Research & Funding Strength

• Active NSF PI (2025–2028): Multimodal medical image segmentation with domain ontologies.
• Lab Director: Founded Data2AI4LifeSciences Lab with $66K startup & HPC grants.
• High Impact: Publications in Nature, Frontiers, and IEEE.

Academic Background & Heritage

• Egyptian & US Foundation: BSc from Ain Shams; MSc & PhD from US programs (UA Little Rock, UT Austin).
• US Faculty Leadership: Assistant Professor at TWU and SAU; NIH Postdoc at UNT.
• Industry R&D: Software engineering and data science roles at Intel, AbbVie, and Arkansas.gov.

Academic Background

Education, training trajectory from Cairo to leading US institutions, and industry engineering roles.

Research & Grants

Data quality in AI, biomedical knowledge graphs, protein GNNs, and active $600K NSF award.

Personal Frontier Project

Taming modern AI parameter explosion via Neuro-Symbolic & Graph-Based architectures.

Teaching Portfolio

Pedagogy (live coding, research synergy), course history, and curricular offerings for ITCS.

Faculty Service & Advising

Curriculum governance, student recruitment coordination, student capstones, and peer review.

Vision for Nile University

Lab setup at NU, securing Science, Technology & Innovation Funding Authority (STDF) & Academy of Scientific Research and Technology (ASRT) funding, and advancing Egypt's Vision 2030.

Degrees & Certifications

Professional Certifications: FE (Fundamentals of Engineering) | ITIL

Academic Appointments & Industry

1. Data Quality in AI

• Entity Resolution (ER): Linking non-unique records across disparate sources.
• Hierarchical record clustering & modularity optimization.
• Automated data quality assessment & observability pipelines.

2. AI in Life Sciences

• Knowledge Graphs & GNNs: Harmonizing multi-omics with PubMed literature.
• Computational drug discovery & automated hypothesis generation.
• Directed Graph CNNs for protein-protein interaction prediction.

3. Human Factors in AI

• Cognitive Modeling: Eye tracking, scanpaths & operator attention modeling.
• Bridging machine logic with human expert cognition.
• Ethical, sustainable, and transparent AI system design.

The Challenge & Innovation

• Biomedical Fragmentation: 35M+ PubMed papers lack unified semantic linkage between authors, clinical entities, and grants.
• PubMed Knowledge Graph (PKG): Extracted entities using fine-tuned BERT; linked authors and NIH funding at massive scale.
• MedGraph Architecture: Knowledge Graph Embeddings (TransE, RotatE) enabling conceptual search beyond simple keyword matching.

Key Dissemination & Venues

Direct Graph CNN Architecture

• The Challenge: Experimental 3D protein structure determination is prohibitively slow; 1D sequence models miss spatial contact topology.
• Our Method (2025): Developed an algorithm to infer Global Dense Residue Transition Graphs directly from primary amino acid sequences.
• Directed GCNN: Captures directional residue constraints, achieving accurate protein-protein interaction (PPI) prediction without physical 3D coordinates.

Translational & Industry Impact

Grant Scope: Auto-Prompting with Knowledge

• Title: Towards auto-prompt textual annotations generation with domain knowledge for multimodal medical image segmentation.
• Lead PI: Collaborative Award with UT Rio Grande Valley.
• Scientific Goal: Foundation models (e.g. SAM) require tedious manual bounding prompts. We automate prompting using medical ontologies and clinical text knowledge.
• Innovation: Developed Spatially Localized Channel Attention (SLCA) for foundation segmentation backbones.

Milestone & Significance for Nile University

NSF CISE/CCF

$210,000 / $600,000

Award #2523786 (2025–2028). Auto-prompt multimodal segmentation.

TWU Startup Fund

$66,000

Established GPU computing lab, student stipends, and research software stack.

HPC Upgrade Grant

$5,000

High Performance Computing memory expansion for large-scale GNN model training.

Lab Mission & Ecosystem

Accelerating Biomedical Informatics, Literature Mining, Imaging, and Computational Drug Discovery through novel GNNs, knowledge extraction pipelines, and cognitive observability.

Training & Team Culture

Mentoring undergraduate and graduate researchers (e.g., student researchers and faculty collaborators) in production PyTorch, HPC workflows, and academic publishing.

My Independent Research Initiative

(Conducted as personal fundamental research alongside student lab projects)

• Structured Relational Knowledge: Replace memorization of facts in dense weights with sparse, dynamically queried Graph Neural Networks (GNNs).
• Neuro-Symbolic Reasoning: Ground neural representations in formal symbolic logic constraints to eliminate hallucination.
• Drastic Parameter Compression: Target equivalent or superior reasoning fidelity with orders of magnitude fewer parameters.

Long-Term Strategic Impact

• Green & Sustainable AI: Enables high-capability AI on edge and local compute clusters without requiring multi-million-dollar GPU farms.
• Verifiable Reasoning: Logic-guided graph traversals guarantee interpretability and safety in high-stakes domains.
• Intellectual Leadership for Egypt: Establishes a unique, high-impact theoretical and algorithmic contribution originating from Nile University.

1. Live-Coding & Debugging

• Live coding in class to demystify compiler and runtime errors.
• Treat model bugs as diagnostic learning moments.
• Ground abstract algorithms in practical software engineering rigor.

2. Research-Teaching Synergy

• The classroom as an incubator for future researchers.
• Replace toy assignments with real open-source biomedical & graph datasets.
• Expose students to genuine challenges in data quality and scalability.

3. Guild-Based Mentorship

• Paired programming and collaborative code reviews.
• HyFlex (Hybrid-Flexible) design ensuring equity for working/remote students.
• Continuous low-stakes assessments to reinforce mastery and confidence.

Courses Taught as Primary Instructor

• Foundations of Data Science (CSCI 3603 / 5173, TWU)
• Principles of Data Mining (CSCI 4823, TWU)
• Big Data & High-Performance Computing (CSCI 4623, TWU)
• Data Warehousing & Engineering (CSCI 4513 / 5803, TWU)
• Statistical Programming (CSCI 5663, TWU)
• Computer Science II & OOP (CSCI 2113, SAU)
• Database Management Systems (MSIS 5133, SAU)
• Unix Operating Systems (MSIS 5033, SAU)
• Computer Vision & Machine Learning (UT Austin)

Curriculum Design & TA Experience

• Curriculum Developer: Designed CSCI 5773 Artificial Intelligence graduate course syllabus.
• UT Austin (Graduate TA): Artificial Intelligence in Healthcare, Data Semantics, Human-AI Interaction, Virtual Environments.
• UA Little Rock (TA): Information Visualization, Python Programming.
• Arkansas Tech (TA): Computer Networks, Intro to Programming.

Undergraduate Core CS

• Artificial Intelligence
• Machine Learning
• Database Systems
• Data Structures & Algorithms
• Operating Systems / Unix
• Data Warehousing

Graduate Program Core

• Deep Learning & Neural Networks
• Knowledge Graphs & Semantic Web
• Big Data & HPC Systems
• Natural Language Processing
• Distributed AI Systems

Proposed Novel Courses

Supervised Capstone & Thesis Projects (Selected)

Mentorship Model: The Research Guild

• Hands-On Apprenticeship: Undergraduates and graduate students contribute directly to funded lab pipelines.
• Pathway to Publications: Mentoring students to present at regional conferences (MCBIOS, TACCSTER) and co-author papers.
• Production Software Stack: Training in production Python, PyTorch Geometric, Git, and cloud infrastructure.
• Undergraduate Retention: Dedicated to empowering first-generation college students in STEM.

Institutional Governance

Scholarly Service & Peer Review

• Journal Reviewer:
  • Nature Quality & Quantity (2025)
  • Journal of Supercomputing (2025)
  • Cambridge University Press J. of Natural Language Eng.
• Conference Reviewer:
  • ACM SIGKDD, ACM CIKM, IEEE BIBM, ACM ETRA
• Editorial Board: Research in Biomedical Engineering and Technology (2025–Present).
• Professional Memberships: ACM, IEEE, Egyptian Syndicate of Engineers (since 2008).

Establish Lab & Teach

• Launch the Data2AI4LifeSciences Lab at Nile University.
• Seamlessly integrate into ITCS core teaching schedule.
• Recruit Nile University undergraduate and MSc students.
• Publish joint papers on ongoing US NSF collaborative deliverables.

Target STDF & ASRT

• STDF Proposals: Submit health informatics & automated drug hypothesis generation grants.
• ASRT Proposals: Target multi-modal data linkage in national biomedical repositories.
• Collaborate with NU Biotechnology and Engineering faculties.

Industry Partnerships

• Partner with Egyptian pharma and health-tech leaders (data observability).
• Pilot Neuro-Symbolic parameter compression tools.
• Host regional workshops on Knowledge Graphs and Trustworthy AI at Nile University.

What Nile University ITCS Offers Me:

• Innovation-Driven Culture: Nile University's standing as a leading research-intensive, forward-thinking university.
• Exceptional Student Talent: Motivated students eager to tackle advanced AI and data engineering problems.
• National Purpose: The opportunity to bring 10+ years of US research and federal grant experience back home to Egypt.

What I Deliver to Nile University:

• Active Federal Grant PI: Brings active NSF funding experience ($600K) and competitive grant-writing expertise.
• High Quality Publications: Track record in Nature Scientific Data, Frontiers, and IEEE.
• Evaluated Teaching Excellence: Dedicated educator with 4.5+ average ratings across 10+ core CS courses.
• Institutional Builder: Eager to serve in curriculum design, faculty recruitment, and student mentorship.