After KAUST Academy
Farah is pursuing a Master's degree in computer science at Columbia University in New York. Farah's paper on identity leakage in privacy-preserving action recognition was accepted to ACCV 2026, and Farah and a team won the Material Discovery Hackathon at KACST in January 2026. Farah also worked as a teaching assistant for KAUST Academy's AI Specialization.
Summer project
Artificial Intelligence Specialization · Cohort 2025
Interpretable Multitask Learning with Kolmogorov–Arnold Networks for Mental Health Comorbidity Prediction
Mental health disorders often co-occur, a phenomenon known as comorbidity, posing significant challenges to accurate diagnosis and personalized treatment. Traditional machine learning approaches for comorbidity prediction frequently operate as black-box models, limiting their clinical applicability due to a lack of interpretability. This project aims to address this gap by developing an interpretable multitask learning framework using Kolmogorov–Arnold Networks (KANs) for predicting mental health comorbidities. By integrating KANs into a multitask learning setting, the proposed framework will simultaneously predict multiple mental health conditions (e.g., depression, sleep, anxiety) while capturing their shared underlying patterns and specific risk factors.
Mentor: Mai Ali
With KAUST Academy
- Cohort 2025
Artificial Intelligence Specialization
University of Toronto · Artificial Intelligence Summer Program
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