priority goals in public health, computational biosecurity, and biomedical foundation models. By developing transparent, interpretable, and ethical AI frameworks, I provide the foundational tools necessary to collaborate and lead in global biotechnology. My commitment to academic excellence and inclusive mentorship ensures that the next generation of scientists is equipped to solve the most complex computational challenges of the 21st century.
Islam Akef Ebeid, Ph.D.
Ph.D. Computer & Information Science
Faculty Candidate Job Talk â— School of ITCS, Nile University
Interactive Reveal.js presentation covering academic background, teaching portfolio, research trajectory, and future vision.
Academic Background & Vision
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 doctoral and postdoctoral 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 industry experience at global technology leaders 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 models) with modern architectures like Graph Neural Networks and Transformers to ensure both high accuracy and physical 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 community-building initiatives and public discourse on national, international, and cyber security. In addition, I 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, or visiting historical museums.
Core Competencies
News & Announcements
Invited faculty interview and presentation for the School of ITCS at Nile University.
Published new paper in Frontiers in Bioinformatics on residue transition graphs for protein interaction prediction.
Awarded $210K / $600K collaborative National Science Foundation (NSF) Grant (CISE/CCF) as Principal Investigator.
Teaching Foundations of Data Science and Fundamentals of Informatics at Texas Woman's University.
Historical Milestones Archive (2016 - 2017)
January 2017: Travel to Miami for family engagements.
December 2016: Complete revamping and redesign of personal academic website.
November 2016: Participated in the Supercomputing 2016 (SC16) conference.
October 2016: Research on multi-projector auto-calibration algorithms.
Current Research & Flagship Projects
ProtGram-DirectGCN
A novel method for protein-protein interaction (PPI) prediction using directed graph convolutional neural networks. This work infers global dense residue transition graphs directly from primary sequence tokens, bypassing computationally heavy 3D folding while achieving state-of-the-art predictive performance.
Multimodal Biomedical AI
Investigating data quality, parameter scaling, and interpretability in biological foundation models. Integrating neuro-symbolic reasoning to resolve exponential parameter explosion while infusing physical chemical priors into biomedical discovery workflows.
Academic & Professional Experience
Assistant Professor of Computer Science
Texas Woman’s University, Denton, TX
Director of Data2AI4LifeSciences Research Lab; PI on active NSF collaborative award ($600K total, $210K TWU).
Assistant Professor of Computer Science
Southern Arkansas University, Magnolia, AR
Postdoctoral Research Associate
University of North Texas, Denton, TX
Research Assistant & Ph.D. Candidate
University of Arkansas at Little Rock, Little Rock, AR
Teaching Assistant
The University of Texas at Austin, Austin, TX
Research Assistant
The University of Texas at Austin, Austin, TX
Research Assistant
Texas Advanced Computing Center (TACC), UT Austin, Austin, TX
Research Fellow
The University of Texas at Austin, Austin, TX
Research Assistant
University of Arkansas at Little Rock, Little Rock, AR
Graduate Assistant
Arkansas Tech University, Russellville, AR
Software Engineering & Research Practice
AbbVie, Intel, Arkansas.gov, Orange Telecom, Vodafone, Giza Systems
View Industry & Business PortfolioSelected Publications
For a complete bibliographic record, visit my profiles on Google Scholar, ResearchGate, and DBLP.
Peer-Reviewed 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). Frontiers in Bioinformatics, 5:1651623.
MedGraph: A semantic biomedical information retrieval framework using knowledge graph embedding for PubMed
Ebeid, I. A. (2022). Frontiers in Big Data, 5:1028994.
Peer-Reviewed Conference Proceedings
Adapting a Segmentation Foundation Model for Medical Image Classification
Gu, P., Tang, H., Ebeid, I. A., et al. (2025). IEEE International Symposium on Computer-Based Medical Systems (CBMS 2025).
Graph-based hierarchical record clustering for unsupervised entity resolution
Ebeid, I. A., Talburt, J. R., & Siddique, M. A. S. (2022). Information Technology-New Generations (ITNG 2022).
Get In Touch
I am always open to discussing research collaborations, student mentorship, academic initiatives, and consulting engagements.
Email Correspondence
Office & Telephone
Cell: 512-921-1311 | Office: 940-898-2165
Texas Woman’s University | MCL 412 | Denton, TX 76204
