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Artificial Intelligence / Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are transforming industries, enabling smarter decision-making, automation, and advanced data analysis. From self-driving cars to personalized recommendations, AI/ML is reshaping the future.

Artificial Intelligence (AI): The simulation of human intelligence in machines, allowing them to perform tasks such as problem-solving, decision-making, and language understanding.
Machine Learning (ML): A subset of AI that enables systems to learn from data and improve their performance without explicit programming.

It has applications in various fields such as:


Robotics	Autonomous Vehicles	Surveillance Systems	AI Governance	Sustainability

Hybrid Human-AI	Trustworthy AI	Logic	Finance

STEP4NAMs -STEP4NAMs - 2025-2028
PavAnalytics – https://www.paveanalytics.eu/ – 2023-2025
Augmented Reading Room for Radiologists – 2024-2025
Project Title: Artificial intelligence-powered 3D printing (aiPRINT)
Funding Agency: Research Ireland – National Challenge Fund (Concept, Seed &Grow Phase)
Start and End Dates: 01/07/2023 – 30/06/2025
PI/Co-PI: Co-Investigator Researchers Involved: Dr. Karl Mason, Dr Andrew Daly, Dr. Vasileios Sergis, Dr. Daniel Kelly, Dr. Usman Haider, Lukasz Szmet
Webpage Link: https://www.sfi.ie/challenges/future-digital/aiprint/
Brief Description: This project advances biofabrication by using computer vision to detect 3D printing errors as they occur and reinforcement learning to correct extrusion errors.
UNSDGs Addressed: 9, 12
Thematic Area: Artificial Intelligence / Machine Learning, Data Science, Computer vision & Robotics
Tunable Multi-Agent Reinforcement Learning for Peer-to-Peer Residential Energy Trading – 2022-2025
Project Title: Effective Integration of Renewable Energy within the Agriculture Sector in Ireland using Artificial Intelligence (EIRE AIAI)
Funding Agency: Research Ireland – Frontiers for the Future Programme
Start and End Dates: 01/07/2022 – 30/06/2027 (60 months)
PI/Co-PI: Principal Investigator and Lead Applicant
Researchers Involved: Dr. Karl Mason, Dr. Abdul Wahid, Dr. Marcos Cruz, Dr. Junlin Lu, Nawazish Ali, Hossein Khaleghy, Mian Shah, Iias Faiud
Webpage Link: https://www.autonomous-agents-research.com/research
Brief Description: This project proposes using Artificial Intelligence methods to effectively integrate renewable generation into dairy farming, by combining it with recent technological developments in the energy sector.
UNSDGs Addressed: 7, 9, 11
Thematic Area: Artificial Intelligence / Machine Learning, Data Science, Smart Infrastructure
Evolving Multi-Objective Robot Swarms (EvoMORS)
Explainable Artificial Intelligence for Bias Detection from Data to Model prediction.
Project Title: Personalised Sensory Regulation: Assessing Biometric Wearable Integration in School-based CUBBIE Sessions
Funding Agency:Data2Sustain EDIH Program June 2025 – May 2026
PI:Dr. Frank Glavin; Dr. Attracta Brennan. Researchers Involved: Damian Gonzalez Garza
Webpage:
Description: This project is a collaboration with Cubbie, a company that develops self-contained, immersive booths that help people, especially those who are autistic or neurodivergent, manage sensory overload by providing a private and calming space. These pods use personalised and adjustable settings like lighting, sound, and visuals to create either a stimulating or calming environment, which can help reduce stress and anxiety. The first phase of this project (June 2025 - November 2025) involved a full data analysis of Cubbies records and planning for biometric device integration. The second phase (December 2025 – May 2026) involves running a pilot study of the integration of a comprehensive biometric wearable in a school setting.
Project Title: Molecular Programming for Designing Bio-molecular Computers
Funding Agency: College of Science & Engineering Strategic (Millenium) Research and Innovation Fund. Sep 2023 – Aug 2025
PI: Principal Investigator
Webpage:
Description:. We are developing a molecular-programming toolbox for the de novo design of DNA hexahexaflexagon (and related context-switchable nanostructures) capable of simple computation. Constraint-aware generative models learn sequence to structure and then structure to function motifs in order to assemble candidates under explicit Watson–Crick pairing and hierarchical self-assembly rules. Designs are triaged in silico using thermodynamic/kinetic analysis and coarse-grained simulation to select foldable, switchable geometries, then fabricated and assessed in vitro by AFM and standard biophysical read-outs to verify state transitions and logic. The project will deliver an open-licence software package and a step-by-step protocol, released as a web service, to standardise and accelerate design–build–test cycles towards diagnostic and therapeutic nanosystems.
Project Title: An Integrated Graph Theoretical Substructure Similarities Searching Algorithm for Drug Repositioning and Off-Target Toxicity Assessments using Antimicrobial Resistance Model
Funding Agency: Ministry of Higher Education, Malaysia – Translational Research Grant. Dec 2022 – Nov 2025
PI:Co-PI.
Webpage:
Description:. We are developing a graph-based 3D substructure-similarity workflow to identify repositionable drugs from approved-drug libraries while flagging probable human off-targets, using antimicrobial resistance as the model system. Binding-site motifs are encoded as residue/atom graphs with explicit geometry and systematically interrogated across bacterial and human proteomes (PDB/AlphaFold) using tolerance-aware subgraph isomorphism. High-scoring candidates are prioritised with lightweight docking and ADMET filters, then progressed to focused experimental validation on curated AMR panels. The project will deliver a reproducible toolkit and web service that broaden discovery beyond exact-match queries while reducing computational cost and turnaround time.
Project Title: Dreamtec Software Ltd T/A Dreamtec System. Innovation Boost powered by Fusion
Funding Agency: InterTradeIreland. Sept 2020 – Mar 2022.
PI:
Webpage:
Description: An 18-month industry–academic project with Dreamtec Systems to build two production-grade capabilities; an analytics engine that turns a proprietary telemetry solution (metered volumes, GPS, timings, routes) into actionable insights—trend analysis, demand forecasting, fleet/productivity KPIs and market dashboards; and then an intelligent, self-learning parser that ingests heterogeneous meter formats without manual templates, enabling faster, more accurate onboarding at scale. Both components will be exposed as APIs and dashboard plug-ins on Dreamtec’s platform to reduce support overheads and unlock upgraded subscription services for customers.
Project Title: Innovative AI Solutions to Support Trustworthy Online Activity
Funding Agency: (Horizon EU) Start/end dates (January 2024 - December 2027)
PI:
Webpage: https://ai4debunk.eu
Description: (Recognizing the persistent and evolving nature of disinformation, AI4Debunk focuses on the symbiotic relationship between humans and advanced AI tools. Our innovative approach involves bridging the sociological aspects of disinformation with concrete AI-based solutions to deter it. Through AI4Debunk, users will gain access to resources, knowledge, and skills, empowering them to detect disinformation in the ever-changing digital landscape. Our priority is to develop user-friendly and inclusive tools to reach individuals of all ages, genders, interests, and online environments.))
Project Title: Charlemont Grant (Royal Irish Academy)
Funding Agency: Royal Irish Academy – Charlemont Grant
Start and End Dates: 25/03/2022 – 25/11/2022 (8 months)
PI/Co-PI: Principal Investigator
Researchers Involved: Dr. Karl Mason, Prof Sabine Hauert
Webpage Link: https://www.autonomous-agents-research.com/research
Brief Description: This project focuses on using multi-objective evolutionary methods to develop controllers for swarm robotics. This research was funded by the Royal Irish Academy and was conducted in collaboration with Prof. Sabine Hauert at the Bristol Robotics Laboratory, UK.
UNSDGs Addressed: 9
Thematic Area: Artificial Intelligence / Machine Learning, Data Science, Computer vision & Robotics