Alexiei Dingli

Associated Professor, Doctor of Artificial Intelligence

Hi, I'm Alexiei, Professor of Artificial Intelligence (AI) at the University of Malta. I have been conducting research and working in the field of AI for more than two decades, assisting different companies to implement AI solutions. My work has been rated World Class by international experts and I won various local and international awards (such as those by the European Space Agency, the World Intellectual Property Organization and the United Nations to name a few). I have published several peer-reviewed publications and I formed part of the Malta.AI task-force which was setup by the Maltese government, aimed at making Malta one of the top AI countries in the world.

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  • 2004-2006

    MBA in Technology Management
    Grenoble Business School, France

    Accounting, Business Law, Finance and Economics, Information Management, International Management, Managing Organisations and change, Marketing, Strategic Management, Technology Management
    Thesis: Aligning operations to strategy using the Balanced Score Card

  • 2001-2004

    Ph.D. in Artificial Intelligence
    University of Sheffield, UK

    Semantic Web, Knowledge Management, Information Extraction and Retrieval, Human Language Technologies, Machine Learning, Adaptive Systems, Web Technologies, Natural Language Processing
    Thesis: Annotating the Semantic Web

  • 1997-2001

    B.Sc. IT (Honours)
    University of Malta, Malta

    Computer Science, Artificial Intelligence, Information Systems, Engineering
    Thesis: MultiPLAT (Multiple Platform Authoring Tool)


Education AI

Every child deserves the best. Through this project, we would like to give every child a personalised education, customised for his needs using AI.

Education is currently facing various challenges. Teachers acknowledge that each student is unique but teaching methods are targeted towards the whole class making it difficult for teachers to cater for the needs of individual students. The Education AI system provideS a personalized learning experience. The browser-based application is aimed at both teachers and students and was specifically designed to be used on their tablets. It generates personalized classwork and homework worksheets for students. They can immediately view the results of the completed worksheets and unlock trophies. Teachers may use the app to create their own exercises or utilize the preloaded curricula. The initial tests show that low performing students who used the app benefitted the most as there was a significant improvement of around 20%. From the questionnaire responses it resulted that almost 3/4 of students preferred working out the worksheets on our system rather than on paper. The teachers surveyed agreed that the app is easy to use and a useful resource. Though we are in the first stages of development of this system, the initial results are very promising.

Alexiei Dingli and Lara Caruana Montaldo: "Artificial Intelligence Assisted Learning Blueprint Designer", in the 13th International Technology, Education and Development Conference (INTED 2019), Valencia, Spain, March 2019. Alexiei Dingli and Lara Caruana Montaldo: "Human Computer Interaction in Education", in the 21st International Conference on Human Computer Interaction (HCII 2019), Orlando, Florida, July 2019. Alexiei Dingli and Lara Caruana Montaldo: "Aspects of an Artificial Intelligence Assisted Learning (AIAL) System", In Embracing Digital Learners in an Age of Global Educational Change and Rapid Technological Advancements: IGI Global (2019)

Intelligent Traffic Management System

Many of us spend 1 month every year stuck in a car. We conduct research on how AI can manage traffic for us, reduce commuting time and allow us to spend with our loved ones.

Advances in technology have enabled the integration of intelligent systems throughout different facets of society. From personal use such as smartphones and IoT devices to larger systems used in hospitals, entertainment, and transport. Intelligent Transport Systems (ITS) are primarily information-based systems that aim to reproduce or predict traffic flow and develop mobility management aids. The problem with implementing ITS systems is the effort in terms of installing and operating the right hardware and technology and the expense that goes with it. In addition, most ITS models work with predictive data. Different use cases are evaluated depending on the location and the model which best fits the requirements is developed. Over time, the model is tweaked to reflect more accurately the traffic and mobility demands of the surrounding environment. The automatic extraction of vehicular and transport information is an important aspect of ITS systems. Being able to effectively manage different sources of data and correctly mapping them on a live representation of the traffic situation can aid not only in correcting issues with predictive analysis but also dynamically manage mobility by controlling traffic lights, tidal lanes, and other traffic-related control structures. Our work presents an approach to modelling live data provided by different sources over the internet. The approach is inexpensive to build and operate. It aims to improve upon current implementation by making information more accessible to both authorities and citizens. In addition, a use case is presented with the intent of establishing the groundwork for future studies in the area.

Mark Bugeja, Alexiei Dingli, Maria Attard, Dylan Seychell: "Comparison of Vehicle Detection Techniques applied to IP Camera Video Feeds for use in Intelligent Transport Systems.", in the 2nd International Congress on Transport Infrastructure and Systems in a changing world (TIS ROMA 2019), Rome, Italy, September 2019.

Virtual Reality (VR) for
Pain Management

Through this system, we want to give hope to all children undergoing treatment, with the use of our VR system whilst at the same time, help them feel less pain and reduce their anxiety.

Children suffering from various illnesses and diseases such as cancer have to undergo numerous procedures on a day-to-day basis as part of their treatment. According to the World Health Organisation, childhood cancer makes up between 50 to 200 cases per million every year. Most medical interventions involved with these cases are painful and as a result instil uneasiness and anxiety in the child who has to undergo these procedures. Distractions are a simple way to alleviate this anxiety and also possibly reduce the actual pain of the intervention itself. Local branches of NGOs such as Dr Klown have popularized the use of "clown doctors" to entertain children suffering from these conditions. However, such NGOs require personnel on a 24/7 basis, whilst still having to spend limited amounts of time beside every patient. The objective of this project is to make use of an Adaptive Virtual Reality (VR) system which is autonomous and can distract the child whilst undergoing treatment. The effect of such a system (which has been documented in various projects) is to reduce the pain felt by the patient. Our project will go beyond the state of the art since we will make use of adaptation which will make the VR environment change according to the level of anxiety experienced by the patient. This will be measured using biometrics collected through non-invasive wearable devices.

AI in Manufacturing

Digital Twin technology can change the way we manufacture goods forver by monitoring thw whole process, optimising the manufacturing process and taking effective decisions autonomously.

Collecting data about processes and performance has been a core area of interest for most businesses. Various technological advancements have improved this process through several tools, methods, and approaches which have been introduced to this field. In the Semiconductor industry, a massive volume of ingested data is being generated from the lifecycles of different products. In contrast, gathering data in real time from platforms such as the Internet of Things (IoT) has brought a number of challenges, for instance; the structured, semi-structured and unstructured data generated from the manufacturing process, bottlenecks when designing large analytical tools, and a need for high capacity storage to save and organize the collected data. Because of this, we are designing a Digital Twin (DT) system within the Semiconductor manufacturing area. A new approach is being piloted that is going to help an multinational semiconductor manufacturing company to automate and digitize their physical production process. Furthermore, this project aims to design a high-level application that allows engineers to enhance their product design, planning, manufacturing while incorporating predictive and prescriptive maintenance.

Alexiei Dingli and Foaad Haddod: "Interacting with Intelligent Digital Twins", in the 21st International Conference on Human Computer Interaction (HCII 2019), Orlando, Florida, July 2019. Alexiei Dingli and Foaad Haddod, "Intelligent digital twin system design in semiconductor manufacturing", at the International Conference on Industry 4.0 and Artificial Intelligence Technologies (INAIT 2019), Cambridge, UK, August 2019.

This is just a small portion of my work. I'm interested in various AI projects in vrious areas (such as FinTech, Creative Computing, Game AI, etc.)

Explainable AI

AI tends to be considered as being the domain of the illuminated few. Systems seem to work well and you have to blindly trust their results. Through this project, we are creating an AI that explains itself, thus reassuring the users.

In recent years, Machine Learning (ML) solutions have taken great strides in domains such as Image Recognition, Natural Language Processing, Speech Automation, and many more domains. However, despite performing their tasks well, many companies which utilize these systems as a backbone for their decision-making processes are wondering how their machines are arriving to certain conclusions. More importantly, they are questioning whether or not they should trust these systems. In order to address these concerns, we are working on Explainable AI (XAI), where a machine is able to demonstrate the reasoning behind its answers in a concise, human-interpretable format. XAI is critical to create transparency in our AI systems to allow these systems to be trusted.


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Rediscovering Heritage Through Technology

April 2020

A collection of innovative research case studies that are reworking the way we experience heritage

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Aligning Operations with Strategy using the Balanced Scorecard

August 2016

The scope of this book is to find out how an organization can realize that it is losing alignment before it is too late and take corrective actions that will have a long lasting effect.

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The New Digital Natives

March 2015

The book investigates the paradigm shift between different generations while exploring how the world needs to change in order to harness the potential of these new digital natives.

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Knowledge Annotation

July 2013

The book explores how to make implicit knowledge explicit using Artificial Intellignce.

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  • Faculty of ICT,
  • University of Malta,
  • MSD 2080