AICL Lab

Longo's AICL Lab

Artificial Intelligence & Cognitive Load Research Lab

Supported by IBM, it leverages on its Power processor-based technology. We use the physical servers and software made available under the IBM Power Academic Initiative umbrella, and IBM Power Virtual Server on IBM Cloud.

The discipline of Artificial Intelligence (AI) has been drastically influencing the society, and since its inception in the 1950s, it has evolved in many different ways. Originally constrained in laboratories, where the foundations of AI were laid, it is now a wide field of research, with many ramifications. AI grew from a bunch of small-scale ideas to important research areas such as machine learning, reasoning, natural language processing, robotics, planning and perception as well as computer vision. This evolution was followed by a plethora of applications in fields such as medicine and health-care, finance and marketing, the entertainment and gaming industry, transportation, military and safety critical fields, as well as more recent such as in education and neuroscience. The development of artificial intelligence is in full speed, powered by advances in machine learning that are supporting the creation of technologies that are changing and re-shaping people`s everyday lives such as self-driving cars and chat-bots just to mention a few. Humans have now the possibility to augment their lives by interacting with technologies and interfaces that are multi-modal in nature, through the use of different senses, including touch, sight, smell and taste. However, the design of these technologies is far from being a trivial task, since the human is at the center and ultimately the final consumer. Therefore, it is not only a matter of designing good algorithms, but rather shaping technologies that learns from human input and collaboration in a closed loop, and can continuously improve themselves while providing an effective experience to their consumers. One emerging and multidisciplinary exciting field of research focuses on cognitive load modeling whereby the cognitive activation of humans, interacting with these multi-modal technologies, can be assessed in real-time and can serve as a meaningful information to both understand users behavior and, in turn, provide rich input to AI-based systems. By developing intelligence machines, with a goal of understanding human behavior, language and emotions, the Artificial Intelligence and Cognitive Load Lab (AICL) performs exciting research aimed at pushing the boundaries of artificial intelligence and bridging the gap between machines and human beings.

Equality, Diversity and Inclusion Statement

The AICL lab and its members believe that science is much better with a diverse team. Optimal science not only benefit from diversity, but requires it to address scientific problems from innovative, novel and unique perspectives. We embrace and respect all aspects of the identities of our members including age, disability, ethnicity, family or marital status, gender identity or expression, origin, political affiliation, race, religion, sexual orientation, socio-economic status. The AICL Lab is dedicated to building a diverse and highly inclusive academic community creating a supportive environment by providing resources for individuals to navigate the systemic obstacles in the broader fields of Computer Science, Neuroscience, Education and academia. Recognizing as human beings we are all 'work in progress', we dedicate time and space to continuing to learn with the aim of collectively source tools and resources to offer. We vow to purposefully identify, discuss and challenge issues of race and color and their impacts to science and academia. We keep challenging ourselves and each other for understanding and continuously correcting any potential inequities and gain a better understanding of ourselves. We apply a community of inquiry approach to learning, actively engaging in difficult but constructive dialogues in order to promote understanding and deepen perspectives.

Active members

Dr. Luca Longo

Founder & Principal Investigator, AICL Lab
Neuroinformatics, Defeasible Argumentation, Deep Learning, Neuro-symbolic reasoning, Explainable Artificial Intelligence, Instructional efficiency
Technological University Dublin

Dr. Lucas Rizzo

Assistant lecturer
Argumentation Theory, Neuro-Symbolic reasoning
Technological University Dublin

Dr. Bujar Raufi

Marie Curie Post Doctoral Fellow
Cognitive Load Modelling
Technological University Dublin

Dr. Giuliano Orrù

Post-doctorate
Intelligence Instructional design, Constructivist learning
Technological University Dublin

Carlos Gómez

PhD candidate
Graph Neural Networks, Computational cognitive load, Multiple Resource Theory, Electroencephalography
Machine Learning Labs, Technological University Dublin

Taufique Ahmed

PhD candidate
Variational autoencoder, Cognitive load modeling
Technological University Dublin

Aparna Nayak

PhD candidate
Knowledge graphs, data quality
Machine Learning Labs, Technological University Dublin

Kyle Hamilton

PhD candidate
Knowlege-graphs, Neuro-symbolic reasoning
Machine Learning Labs, Technological University Dublin

Karim Moustafa

PhD candidate
Explainable Artificial Intelligence, Evaluation methods for XAI
Technological University Dublin

Giulia Vilone

PhD candidate
Explainable Artificial Intelligence, Rule extraction method, Neuro-symbolic reasoning
Technological University Dublin

Arjun Vinayak Chikkankod

PhD candidate
Convolutional deep autoencoders, Cognitive load modeling, Microstate theory
D-Real centre, Technological University Dublin

Beth Walsh

PhD candidate
Cognitive Load Modelling
Technological University Dublin

Gargi Gupta

PhD candidate
Explainable Artificial Intelligence
ML-Labs, Technological University Dublin

Vladimir Marochko

PhD candidate
Autoencoders, Neurophysiology, Event-related potentials, Virtual Reality
D-Real centre, Technological University Dublin

Sanjay Kumar

PhD candidate
Computational Argumentation, NLP, Argument mining
D-Real centre, Dublin City University, Technological University Dublin

Anastasia Natsiou

PhD candidate
Autoencoders, Sound generative models, Deep learning
ML-Labs, Technological University Dublin

Matthew Rigney

PhD candidate
Neuroergonomics, Electroencephalography
Technological University Dublin

Oleksandr Davydko

PhD candidate
Medical Image Processing, Deep Learning, Operation research in medical field
Technological University Dublin

Alumni


PhD research candidates
Post-doctoral researchers
Master students


Credits

AIRC ML-Labs D-Real CeADAR SFI EI IRC IBM academic initiative TU Dublin