CV

Research interests

Privacy-preserving machine learning, federated learning, and LLMs; secure collaborative learning; trustworthy AI; applications in healthcare and finance.

Education

• Ph.D. in Computer Science, Washington State University, USA, 2025–present
  Advisor: Prof. Mohamed Elmahallawy
• M.Sc. in Information Systems, Menoufia University, Egypt, 2022
• B.Sc. in Information Systems (Honors, Rank 1), Menoufia University, Egypt, 2014

Publications

SecureGate: Learning When to Reveal PII Safely via Token-Gated Dual-Adapters for Federated LLMs

ACL 2026 — arXiv

Awards

• Best Graduate Research Project Award, Showcase of Research and Creative Activities, Washington State University, April 2025

Research experience

• Research Assistant, Washington State University, 2025–present
  • Conduct research in machine learning with a focus on large language models, privacy, and security.
  • Review literature to identify research gaps, risks, and directions.
  • Develop research ideas, experimental pipelines, and technical foundations through coursework and projects.

Professional experience

• Backend Team Lead, Leannode, 2022–2024
  • Led backend engineering; oversaw architecture and code quality.
  • Designed and maintained scalable systems for high-traffic applications.
  • Improved workflows, CI/CD, and system performance.
• Senior Software Engineer, Leannode, 2019–2022
  • Built and maintained scalable backend systems.
  • Collaborated on system design and implementation with stakeholders.

Mentoring

• Graduate Research Mentor, CySER Program (DoD VICEROY Initiative), Spring 2025
  • Mentored undergraduates on security and trustworthiness of ML systems; guided federally funded cybersecurity research projects.
• Undergraduate Research Mentor, Chancellor’s Summer Scholar Program (WSU), Summer 2025
  • Supervised a project from planning through final presentation; supported methodology and communication of results.
• Udacity Nanodegree Mentor, 2017–2018
  • Mentored students in Android development; one-on-one feedback and technical support.

Selected project

SecureGate (federated LLM privacy framework)

• Dual-adapter architecture separating global and sensitive knowledge.
• Token-level gating for privacy-aware inference.
• Strong privacy–utility trade-offs with low overhead.

Technical skills

• ML & AI: Machine learning, deep learning, LLMs, federated learning, cybersecurity
• Frameworks: PyTorch, scikit-learn, NumPy, Pandas
• LLM tooling: Hugging Face Transformers, OpenAI API, prompt engineering
• Languages: Python, C++
• Systems: Distributed training, Docker, Kubernetes, AWS, GCP
• Data: PostgreSQL, MongoDB, Redis
• Tools: Git, GitHub

Certifications

• Google Associate Cloud Engineer, 2024 — credential