I'm a Data Scientist specializing in LLMs working remotely from Virginia, USA.
Data Scientist with expertise in AI/ML, web development, and healthcare technology. Passionate about creating impactful solutions and sharing knowledge through teaching and mentoring.
I specialize in building intelligent systems that solve real-world problems. My experience spans from developing AI-powered applications to conducting research in healthcare technology. I believe in the power of technology to make a positive impact on society.
When I'm not coding, I enjoy teaching and mentoring others, sharing knowledge through workshops and community talks, and staying up-to-date with the latest developments in AI and software engineering.
When it’s time to unwind, you’ll find me enjoying a good podcast, hiking, or trying to master a new skill.
I thrive on bringing clarity to the complex.
My approach is practical, people-focused, and rooted in real-world needs—every solution starts with listening and ends with measurable impact.
Translate Ambiguity Into Impact
I turn complex, unclear requirements into scalable machine learning and LLM solutions. Whether translating pain points into actionable product features or navigating ambiguous clinical and business goals, I focus on delivering clarity and measurable value at every stage.
Stakeholder-Driven AI
I engage directly with clinicians, engineers, designers, and executives to build features that truly matter. By leading co-design sessions and gathering ongoing feedback from stakeholders, I ensure every product decision aligns with real-world needs and maximizes user adoption.
Results at Scale
I deliver production-ready, robust systems—HIPAA-compliant, scalable, and cost-efficient—that securely process large volumes of sensitive data. My experience includes building automated pipelines for data anonymization and healthcare workflow optimization, resulting in high performance and tangible benefits for organizations.
Mentor & MLOps Leader
As a passionate mentor and team leader, I coach engineers, teach best practices, and champion MLOps principles for reproducible, maintainable AI. I enjoy delivering technical workshops, leading teams, and driving adoption of CI/CD, code review, and collaborative development in every project.
ML Models & Expertise
Expert in fine-tuning and deploying large language models like GPT, BERT, and T5 for various NLP tasks.
Building Retrieval-Augmented Generation systems that combine the power of LLMs with knowledge bases for accurate, contextual responses.
Creating and optimizing vector embeddings for semantic search, document similarity, and efficient information retrieval.
Crafting effective prompts and designing robust prompt templates for consistent, high-quality LLM outputs.
Developing comprehensive evaluation frameworks to assess and improve LLM performance across various metrics and use cases.
Working with cutting-edge models that combine text, image, and audio processing for comprehensive AI solutions.
Publications
Generative Modeling of Networked Time-Series via Transformer Architectures
Yusuf Elnady • arXiv preprint, Virginia Tech • 2025
Security and network applications often lack sufficient data to train machine learning models. While Transformers can generate synthetic data, these samples typically underperform compared to real data. We introduce an efficient, generalizable transformer-based model for time-series generation, producing high-quality samples and achieving state-of-the-art results across multiple datasets.
AI in Radiation Oncology Consultation: Boosting Efficiency and Quality in History of Present Illness Documentation
Yusuf Elnady, Christel Smith, Lauren Mancuso, Matthew Terry, Christopher D. Jahraus • The Radiation Oncology Summit ACRO • 2025
An AI-driven workflow for radiation oncology consultation notes demonstrates high accuracy and consistency in automating the HPI section, reducing administrative burden while maintaining documentation quality.
Detecting Credit-Seeking Behavior with Programmed Instruction Framesets in a Formal Languages Course.
Yusuf Elnady, Mohammed Farghally, Mostafa Mohammed, Clifford A. Shaffer • Education Sciences, 15(4), 439. • 2025
A study on detecting credit-seeking behavior in an interactive Formal Languages eTextbook, revealing its correlation with lower student performance and the influence of question type on engagement.
Scalable and Maintainable Distributed Sequence Alignment Using Spark
Karim Youssef, Yusuf Elnady, Eli Tilevich, Wu-chun Feng • IEEE Transactions on Computational Biology and Bioinformatics • 2025
A scalable and maintainable parallel BLAST tool built on Spark, SparkLeBLAST improves performance and simplicity over prior solutions, enabling up to 88.6x faster genomic analysis across large datasets.
Equine Distal Limb Ultrasound Simulator: Safe, High-Fidelity Training for Veterinary Education
Fawzy Elnady, Yusuf Elnady, Lauren Trager Burns • Virginia Tech Intellectual Properties, Inc. • 2024
A novel simulator for veterinary students and professionals, using ethically-sourced, preserved horse limbs embedded with RFID and paired with an AI-driven ultrasound interface. The device enables hands-on practice of ultrasound diagnostics without the risks of live animal use. It provides real-time feedback, supports both normal and pathological imaging, and increases safety, fidelity, and accessibility in equine veterinary training.
Presentations
Unleashing AI in Radiation Oncology
Apr 2025
Introduction to the general concepts in AI and to provide a framework for the potential applications of AI in radiation oncology.
Unleashing the Transformative Power of AI in Radiation Oncology: An AI Primer
Oct 2024
Introduction to the general concepts in AI and to provide a framework for the potential applications of AI in radiation oncology.
LLMs with RAG for Domain Adaptation
Jan 2025, Oct 2024, Feb 2024
A hands-on guest lecture at Virginia Tech on building domain-adapted AI virtual assistants using LLMs with Retrieval-Augmented Generation (RAG), bridging academic concepts with real-world industry applications in healthcare AI.
A Data Scientist’s Journey into AI and Healthcare
May 2022
A personal talk on the journey from Egypt to the U.S. in computer science and machine learning, comparing educational systems and offering practical insights into careers in AI and healthcare data science.
Education
Virginia Tech
Master's in Computer Science • GPA: 4/4.0 • A+
2022 - 2024 • Blacksburg, VA
Thesis:
Detecting Credit-Seeking Behavior on Programmed Instruction Framesets
Honors & Awards:
- •Ranked in the top 10% of graduate students in the College of Engineering at Virginia Tech
- •Graduated with summa cum laude
Activities:
Radford University
Bachelor's in Computer Science • GPA: 4/4.0 • A+
08/2019 - 07/2020 • Radford, VA, USA
Honors & Awards:
- •Graduated with summa cum laude
Cairo University
Undergraduate Studies in Computer Science • A+
07/2016 - 05/2019 • Cairo, Egypt
Honors & Awards:
- •1st Place for three years; Won the Ideal Student Medal