Haidar Jomaa

Haidar Jomaa

I am interested in the intersection of machine learning, healthcare, and biology to better understand and improve human health.

Projects

Computational Prediction of Replication Origins: A Comparative Review of Methods, Benchmarks, and Trends from Heuristics to Deep Learning

Published in Briefings in Bioinformatics (under review), 2025

A comprehensive review of computational approaches for replication origin prediction, covering heuristic, statistical, and deep learning-based methods.

Recommended citation: Haidar Jomaa, Rida Assaf. (2025). "Computational Prediction of Replication Origins: A Comparative Review of Methods, Benchmarks, and Trends from Heuristics to Deep Learning." Briefings in Bioinformatics (under review).

Machine Learning-Based Detection of Keratoconus Using Zernike Polynomials Derived from Corneal Pachymetry Profiles

Published in AUB Biomedical Research Days (poster), 2025

A machine learning framework leveraging Zernike polynomial representations of corneal pachymetry for keratoconus detection.

Recommended citation: Haidar Jomaa, Rida Assaf, Jad Assaf, Zahi Wehbi, Shady T. Awwad. (2025). "Machine Learning-Based Detection of Keratoconus Using Zernike Polynomials Derived from Corneal Pachymetry Profiles." Poster presented at AUB Biomedical Research Days, American University of Beirut.

Raw Versus Engineered Tomographic Representations for Post–Laser Vision Correction Ectasia Diagnosis

Published in Ophthalmology Science (submitted), 2025

An empirical comparison of raw and engineered corneal tomographic representations for post-LVC ectasia detection using machine learning.

Recommended citation: Rida Assaf, Haidar Jomaa, Jad F. Assaf, Zahi Wehbi, Shady T. Awwad. (2025). "Raw Versus Engineered Tomographic Representations for Post–Laser Vision Correction Ectasia Diagnosis." Ophthalmology Science (submitted).

Revisiting the Inferior–Superior (I–S) Value Metric Using Machine Learning for Early Detection of Keratoconus

Published in Journal of Refractive Surgery (submitted), 2025

A machine learning-based re-evaluation of the classical Inferior–Superior corneal metric for early keratoconus detection.

Recommended citation: Shady T. Awwad, Jad F. Assaf, Zahi Wehbi, Haidar Jomaa, Rida Assaf. (2025). "Revisiting the Inferior–Superior (I–S) Value Metric Using Machine Learning for Early Detection of Keratoconus." Journal of Refractive Surgery (submitted).