Team | Computational Reasoning and Discovery Lab

PI

Rida Assaf

Rida Assaf leads the Computational Reasoning and Discovery Lab, focusing on computational methods for biomedical data analysis.

Current

Haidar Jomaa

MSc Computer Science

Working on machine learning and computer vision for ophthalmic disease detection and corneal analysis, genomics (variant calling), and investigating the impact of differential privacy on learned representations in neuroscience.

Zein Shehabeddine

MSc Computer Science

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.

Zahraa Al-Husaini

MSc Computational Science

Researching privacy-preserving methods for medical imaging, specifically the impact of differential privacy on models trained on MRI data.

Aida Fakher

MSc Computer Science

Interested in applied machine learning and data-driven healthcare, with work spanning clinical prediction models and personalized treatment optimization, particularly in using predictive modeling to support Disease-Modifying Therapy (DMT) selection in Multiple Sclerosis.

Sara Shawraba

MSc Computational Science

Interested in computational biosciences and the application of AI and machine learning to biological and healthcare problems. My research so far has focused on developing large language models for gene essentiality prediction.

Nour Obeid

MSc Computer Science

Interested in applied machine learning and bioinformatics. My work spans clinical prediction models using deep learning and network-based analysis for high-dimensional Omics datasets, aiming to bridge the gap between computational models and biological discovery.

Alumni

Bassel Fakhri

MSc Computer Science

Worked on representation learning in genomics (LLM-based operon prediction), and computer vision methods for cellular image preprocessing and segmentation to support pharmacological studies.

Ghia Sanjar

MSc Computational Science

Worked on applied machine learning for large-scale humanitarian survey analytics (TabNet-based analysis of negative coping mechanisms in Ukrainian households) and LLM-based methods for serialized injury claim classification.