Professor Aladdin Ayesh

CITP CEng FBCS, SFHEA

Personal Chair of Artificial Intelligence

Accepting PhDs

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Email Address: aladdin.ayesh@abdn.ac.uk
School/Department: School of Natural and Computing Sciences

Biography

Aladdin Ayesh, MSc (Essex, 1996), PhD (LJMU, 2000), is currently the Vice Dean for Joint Institute of Data Sciences and Artificial Intelligence at 51cg 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.

Memberships and Affiliations

Internal Memberships: School Executive.

Joint Institute Committees.
External Memberships: BCS Fellow

IEEE Senior Member

AHE Senior Fellow

IEEE Transactions on Affective Computing steering committee chair (2019 - 2024)

Research

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.

Research Areas

Accepting PhDs

I am currently accepting PhDs in Computing Science, Artificial Intelligence.

Please get in touch if you would like to discuss your research ideas further.

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Computing Science

Supervising
Accepting PhDs

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Artificial Intelligence

Supervising
Accepting PhDs

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Nutrition and Health

Supervising

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Applied Health Sciences

Supervising

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Research Specialisms

Artificial Intelligence
Machine Learning
Cognitive Modelling
Natural Language Processing
Neural Computing

Our research specialisms are based on the Higher Education Classification of Subjects (HECoS) which is , published under the licence.

Supervision

My current supervision areas are: Computing Science, Artificial Intelligence, Nutrition and Health, Applied Health Sciences.

I have supervised 25 PhD projects to successful completions of which 21 first supervision and 4 second supervision.

I have examined 23 PhD and MPhil projects of which 18 at National Universities: Hull, Essex, Manchester, Edinburgh, Leeds, Leicester, Huddersfield, Liverpool John Moores, and Bradford; and 5 at European Universities of: Le Havre, Rouen, Paris 8, and Granada.

Publications

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Biography

Memberships and Affiliations

Research Overview

Research Areas

Accepting PhDs

Computing Science

Artificial Intelligence

Nutrition and Health

Applied Health Sciences

Research Specialisms

Supervision

The implementation of optimization algorithm for energy efficient dynamic ad hoc Wireless Sensor Networks

Threshold efficient multiple routes in AODV extensions

Optimizing the communication distance of an ad hoc wireless sensor networks by genetic algorithms

Access Network Selection Based on Fuzzy Logic and Genetic Algorithms

Access network selection for coexisted WWAN, WMAN and WLAN using combined fuzzy logic and AHP

A millenson-based approach to emotion modelling

Aqua swarms: Design and implementation of water surface AUV

Development of software effort and schedule estimation models using soft computing techniques

Energy efficient PSO-based algorithm for optimizing autonomous wireless sensor network

Identification of a chemical process reactor using soft computing techniques

Is paice method suitable for evaluating arabic stemming algorithms?

Modeling the emotional state of an agent through fuzzy logic with reference to the geneva emotion wheel

Multi-agent system based regional Data grid

On-demand multiple route maintenance in AODV extensions (ORMAD)

Optimizing autonomous mobile sensors network using PSO algorithms

Pattern-based algorithm for part-of-speech tagging arabic text

Real time movement cooredination technique based on flocking behaviour for multiple mobile robots system

Regionally Distributed Architecture for Dynamic e-Learning Environment (RDADeLE)

Text mining based on Self-Organizing Map method for Arabic-English documents

The AISB'08 symposium on swarm intelligence algorithms and applications

Access network selection using combined fuzzy control and MCDM in heterogeneous networks

A general framework for multilingual text mining using self-organizing maps

Analytical study to detect threshold number of efficient routes in multipath AODV extensions

Fuzzy Individual Model (FIM) for realistic crowd simulation: Preliminary results

Particle swarm optimisation enhancement approach for improving image quality

An application of neural network for extracting Arabic word roots

Developing a tagset for automated POS tagging in Arabic

A triliteral word roots extraction using neural network for Arabic

Structured sound based language for emotional robotic communicative interaction

Simulation and Visualization of a large scale Real Time Multi-Robot system

Emotional analysis of facial expressions

Emotionally motivated reinforcement learning based controller

Extracting subtle facial expression for emotional analysis

Guest editorial: Special issue on perception and emotion based reasoning

Perception and emotion based reasoning: A connectionist approach

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