About Me

I am a PhD student at Queen Mary University of London and a DeepMind PhD Scholar, specializing in Vision-Language Foundation Models with an emphasis on fairness, trustworthy AI, and multimodal reasoning. My work is driven by a commitment to ‘AI for Good,’ exploring innovative solutions in machine learning—particularly for medical applications—that balance cutting-edge performance with ethical and societal considerations. My research includes projects on transfer learning, computer vision, and graph neural networks, showcasing my dedication to leveraging technology for real-world impact.

I hold a Master’s Degree from the American University of Beirut, where I also served as a teaching assistant in programming and data structures. Additionally, I have collaborated with platforms like deeplearning.ai to develop and test new ML curricula, reflecting my passion for both advancing research and democratizing AI education.

Education

PhD in Computer Science
Queen Mary University of London
Trustworthy Vision Language Models with focus on Fairness, Reasoning, and Safety
DeepMind PhD Scholar
Master’s in Electrical and Computer Engineering
American University of Beirut
Thesis:Transferability of Graph Neural Networks for Time Series Applications
Research on transfer learning, computer vision, and graph neural networks

Research

PhD Researcher
Queen Mary University of London
Working on Vision Language Foundation Models while focusing on fairness and reasoning.
Research Assistant
American University of Beirut
Assisted in various AI projects, particularly in transfer learning, computer vision, spatio-temporal graph neural networks, ai for agriculture, bias in hate speech detection systems, and deployment of ai models.
Machine Learning Researcher
Fields Institute and University of Toronto
Proposed adding fractional derivatives and features (vaccination, mean society age ,stringency index) then applying PCA to Hurricane forecasting model.

Teaching

Teaching Assistant
University College London
Supported courses in machine learning and programming, enhancing student understanding of complex concepts.
Demonstrator
Queen Mary University of London
Assisted in teaching deep learning and computer vision courses, providing hands-on guidance and support to students.
Teaching Assistant
American University of Beirut
Delivered courses in programming and data structures, emphasizing foundational education in computing principles.
Instructor
Udacity
Led online instructional sessions focused on Python programming and introductory AI concepts, tailoring lessons to a diverse online audience as part of AWS AI scholarship and Google Launchpad scholarship.
Teaching Assistant
Data Science and AI in Health Summer School
Directed sessions on AI applications in health, demonstrating the impact of machine learning in medical diagnostics through interactive learning.

Publications

FairCoT: Enhancing Fairness in Text-to-Image Generation via Chain of Thought Reasoning with Multimodal Large Language Models
Proposes an MLLM reasoning based method for debiasing text to image generation,while introducing a new evaluation metric.
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Bias Mitigation in Hate Speech Detection
Discusses bias mitigation in hate speech detection with hands-on exercises.
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Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges
A comprehensive exploration of multimodal ML techniques for enhancing mental health diagnostics and treatments.
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The Power of Transfer Learning in Agricultural Applications: AgriNet
Proposes pretrained models and agriculture dataset for improving efficiency in agricultural practices.
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Spatio-Temporal Graph Neural Networks: A Survey
Surveys the development of spatio-temporal GNNs and their applications in dynamic environments and proposes a taxonomy for classifying the algorithms.
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Awards

DeepMind PhD Fellowship
Winner of AI Hack Covid Hackathon
LIRA Fund Award
FYP Accelerator Award (Advisor)