Faculty & AI Research Profile

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

Islam Akef Ebeid, Ph.D.

Ph.D. Computer & Information Science

Featured Presentation

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

Python  C / C++  Unix / Linux  Java  SQL  Graph Theory  Linear Algebra  Natural Language Processing  Deep Learning  Machine Learning  TensorFlow  Scikit-Learn  HPC / Supercomputing  OpenCV  OpenGL  WebGL 

News & Announcements

October 2026

Invited faculty interview and presentation for the School of ITCS at Nile University.

October 2025

Published new paper in Frontiers in Bioinformatics on residue transition graphs for protein interaction prediction.

August 2025

Awarded $210K / $600K collaborative National Science Foundation (NSF) Grant (CISE/CCF) as Principal Investigator.

October 2024

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

Proteomics & GCN

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.

Neuro-Symbolic AI

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

2023 - Present

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).

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 & Ph.D. Candidate

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 (TACC), UT 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

2008 - 2020

Software Engineering & Research Practice

AbbVie, Intel, Arkansas.gov, Orange Telecom, Vodafone, Giza Systems

View Industry & Business Portfolio

Selected 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

iebeid@twu.edu (Institutional)

iaebeid@utexas.edu (Alumni / UT Austin)

iaebeid@gmail.com (Personal)

Office & Telephone

Cell: 512-921-1311 | Office: 940-898-2165

Texas Woman’s University | MCL 412 | Denton, TX 76204