This area of research is focused on developing new machine learning (ML) algorithms for advancing the field of Artificial Intelligence, Natural Language Processing (NLP), Computer Vision, Big Data Analytics, and Robotics. The research also uses the advances to solve real world problems. Example applications include: Genomic analysis, health and disease relations, energy, water, smart cities, cybersecurity, networking, refugees, e-government, crowdsourcing, addressing bias in AI, conversational AI, Arabic-specific NLP and language models, and Arabic specific ML resources.
Below is a list of selected faculty members who conduct research relevant to this area
Applications in cybersecurity and networking
Fatima Abu Salem
Data Science and artificial intelligence for the public and social good.
Enabling explicit machine processable semantics using knowledge graphs and application of machine learning for analyzing unstructured data (e.g., product reviews).
Machine Learning theory and applications to advance the field. In particular: Natrual Language Preocessing, Vision, and Sensor Analytics.
Machine Learning to improve health outcomes for mothers & newborns.
Designing algorithms that impose fairness measures in training machine learning models. Developing algorithms that improve generalization in deep learning.
Multi Agent research, Human Machine Interaction.
Improving the perfromance of robotic system solutions that are tailored for precision agriculture (PA) and human-robot interaction (HRI).
AI models to predict how the human genome unfolds in health and disease states.
Applications of machine learning to VLSI and noise mitigation.
Applications of deep learning in various domains such as Natural Language Processing, Computer Vision, Medicine, and Public Health.
Deep learning for Natural Language Processing, Information Retrieval and Computer Vision.
Detection and mitigation of bias and discrimination in AI applications, and hate speech detection.
Resources and models to enhance Arabic Natural Language processing and understanding.