Dr. Pratheepan Yogarajah has been a Lecturer (~Assistant Professor) in Computer Science at Ulster University since January 2016. He obtained a first-class honours degree in Computer Science from the University of Jaffna, Sri Lanka, in 2001 and an MPhil in Computer Vision from Oxford Brookes University, UK, in 2006. He later earned his PhD from Ulster University, UK, in 2015.

Dr. Yogarajah is a member of the British Computer Society (BCS) and IEEE. He has filed two patents and has published over 60 papers in international conferences, journals, and books.

Research and Specialization

Dr. Yogarajah specializes in computer vision, machine learning, and deep learning, with a primary focus on health informatics applications. His research is particularly dedicated to:

• Early detection of mental health conditions, including autism, dyslexia, and dysgraphia in children
• Advancements in deep learning and explainable AI for medical imaging

With a strong background in artificial intelligence and data science, he actively contributes to interdisciplinary research, applying AI-driven solutions to real-world healthcare challenges. His work aims to bridge the gap between theoretical AI models and practical applications, ensuring that machine learning technologies are interpretable, effective, and ethical in sensitive domains such as mental health screening and medical imaging.

Awards and Grants

Dr. Yogarajah has been awarded an EPSRC grant (2024–2026) for his research on automated pre-screening tools for Specific Learning Disabilities (SpLDs) in children. He has also received several prestigious awards and funding, including:

• Oxford Brookes University HMGCC Scholarship Award (2005)
• Proof of Principle (PoP) Award, Ulster University (2012)
• Proof of Concept (PoC) Grant, Invest Northern Ireland (2013)
• Interreg North-West Europe Grant (2019)
• Innovate UK Grant (2021)
• GCRF Pump-Priming, Research Impact, and ISPF Grants from Ulster University

Through his pioneering research, Dr. Yogarajah continues to make significant contributions to AI-driven healthcare innovations, particularly in the early detection and intervention of mental health conditions and learning disabilities.