Our research in the AI area focuses on applying algorithmic advances, in machine and deep learning, to problems in a wide range of areas, including health, networking and systems, search and information retrieval, social science, and Arabic natural language processing.
Faculty Members:
Primary:
Abdulaziz Al-Ali (Machine Learning, Deep Learning)
Saleh Alhazbi (Machine Learning)
Cagatay Catal (Deep Learning)
Tamer Elsayed (Information Retrieval, Natural Language Processing, Machine Learning)
Junaid Qadir (AI Ethics)
Khaled Shaban (Natural Language Processing, Deep Learning, Machine Learning)
Secondary:
Khalid Abualsaud (Machine Learning)
Abdelaziz Bouras (Deep Learning)
Abdelkarim Erradi (Deep Learning)
Wadha Labda (Machine Learning)
Amr Mohamed (Deep Learning)
Uvais Qidwai (Machine Learning)
Saeed Salem (Machine Learning)
Ahmed Badawy (Machine Learning)
Related Projects:
NPRP13S-0112-200037: MURASALAT: Multi-dialect Arabic Understanding Models for Empathy-based Negotiating Chatbot and Question Answering
NPRP11S-1204-170060: Early Detection of Fake News over Arabic Social Media
NPRP10-0205-170346: MOALEM: An Assistive Platform for Children with Arabic Reading and Writing Difficulties
NPRP7-1313-1-245: Efficient and Scalable Evaluation for Searching Massive Arabic Social Media and Web Collections
NPRP7-442-1-082: Intelligent System to Digitally Support Paleographic Analysis of Ancient Manuscripts in Qatar
NPRP6-1377-1-257: Answering Real-time Questions from Arabic Social Media
NPRP5-1345-1-228: Novel Prognostic Biomarkers for Colorectal Cancer via Computerized Analysis of Highly Multiplexed Protein Fluorescence Image Data
NPRP09-864-1-128: Intelligent Document Management System for Automatic Writer Identification
NPRP14S-0402-210127: Dimension-Specific Automated Scoring of Arabic Language Writing Proficiency
NPRP14S-0413-210206: Defense Against Hardware Intrinsic Attacks in Resource-Constrained COTS based Traditional and Futuristic Artificial Intelligence of Things (AIoT)