Zein Shehabeddine
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I am a computer scientist interested in applied machine learning, software engineering, and bioinformatics.
My work spans clinical prediction, genetic variant classification, sign language technologies, and large-scale bacterial genomics.
Skills
- PyTorch
- TensorFlow
- scikit-learn
- Pandas
- NumPy
- OpenCV
- MediaPipe
- Deep Learning
- Computer Vision
- Statistical Analysis
- Data Analysis
- Computational Genomics
- Python
- C
- Java
- JavaScript
- SQL
- Bash
- HTML/CSS
- Linux
- Docker
- Git
- Flask
- REST APIs
- SQLite
- CI/CD
- HPC
Experience
Graduate Research Assistant
Conducted research in applied machine learning and bioinformatics across clinical outcome prediction, genetic variant classification, autism speech analysis, and large-scale bacterial operon prediction. Developed machine learning and data analysis pipelines for clinical, genomic, speech, and bacterial genome datasets.
Graduate Assistant
Taught computer science labs and recitations covering data structures and algorithms, Java programming, cybersecurity, and Microsoft Excel/Access. Developed lab exercises and assessment questions, prepared instructional materials, and graded lab assignments and examinations.
Education
American University of Beirut
Dean’s Honor List · Competitive Programming Team · ABLE Hackathon Finalist
OperonAtlas
OperonAtlas is the largest-scale database of computationally predicted bacterial operons, spanning more than 21,000 bacterial genomes and nearly 90 million genes. It provides an interactive web platform for searching, visualizing, comparing, and downloading predicted operons.
Zero Knowledge Battleship
A zero-knowledge implementation of Battleship using the gnark proving system (Groth16) and a custom MiMC-based circuit. The project demonstrates private board commitments, constraint programming, proof generation, and verification for secure gameplay without revealing players' hidden boards.
Labeeb
Labeeb is the first Lebanese Sign Language translation app, providing real-time offline translation for the Arabic alphabet and common Lebanese signs using lightweight landmark-based machine learning models.
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