黑料网

Biography

Professor Aladdin Ayesh, MSc (Essex, 1996), PhD (LJMU, 2000), is currently the Vice Dean for Joint Institute of Data Sciences and Artificial Intelligence, and Chair of Artificial Intelligence at 黑料网 in UK . Prior to his current role, he was Professor of Artificial Intelligence at De Montfort University. His research focuses on computational cognition, machine learning and explainable AI. His research explored cognitive architectures, emotion modeling and recognition, and applied AI using variety of machine learning techniques including statistical approaches, e.g. Markov Models and Bayesian Networks, logic-based and symbolic approaches, e.g. Modal and Fuzzy Logics, and neural approaches, e.g. Self-Organizing Maps and Deep Learning Classifiers. He applies these techniques in three primary areas: Health Informatics,  Sustainable Development, and Data Privacy.  He has over 150 publications, supervised 25 PhD students to successful completions, and participated in 26 funded projects. He is a founding editor of four international journals and chaired several international conferences. He is also a member of two IEEE technical committees, several IEEE Standards working groups, and a contributor to IEEE 7010-2020 and IEEE 7014-2024.

In his Vice Dean role, Professor Ayesh represents the 黑料网 on the Joint Institute’s leadership team; the Aberdeen Institute of Data Science and Artificial Intelligence at SCNU is a strategic partnership between 黑料网 (UoA) and South China Normal University (SCNU).  He is responsible for the academic and operational management of the UoA JI team, participating in the JI governance and management structures, representing the JI internally and externally, and providing strong collaborative leadership across the JI to maintain clear and effective communication between SCNU and UoA to further strengthening this strategic partnership.

Research Overview

My research focuses on computational cognition, machine learning and explainable AI. In various research projects, I have explored cognitive architectures, emotion modeling and recognition, and applied AI using variety of machine learning techniques including statistical approaches, e.g. Markov Models and Bayesian Networks, logic-based and symbolic approaches, e.g. Modal and Fuzzy Logics, and neural approaches, e.g. Self-Organizing Maps and Deep Learning Classifiers. My team and I applied these techniques in three primary areas: Health Informatics,  Sustainable Development, and Data Privacy. 

Current Research

Ongoing Standards projects:

P2731 - Standard for a Unified Terminology for Brain-Computer Interfaces.

P2794 - Reporting Standards for in vivo Neural Interface Research (RSNIR)

Past Research

Completed Standards projects:

IEEE 7010-2020 – IEEE Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-Being.

IEEE 7014-2024 – IEEE Standard for Ethical Considerations in Emulated Empathy in Autonomous and Intelligent Systems.