Find Academic Supervisors

Yuanyuan PuView profile

Academic

• Food Science
• Food Engineering
• Spectral Sensing Technology
• +4 more

Dr. Yuanyuan Pu is an Assistant Lecturer in the Department of Land Sciences at South-East Technological University. She holds a PhD in Biosystems and Food Engineering from University College Dublin (2017) and completed postdoctoral research at Teagasc Moorepark Food Research Center (2018–2019). She was awarded a Marie Skłodowska-Curie Fellowship (2020–2023), during which she conducted research at the University of Cordoba and collaborated with an Irish dairy company. She currently manages the Marie-Curie Alumni Association-Ireland Chapter. Her research focuses on spectral/imaging sensing technologies combined with machine learning for food quality analysis, process analytical technology (PAT) in food processing, and green technologies like microwave vacuum drying. She also specializes in predictive modeling and data visualization. Her work aligns with UN SDGs related to food security and sustainable consumption. Research Trends: Recent publications emphasize deep learning applications in seed phenotyping, multispectral techniques for citrus quality, and sensor development for dairy processing. Her work bridges academia and industry, addressing challenges in food safety and efficiency. Awards: Marie Skłodowska-Curie Fellowship (2020–2023) Teaching & Projects: Teaches courses on food processing technologies and project management. Leads projects integrating PAT tools and sustainable processing methods. Collaborates with industry partners for technology transfer. Labs/Teams: Involved in interdisciplinary teams at Teagasc, University of Cordoba, and Irish dairy industries.

Patricia BoweView profile

Lecturer

• Higher Education Assessment
• Student Learning Strategies
• Taxation
• +3 more

Dr. Patricia Bowe is Head of the Department of Management and Organisation at SETU's School of Business since September 2021. With 25+ years of academic experience, she lectures in Accounting and Taxation at undergraduate and postgraduate levels. Her expertise includes corporate governance (MSc from Bournemouth University, 2001) and educational assessment (EdD from University of Sheffield, 2015). Professional qualifications: ICSA and ACCA certifications Current teaching: Taxation (L8/L9) and Reflective Research Practice (L10-DBA) Research focuses on transforming higher education assessment practices to promote 'deep learning' strategies, addressing critical gaps between secondary and tertiary education methodologies. Her work aligns with UN SDG 4 (Quality Education), emphasizing graduate employability through pedagogical innovation. Led institutional initiatives to modernize assessment frameworks, advocating for active learning approaches over traditional rote methods. Her scholarship challenges conventional educational paradigms to better prepare students for professional environments.

• Artificial Intelligence
• Natural Language Processing
• Deep Learning
• +2 more

Chanda Hirway is an Assistant Lecturer at Technological University of the Shannon's Department of Computer and Software Engineering. Her research focuses on developing AI-driven solutions for email analysis and consumer communication processing. Current projects employ generative AI and ensemble learning methods to improve intent classification in truncated communications and receipt identification systems. Hirway's technical approaches include LSTM networks, transformer models (GPT-Neo, GPT-2), and deep learning architectures designed to minimize false negatives in email classification. Her work has applications in retail analytics and consumer behavior research.

• Wireless Sensor Networks
• Machine Learning
• Deep Learning Methods
• +5 more

Shagufta Henna is a Lecturer in Computer Science at the Department of Computing, Atlantic Technological University. Her research focuses on machine learning applications in wireless sensor networks, IoT systems, cybersecurity, and environmental monitoring. She contributes to UN Sustainable Development Goals through projects addressing environmental sustainability and health. Her expertise includes federated learning for malware detection in healthcare IoT, deep learning optimization for sensor networks, explainable AI for water quality prediction, and ensemble methods for human activity recognition. Recent work highlights include: Developing graph neural networks for IoT-based healthcare cybersecurity Accelerating environmental monitoring via deep learning in uncontrolled sensor networks Deploying SHAP-based XAI for water quality prediction interpretability No scientific awards explicitly listed. Research output includes 45 publications across journals and conferences, with recent contributions in Results in Engineering and IEEE proceedings. Active collaborations span international institutions focusing on AI-driven environmental and healthcare solutions.

Shane GilroyView profile

Lecturer

• Autonomous Vehicles
• Image Sensors
• Automotive Engineering
• +3 more

Shane Gilroy is a Lecturer in Electronic Engineering at the Department of Mechatronic Engineering, ATU Sligo. His research focuses on automotive systems, particularly autonomous vehicles, image sensor technology, and object detection challenges in occluded environments. He actively contributes to improving road safety through advancements in pedestrian detection, sensor validation, and cybersecurity in connected vehicles. His work aligns with UN Sustainable Development Goals, emphasizing innovation in urban mobility and sustainable infrastructure. Gilroy’s expertise spans computer vision, robotics, and automotive engineering, with notable contributions to occlusion classification methods and lifecycle validation of automotive components. He is currently accepting PhD students to further explore these interdisciplinary areas. Key research themes include: Autonomous vehicle perception systems Occlusion challenges in road safety Image sensor performance optimization Cybersecurity for connected vehicles Lean manufacturing methodologies His publications demonstrate a strong emphasis on practical applications, from improving pedestrian detection algorithms to evaluating sensor reliability under real-world conditions. Recent work explores urban mobility solutions and the integration of machine learning models for occlusion analysis.

Helen McAllisterView profile

Professor

• Embroidery
• Textile Arts
• Material Culture
• +6 more

Dr. Helen McAllister is a Professor and Head of the Department of Applied Material Cultures at the National College of Art and Design (NCAD) in Dublin, Ireland. With a PhD in Practice-based Design (Embroidered textiles) from NCAD, she has established herself as a leading figure in textile arts, material culture, and craft education. Her academic leadership spans from 2014-present as Head of Applied Material Cultures, following her role as Head of Fashion and Textiles from 2009-2014. McAllister's research focuses on embroidery and textile practices, particularly exploring shoe-derived forms, historical Venetian textiles, and the narrative potential of crafted artifacts. Her work bridges design processes with material culture studies, examining symbolism, metaphor, and the interdisciplinary nature of maker disciplines. She investigates how crafted artifacts engage viewers and transform across different contexts and audiences. Her scholarly output demonstrates consistent engagement with textile innovation, craft education, and material culture. McAllister's publications and conference presentations reveal a trajectory from technical textile exploration toward broader considerations of social equality, cultural heritage, and the future of craft education. Her research shows particular interest in Venetian historical textiles, the symbolism of footwear, and the educational dimensions of material practice. 2002: The Golden Fleece Award | Overall Winner 1986: Taylor Award, RDS Craft Award 2011: An Post | Crafts Council of Ireland Collaboration Stamp Collection 2012: Innovation Voucher | Polio Society Ireland Throughout her career, McAllister has served as an external examiner for multiple institutions including UL Limerick, St. Anne's College Sligo, and G.M.I.T. Galway. She has curated significant exhibitions like the Equality – Invisibility Series (2020) which raised funds for ALONE charity during the pandemic. Her professional activities demonstrate deep commitment to advancing textile arts both academically and within the broader cultural sector.

• Deep Learning
• Wireless Sensor Networks
• Environmental Monitoring
• +3 more

Asif Yar, PhD, is a researcher in the Department of Computing, focusing on deep learning, wireless sensor networks, and environmental monitoring. His work bridges machine learning and IoT technologies to address real-world challenges in sensor calibration and data accuracy. Research Themes : Deep learning methods, wireless sensor networks, extreme learning machines, environmental monitoring, and distributed optimization. Collaborations : Active in interdisciplinary research with affiliations in IIoT edge environments and air pollution monitoring systems. Citations : 1 citation (Scopus), with a h-index of 1. His recent publications highlight advancements in self-calibration algorithms for large-scale wireless sensor networks using deep learning and attention-based GRUs, alongside novel approaches like temporal fusion transformers for air pollution monitoring in IIoT edge environments. He also explores distributed optimization frameworks using Apache Spark for scalable systems. Asif Yar is currently accepting PhD students and contributes to the academic community through peer-reviewed conference publications and collaborative research projects.

• Transportation Engineering
• Intelligent Transportation Systems (ITS)
• Infrastructure Resilience
• +7 more

Dr. Amirreza Kandiri is a Postdoctoral Research Fellow at the School of Civil Engineering, University College Dublin (UCD), where he conducts advanced research in transportation engineering, intelligent transportation systems (ITS), and infrastructure resilience. He earned his PhD at UCD, focusing on AI-driven traffic prediction and optimization under variable speed limits. He also holds an MSc in Construction Management and a BSc in Civil Engineering. Education: PhD, University College Dublin MSc, Islamic Azad University BSc, Buali Sina University His research centers on the application of machine learning and data analytics to solve critical challenges in civil and transportation engineering. He specializes in real-time traffic management, smart mobility, structural materials modeling, and remote sensing for infrastructure monitoring. His work integrates predictive modeling, optimization, and uncertainty analysis to enhance safety, efficiency, and sustainability in urban systems. The recent publications of Dr. Kandiri reflect a strong interdisciplinary trend combining civil engineering with artificial intelligence. His work spans intelligent traffic control using autonomous agents, deep learning for InSAR data analysis, and machine learning models for predicting concrete properties. These studies highlight a consistent focus on optimization, model accuracy, and real-world applicability across transportation and construction domains. Scientific Awards: 2024 Dean’s Award, UCD STEM Early Career Researchers’ Symposium Recognized for one of the most cited papers in Applied Sciences Dr. Kandiri has been actively involved in academic service, including peer reviewing for journals and serving on the UTSG 2025 organizing committee. He has contributed to teaching at both University College Dublin and Chang’an University in China. While no formal students are listed, his collaborative research involves multiple co-authors across disciplines. He has not received mention of grants, but his research output suggests involvement in funded projects related to smart infrastructure and sustainable materials. His work is closely tied to real-world applications, particularly on the M50 motorway in Dublin, and leverages platforms like Google Scholar, ResearchGate, and ORCID (0000-0001-8683-7984) for dissemination. He is part of a growing collaboration network in transportation and structural engineering research.

• Structural Biology
• NMR Spectroscopy
• Antimicrobial Peptides
• +7 more

Associate Professor Chandralal Hewage is a structural biologist at the School of Biomolecular and Biomedical Science, University College Dublin, with over 30 years of expertise in NMR spectroscopy. His research focuses on biologically active peptides for diabetes therapy, antimicrobial drug development, and liver disease metabolic studies. Research Interests: Structural analysis of diabetes-related peptides Design of GIP and endothelin receptor drugs NMR-based antimicrobial peptide studies Liver cell metabolic profiling in disease states Development of computational peptide modeling tools Scientific Contributions: Organizer of international conferences EUROMAR 2012 and ICMRBS 2018. Author of >100 publications. Developer of bioinformatics tools APPTEST and ENNAVIA for peptide structure/activity prediction. Professional Affiliations: ICMRBS Council Member European Peptide Society Council Member Chairman of Irish Peptide Society

• Genetic Programming
• Evolutionary Algorithms
• Combinatorial Optimisation
• +6 more

Dr. Edgar Galván is an Associate Professor in the Department of Computer Science at Maynooth University's Faculty of Science & Engineering. He is a leading expert in Genetic Programming (GP) and Evolutionary Algorithms, with a focus on semantic-based approaches, neutrality, and multi-objective optimization. His work spans applications in combinatorial optimization, gaming (e.g., Carcassonne), and software engineering, including neuroevolution for deep learning architectures. Current Affiliation: Maynooth University Previous Roles: Senior Researcher at University College Dublin, Trinity College Dublin, and INRIA Paris-Saclay Research interests include: Semantic-based Genetic Programming Multi-objective Evolutionary Algorithms Monte Carlo Tree Search Circular Economy Applications Privacy-Preserving Optimization Neuroevolution in Autonomous Systems His recent publications analyze semantic diversity in GP, neural architecture search, and privacy-aware swarm optimization. Key awards include being ranked among the top 1% of GP researchers by University College London (2020), a Marie Curie Fellowship (2014), and a Best Paper Award at ECTA 2015. Current Projects: REBUILD (Circular Economy Buildings, 2024-2027), VISION (Circular Business Models, 2023-2026) Previous Grants: Stochastic Bio-inspired Algorithms (2014-2017, €267k), circAI (2022-2023, €142k) Dr. Galván serves on program committees for IEEE, ACM, and Springer conferences, and as Scientific Adviser for institutions in Ireland, France, and Mexico. His work bridges theoretical GP analysis with real-world applications in energy optimization and AI.

Jim BuckleyView profile

Professor

• Software Engineering
• Software Evolution
• Artificial Intelligence for Software Engineering
• +5 more

Jim Buckley is a Professor in the Computer Science and Information Systems Department at the University of Limerick, Ireland, and a Principal Investigator in Lero. He leads the ARC research group focused on software evolution and legacy system modernization, with significant industry collaborations including Huawei, IBM, and Fidelity. Education: BSc in Biochemistry, University of Galway (1989) MSc in Computer Science, University of Limerick (1994) PhD in Computer Science, University of Limerick (2002) Research Focus: His work centers on AI-enhanced software engineering (AI4SE/SE4AI), featuring breakthroughs in clone detection, software architecture evaluation, and feature location. He has developed industry-adopted tools for software comprehension and evolution, with recent emphasis on explainable AI (XAI) and scalable neural network applications for industrial codebases. His research consistently bridges academic rigor with industrial implementation. Publication Trends: Recent articles (2022-2025) reveal a dominant shift toward AI-driven software engineering solutions, particularly in clone detection and architecture recovery. Key themes include industrial scalability, developer experience optimization, and responsible AI integration, with strong representation in top-tier venues like IEEE Transactions and ACM Computing Surveys. Scientific Awards: No awards specified in source material. Grants & Industry Impact: Leads the Huawei-funded TREES Programme and maintains active partnerships with 9+ companies. His research has yielded licensed tools (e.g., for legacy system evolution), two IP assignments from LLM-based clone detection work, and practical frameworks adopted by seven Irish enterprises. Research Infrastructure: Directs the ARC group within Lero, which combines academic researchers and industry practitioners to address real-world software maintenance challenges through empirical studies and tool prototyping.

• Machine Learning
• Natural Language Processing
• Network Analysis
• +3 more

Dr. Derek Greene is an Assistant Professor at the School of Computer Science, University College Dublin, and a Funded Investigator at the Insight Centre for Data Analytics and the VistaMilk Research Centre. His research spans machine learning, natural language processing, and network analysis, with a focus on interdisciplinary applications in cultural analytics, smart agriculture, and political science. Dr. Greene has published over 60 research papers at international conferences and journals. His work includes developing methods for natural language processing , network analysis , and deep learning applied to diverse domains such as literary text mining, dairy industry monitoring, and political communication analysis. He leads projects integrating machine learning into cultural analytics, enhancing agricultural practices, and modeling policy agendas. The articles in his Google Scholar profile highlight a trend toward explainable AI , synthetic data generation , and network-based modeling . Key sub-fields include counterfactual explanations , transformer-based frameworks , temporal analysis of historical texts , and interdisciplinary knowledge transfer . His work bridges machine learning with applications in cultural studies , agriculture , and political science . Dr. Greene collaborates with institutions like the Insight Centre for Data Analytics and the VistaMilk Research Centre , integrating academic research with industry and policy needs. His funded investigator roles reflect ongoing support for applied research in data analytics and agricultural technology.

Suchana DattaView profile

Research Fellow

• Information Retrieval
• Natural Language Processing
• Digital Humanities
• +3 more

Suchana Datta is a Research Fellow at the Insight Centre for Data Analytics . Her work bridges Information Retrieval and Artificial Intelligence , with a focus on query performance prediction (QPP) and causality-driven retrieval . She has contributed extensively to improving neural ranking models through relevance feedback and reproducibility studies. Research Highlights Developing novel frameworks for supervised and unsupervised QPP using deep learning and hybrid feature integration Advancing causality modeling in information retrieval systems Exploring temporal trends in 19th-century literature through computational analysis Pioneering work in cloud forensics with dynamic forensic frameworks Publication Trends Her recent work (2022-2025) emphasizes neural QPP models , reproducibility in IR experiments, and causality detection in query events. Earlier contributions (2016-2020) focus on cloud forensic frameworks like DCF and causal analysis in retrieval systems.

Kevin MeehanView profile

Lecturer

• Computer Vision
• Machine Learning
• Ubiquitous Computing
• +3 more

Dr. Kevin Meehan serves as a Lecturer in Computing at Atlantic Technological University (ATU) in Ireland, holding dual roles as Principal Investigator for both WisarLab and the Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE). His academic foundation includes a BSc Hons and PhD in Computer Science from Ulster University, complemented by an MA in Learning & Teaching and a PGCE in Further and Higher Education. His research spans Computer Vision , Machine Learning , and Ubiquitous Computing , with significant contributions in trajectory prediction, immune response modeling, and environmental monitoring systems. Recent publications demonstrate expertise in BiLSTM networks, graph neural networks, and DenseNet applications for real-world problem solving. Notable professional recognition includes Fellowship in the Higher Education Academy. His work aligns with UN Sustainable Development Goals through technological solutions for health and environmental challenges. As an educator, he teaches Machine Learning, Computer Vision, and Data Science while leading industry collaborations with over 45 SMEs. His research has secured €400,000+ in funding for knowledge transfer projects, demonstrating strong industry-academia linkage.