Aladdin Ayesh, MSc (Essex, 1996), PhD (LJMU, 2000), is currently the Vice Dean for Joint Institute of Data Sciences and Artificial Intelligence at University of Aberdeen in UK. He also holds a Personal Chair as a Professor of Artificial Intelligence. Prior to his current role, he was a 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. Prof. Ayesh has over 150 publications, supervised 24 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 – IEEE Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-Being.
DVP term expires December 2025
User-Centric AI Analytics for Chronic Health Conditions Management
The use of AI analytics in health informatics has seen a rapid growth in recent years. In this talk, we look at AI analytics use in managing chronic health conditions such as diabetes, obesity, etc. We focus on the challenges in managing these conditions especially with drug-free approaches due to the variations in individual circumstances.
Challenges and Advantages of AI in Setting Up Digital Twins Infrastructures
In a highly connected world, AI technologies proved very useful in drawing insights from data and managing large-scale infrastructures while allowing scalability in underlying system models. In this talk, we examine these AI technologies specifically in the context of digital twins. We look at how AI technologies can help to achieve some of digital twins key objectives, especially in enabling its infrastructure setup and scalability. At the same time, we look at the challenges digital twins infrastructure presents. We would particularly draw on examples from sustainable development case studies
- User-Centric AI Analytics for Chronic Health Conditions Management
- Challenges and Advantages of AI in Setting Up Digital Twins Infrastructures