Haidar Jomaa

Haidar Jomaa

I'm interested in the intersection of machine learning, healthcare, and biology. I'm also interested in cognition, and using learning as a lens to study complex problems :)

Posts by Collection

portfolio

publications

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

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).

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

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).

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

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.

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

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).

talks

Huawei Competitions and Opportunities in AI

Published:

Talk presented as a Huawei ambassador, highlighting student competitions, research opportunities, and pathways in artificial intelligence and advanced technologies.

NVIDIA DLI: Fundamentals of Deep Learning

Published:

Hands-on workshop introducing the fundamentals of deep learning using NVIDIA Deep Learning Institute materials, with practical exercises and real-world examples.

From Data to Intelligence

Published:

Talk covering the transformation of raw data into intelligent systems, highlighting machine learning pipelines and real-world applications.

Interview on the AI Landscape in Lebanon

Published:

Interview discussing the state of artificial intelligence research, education, and industry in Lebanon, including challenges and future opportunities.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.