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Nationally Funded

List of all projects that receive Funding from National bodies 

Project Title: Framework for Transparency in Agentic AI Systems   

  • Funding Agency: Insight Research Ireland (Grant Number 12/RC/2289_P2), Genesys (Industry)
  • Start and End Dates: November 2025 - April 2027 
  • PI: Dr Ihsan Ullah
  • PI/Co PI: John McCrae, Andy Donald, Edward Curry 
  • Researchers Involved: Andy Donald; Atul Kumar Ojha; Edward Curry; Emir Muñoz; Fergal Monaghan; Ihsan Ullah; John McCrae; Talha Iqbal
  • Webpage Link: 
  • Brief Description: This project builds transparency into the system design. Models provide structured justifications using ReAct and CoT traces. A compliance engine checks alignment with regulatory controls during operation. The architecture gives organisations traceable, explainable, and verified AI workflows. It will ensure that new regulations, such as ISO 42001 and the EU AI Act, are followed, which require clear records of decisions, data, and agent interactions. 
  • UNSDGs Addressed: 9
  • Thematic Area: Artificial Intelligence / Machine Learning, Data Science, Knowledge Graphs and Linked Data

Project Title: Machine Learning for Wastewater Treatment Plant Optimisation (Project Acronym: ML-WWTP)

  • Funding Agency: Ward & Burke Construction Ltd. & Insight
  • Start and End Dates: 
  • PI/Co-PI: Coordinator, Project Partner, Individual 
  • Webpage Link: http://jmmcd.net/research.html
  • Brief Description: 
  • UNSDGs Addressed: 6, 13, 14
  • Thematic Area: Smart Infrastructure

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

Project Title: Augmented Reading Room for Radiologists 

  • Funding Agency: Insight Research Ireland Center for Data Analytics
  • Start and End Dates: 2024 – 2025
  • PI: Dr Ihsan Ullah
  • Webpage: 
  • Description:. The objective of this project is to develop a simple platform that enhances the radiologist’s workflow using AR/VR technology. The proposed “Augmented Reading Room” will enable radiologists to directly interact with research-level AI models without requiring complex or time-consuming integration with hospital PACS systems. This platform is not intended to replace or compete with existing diagnostic tools already available in hospital systems. Instead, it aims to complement them by supporting clinical decision-making and adding a crucial layer of interpretability and explainability, which is often lacking in current AI solutions. Ultimately, the AI4MI “Augmented Reading Room” will facilitate and accelerate the adoption of research-level AI models in the field of radiology.

Project Title: Fairness, Scalability and Explainability in Large Language Models  

  • Funding Agency: Insight Research Ireland (Grant Number 12/RC/2289_P2)
  • Start and End Dates: Nov 2023 – Aug 2025 
  • PI: Dr Ihsan Ullah
  • Co PI: Edward Curry, Andy Donald, John McCrae
  • Researchers Involved (Ascending Order): Andy Donald; Apostolos Galanopoulos; Atul Kumar Ojha; Edward Curry; Emir Muñoz; Ihsan Ullah; John McCrae; Manan Kalra; Sagar Saxena; Talha Iqbal
  • Webpage: 
  • Description:. The project focuses on automating the generation of Model, Data, and Dataspace schemas using NLP and RDF mappings for semantic enrichment. It develops a bias detection framework leveraging Transformer models (DistilBERT, RoBERTa, ELECTRA, XLNet) across synthetic and real-world datasets. The research also introduces an open, sustainable data catalogue using CKAN and Airflow to support transparent data management, visualisation, and FAIR AI documentation practices.
  • UNSDGs Addressed: 9
  • Thematic Area: Artificial Intelligence / Machine Learning, Data Science, Knowledge Graphs and Linked Data.  

Project Title: Anatomical Part Segmentation of Newborn Skeleton

  • Funding Agency: Insight Research Ireland Center for Data Analytics
  • Start and End Dates: 2024 – 2025
  • PI: Dr Ihsan Ullah
  • Webpage: 
  • Description:. Accurate and fully articulated modeling of the fetal skeleton plays a crucial role in enhancing childbirth simulations, enabling better prediction of delivery outcomes, and supporting clinical decision-making during labor. However, the collection of high-quality fetal imaging data poses significant challenges due to ethical, technical, and safety constraints. In contrast, neonatal imaging data are more accessible and can serve as a viable proxy for studying and modeling the fetal skeletal structure. This project aims to develop a geometric deep learning framework capable of automatically segmenting and reconstructing the neonatal skeleton from medical imaging data. Ultimately, this work contributes to the broader goal of improving biomechanical simulations of childbirth, facilitating more precise prediction of complications, and paving the way for personalized obstetric care through data-driven modeling of fetal and neonatal anatomy.

Project Title: Agentic Multi-Agent Framework for Chronic Stress Monitoring: A Healthcare Crew  

  • Funding Agency: Insight Research Ireland (Grant Number 12/RC/2289_P2)
  • Start and End Dates: Sep 2024-Feb 2025  
  • PI: Dr Ihsan Ullah
  • Researchers Involved: Dr Talha Iqbal; Edward Curry
  • Webpage: 
  • Description:. The project explores agentic AI for stress monitoring using multi-agent crews, enabling autonomy, real-time adaptation, memory updates for self-improvement, and dynamic thresholding for personalised detection. Discusses transparency, accountability, bias, safety, and ethics challenges. 
  • UNSDGs Addressed: 3
  • Thematic Area: Artificial Intelligence / Machine Learning, Health, Computer Vision and Robotics. 

Project Title: Advanced Machine learning approaches to code similarity measurement   

  • Funding Agency: Research Ireland  
  • Start and End Dates: 01/10/2023 to 30/09/2027  
  • PI/Co-PI: Takfarinas Saber  
  • Researchers Involved: Zixian Zhang  
  • Webpage Link: 
  • Brief Description: Source code similarity measurement, which involves assessing the degree of difference between code segments, plays a crucial role in various
  • UNSDGs Addressed: 
  • Thematic Area:  

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

Project Title: Personalised Sensory Regulation: Assessing Biometric Wearable Integration in School-based CUBBIE Sessions

  • Funding Agency: Data2Sustain EDIH Program
  • Start and End Dates: 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.  
  • UNSDGs Addressed: 17
  • Thematic Area: Artificial Intelligence, Data Science, Health

 

Project Title: Molecular Programming for Designing Bio-molecular Computers

  • Funding Agency: College of Science & Engineering Strategic (Millenium) Research and Innovation Fund.
  • Start and End Dates: 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.
  • UNSDGs Addressed: 3, 9, 12 and 17
  • Thematic Area: AI/ML, Computer Systems, Health

Project Title: URGE, Management of Acute Aortic Syndrome   

  • Funding Agency: Insight Research Ireland (Grant Number 12/RC/2289_P2)
  • Start and End Dates: Sep 2023 - Aug 2024   
  • PI: Prof Sherif Sultan; Prof Osama Soliman 
  • Co-PI: Dr Ihsan Ullah, Venkatesh Kannan
  • Researchers Involved: Dr Talha Iqbal; Yogesh Acharya; Esmael Hamuda; Joseph Parker; Cagri Ayhan; Venkatesh Kannan
  • Webpage: 
  • Description:. This research evaluates ML models for mortality prediction in patients undergoing surgery for acute aortic syndromes. It investigates the impact of small datasets on model performance and explores strategies such as oversampling, feature selection, and other methodological adjustments to enhance predictive accuracy. The project aims to inform clinical decision-making, improve patient outcomes, and provide guidance for future ML studies in cardiovascular surgery
  • UNSDGs Addressed: 3
  • Thematic Area: Artificial Intelligence / Machine Learning, Health, Computer Vision and Robotics. 

Project Title: Cardiovascular Data Analysis Using ML and DL    

  • Funding Agency: Insight Research Ireland (Grant Agreement No SFI/12/RC/2289_P2) Research Ireland Centre for Research Training in AI (CRT-AI) 18/CRT/622 School of Computer Science (Summer Research Fellowship) 
  • Start and End Dates: January 2022 - Ongoing    
  • PI: Dr Ihsan Ullah 
  • Researchers Involved:  Talha Iqbal; Osama Soliman; Sherif Sultan; Ihsan Ullah;  Ayman Abaid; Gianpiero Guidone; Sara Alsubai; Fouziyah Alquahtani; Ruth Sharif; Hesham Elzomor; Emiliano Bianchini; Naeif Almagal; Aaleen Khalid; Michael G. Madden; Faisal Sharif
  • Webpage: https://vil-galway.github.io/
  • Description:. The project applies ML and DL algorithms to multi-modal cardiovascular imaging datasets, including CT, MRI, and echocardiography, to detect and quantify TBAD, arterial plaque, calcium burden, and ejection fraction. It focuses on segmentation, classification, and predictive modelling to identify clinically relevant patterns. The research explores model explainability, reproducibility, and robustness, aiming to provide interpretable outputs that can assist clinicians in decision-making and treatment planning. By combining advanced analytics with high-quality datasets, the study seeks to enable automated, reliable cardiovascular assessment and improve overall diagnostic efficiency.
  • UNSDGs Addressed: 3
  • Thematic Area: Artificial Intelligence / Machine Learning, Computer Vision, Robotics and Health, 

Project Title: Transparency & Explainability in AI

  • Funding Agency: Insight Research Ireland (Grant Number 12/RC/2289_P2).
  • Start and End Dates: Dec 2022 – Aug 2023 
  • PI: Dr Ihsan Ullah
  • Co PI: Edward Curry, Andy Donald 
  • Researchers Involved (Ascending Order): Andy Donald; Apostolos Galanopoulos; Edward Curry; Emir Muñoz; Ihsan Ullah; M. A. Waskow; Manan Kalra; Sagar Saxena; Talha Iqbal
  • Webpage: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=11008609
  • Description:. The project proposed a Semantic Web approach to transform Model Cards, Data Cards and Dataspace Cards into Linked Data, model and dataspace cards which can be of use in knowledge graphs. Introduces a linking vocabulary to connect card ontologies and demonstrates applicability through bias-detection and sentiment/emotion-analysis use cases.
  • UNSDGs Addressed: 9
  • Thematic Area: Artificial Intelligence / Machine Learning, Data Science, Knowledge Graphs and Linked Data

Project Title: Dreamtec Software Ltd T/A Dreamtec System. Innovation Boost powered by Fusion

  • Funding Agency: InterTradeIreland.
  • Start and End Dates: Sept 2020 – Mar 2022.
  • PI: Co-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.
  • UNSDGs Addressed: 8, 9, 12 and 13
  • Thematic Area: AI/ML, Data Science

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

 

Internationally Funded

List of all projects that receive Funding from international bodies

 

Project Title: Enhancing the Quality of Millimetre-Wave Satellite Communications by Space Multiplexing and Innovative Beamforming Matrices Using Gap-Waveguide Technology

  • Funding Agency: ROSETTA (Marie Skłodowska-Curie COFUND and Lero) 
  • Start and End Dates: 01/06/2025 to 30/04/2027 
  • PI/Co-PI: Takfarinas Saber 
  • Researchers Involved: Mohammad Alibakhshikenari  
  • Webpage Link: https://rosetta.lero.ie/dr-mohammad-alibakhshikenari/
  • Brief Description: This fellowship proposal will address the challenges inherent in beamforming matrices (BFMs) in antenna-array systems for practical applications in space multiplexing to enhance the performance of satellite communication networks through millimeter-wave spectrum. Therefore, this project aims to implement advanced BFMs by exploiting the low loss and efficient propagation properties of Gap-Waveguide technology.
  • UNSDGs Addressed: 3, 9, 11
  • Thematic Area: Computing Systems => Networking

Project Title: UAV-APM: UAV-Enhanced Air Pollutants Concentration Monitoring and Prediction 

  • Funding Agency: SyMeCo (Marie Skłodowska-Curie COFUND and Lero) 
  • Start and End Dates: 16/02/2025 to 15/02/2027 
  • PI/Co-PI: Takfarinas Saber 
  • Researchers Involved: Ahmed Moursi  
  • Webpage Link: https://uav-apm.github.io/
  • Brief Description: Air pollution and climate change are major environmental challenges requiring innovative solutions. This project aims to develop a flexible Air Quality Monitoring System (AQMS) using UAV-mounted sensors to collect and predict air pollutant concentrations efficiently. Designed for rapid deployment at events like city fairs, outdoor sports, and industrial sites, the system optimizes data collection by predicting air quality with minimal sensing locations. Building on research in UAV-based sensing, pollutant prediction, and optimized sensor placement, this solution enhances monitoring efficiency, reduces costs, and supports authorities in protecting public health, particularly during short-term pollution spikes.
  • UNSDGs Addressed: 3, 9, 11, 13
  • Thematic Area: Artificial Intelligence / Machine Learning => Autonomous Vehicles  

Project Title: RAPIDE Project   

Project Title: ReMEDy: Responsible DevOps of ML-Enabled Systems  

  • Funding Agency: Lero, Research Ireland Centre for Software 
  • Start and End Dates: 01/09/2023 to 31/08/2027  
  • PI/Co-PI: Takfarinas Saber and Goetz Botterweck 
  • Researchers Involved: Kouider Chadli  
  • Webpage Link: 
  • Brief Description: In this project, we aim to examine the full engineering pipeline of Machine Learning-Enabled Systems (MLES) with its multiple processes and components to identify how trustworthiness requirements could be assessed at each stage--(potentially) across multiple ML components embedded in bigger multidisciplinary software systems. We also aim to lay the foundations for conceptualization and abstraction of the whole MLES DevOps pipeline with the goals of devising model-driven approaches that enable a strategic reasoning as well as a holistic assessment, monitoring, and optimization of efficient and trustworthy MLES over time.
  • UNSDGs Addressed: 8, 9, 10, 11
  • Thematic Area: Artificial Intelligence / Machine Learning => Trustworthy AI  

Project Title: PavAnalytics, An AI Based Pavement Condition Assessment Service 

  • Funding Agency: EU Commission RRF (Grant Number 22/NCF/OT/11220) Insight Research Ireland (Grant Number 12/RC/2289_P2) 
  • Start and End Dates: July 2023-March 2025 
  • PI: Dr Ihsan Ullah, Co-PI: Dr Waqar Shahid Qureshi
  • Researchers Involved: Syed M Haider Shah; Muhammad Hassam Baig; Jeziel Antonio Ayala Garcia  
  • Collaborator: Kathleen Belbonjean (Societal Impact Champion, GortCycleTrail),  Gerard O’Dea, Asset management Officer, TII, David Power, PMS 
  • Webpage: https://www.paveanalytics.eu/ 
  • Award:  https://itag.ie/itag-excellence-awards-2025-shortlist/
  • Description:. The project develops an intelligent sensing and data analytics system for assessing and maintaining walking and cycling infrastructure. Advanced sensors mounted on cycles capture pavement condition data, which is analysed and visualised through a software suite. The system generates standardised pavement condition ratings (PSCI) and supports stakeholder-driven refinement of these ratings. It also explores automation and public participation mechanisms that simplify issue reporting, allowing citizens to flag maintenance problems more easily. 
  • UNSDGs Addressed: 3, 9, 11, 13
  • Thematic Area: Artificial Intelligence / Machine Learning, Computer vision & Robotics, Health, Smart Infrastructure 

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.
  • Start and End Dates:  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.
  • UNSDGs Addressed: 3, 9, 12
  • Thematic Area: AI/ML, Knowledge Graphs and Linked Data, Health

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.
  • UNSDGs Addressed: 16
  • Thematic Area: NLP, Data Science, AI

Project Title: Three Dimensional (3D) Polymorphic DNA Nanocage Assembly using DNA Polyominoes

  • Funding Agency: Ministry of Higher Education, Malaysia – Prototype Research Grant
  • Start and End Dates:  Jan 2018 – Dec 2021
  • PI: Principal Investigator.
  • Webpage:
  • Description:. We developed a prototype platform for three-dimensional polymorphic DNA nanocage assembly using DNA Polyominoes (i.e., blocks that are modular, interchangeable that can self-organise into target supra-structures and can be reconfigured for payload delivery and sensing). The architecture compartmentalised function into a capsule body, sensors and locomotion; in silico optimisation (sequence/shape selection, MD/quantum analyses) guided in vitro fabrication and validation (AFM/EM/NMR). We delivered multiple capsid constructs, a supporting computational tool, and a curated sequence/structure library, with IP planning around fabrication methods and targeted binding. The outcome was a faster, m
  • UNSDGs Addressed: 
  • Thematic Area: