Zein Shehabeddine
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I am a computer science researcher interested in applied machine learning, as well as cybersecurity and low‑level programming.
My work spans clinical prediction models, genetic variant classification, and applied systems projects such as autonomous decision‑making systems.
Skills
- Clinical Machine Learning
- Computational Genomics
- Deep Learning
- Computer Vision
- Feature Engineering
- Statistical Analysis
- Data Analysis
- Cybersecurity
- Operating Systems
- Low-Level Programming
- Virtual Machines
- Applied Cryptography
- SSH
- Containerization
- Server Administration
- Cloudflare
- Oracle Cloud
Experience
Graduate Research Assistant
Conducting research in applied machine learning and bioinformatics, including projects on clinical prediction (PAD/CLTI), genetic variant classification (BRCA1/2), autism speech modeling, and bacterial operon annotation. Also instructed computer science labs in cybersecurity and data structures.
Competitive Programmer
Solved over 150 Project Euler problems and competed in programming tournaments. Awarded the Late Henri Qais Naccache Competitive Programming Award.
ABLE Hackathon Finalist
Led a team to develop a real-time sign language translation system using TensorFlow, OpenCV, and custom data collection pipelines.
Education
American University of Beirut
Dean’s Honor List · Competitive Programming Team · ABLE Hackathon Finalist
Zero Knowledge Battleship
A full zero-knowledge proof implementation of the Battleship game using the gnark proving system (Groth16) and a custom MiMC-based circuit. The project demonstrates private board commitment, constraint programming, and on-chain/Verifier-level proof checking for secure gameplay without revealing secret information.
Labeeb
Labeeb is the first Lebanese Sign Language Translator app designed to address communication challenges faced by the deaf and hard-of-hearing community. The app offers fast real-time offline translation services for both the Arabic alphabet and common Lebanese words, facilitating seamless communication between sign language users and those seeking to engage with this community.
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