Michael Madden is the Established Professor and Head of the School of Computer Science at the University of Galway. He founded the Machine Learning Research Group in 2001 and focuses on theoretical advances in machine learning applied to medicine, engineering, and physical sciences. His work includes deep learning with virtual data augmentation, dynamic Bayesian networks for ICU monitoring, and probabilistic analytics for time series analysis. Research Areas: Machine Learning, Algorithms, Bayesian Networks, Reinforcement Learning, Time Series Analysis Applications: Healthcare (ICU monitoring, gene expression), Engineering, Physical Sciences Industry Collaborations: Hewlett Packard Enterprise, Valeo, IBM, University Hospital Galway Scientific Awards 9 publication awards
Dr. Bharathi Raja Chakravarthi is a funded investigator at the Insight SFI Research Centre for Data Analytics and a permanent Lecturer at the School of Computer Science, University of Galway, Ireland. His research focuses on multimodal machine learning, abusive/offensive language detection, bias in NLP tasks, inclusive language detection, and multilingualism. Multimodal Data Analysis Abusive Language Detection NLP Bias Mitigation Dravidian Language Processing He has supervised 33 MSc students and is currently advising 6 MSc and 3 PhD students. His editorial roles include Associate Editor for Expert System with Application (Elsevier) and Editorial Board Member for Computer Speech & Language (Elsevier) . He has served as Area Chair and General Chair for multiple international conferences. Best Application Paper Award at DSAA 2020
Nyo Thiri Aung is a Postdoctoral Researcher at the Insight Centre for Data Analytics, affiliated with the Decision Making RC 6 research group. Her work focuses on interdisciplinary applications of artificial intelligence in vehicular networks, metaverse, and recommender systems. Fields of Interest: Machine Learning, Edge Computing, Metaverse, Blockchain, Vehicular Networks, and Recommender Systems Key Contributions: Pioneering research on deep reinforcement learning for metaverse-edge integration, hybrid recommendation systems in social IoT, and trust management in IoT networks Recent publications highlight her expertise in deploying AI for: Optimizing inference accuracy in distributed environments Developing blockchain-based solutions for vehicular networks Creating personality-aware recommendation systems Advancing 3D medical imaging techniques
Eoghan Cunningham is a PostDoctoral Researcher affiliated with the Insight Centre for Data Analytics, specializing in machine learning and data science applications. His current role involves the ParliView project, which aims to enhance parliamentary-public engagement through computational methods. In 2024, he completed his PhD, focusing on machine learning and network analysis of citation networks. Research Interests: Machine learning algorithms Data science for social impact Network analysis in academic contexts Citation network modeling Contact: Email: eoghan.cunningham@insight-centre.org
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.
Erika Duriakova serves as a Postdoctoral Research Fellow at the Insight Centre for Data Analytics, specializing within the Recommender Systems research group. Her current research centers on pioneering secure decentralised marketplaces for data sharing, integrating her expertise in distributed computing and machine learning to address critical privacy challenges in modern data ecosystems. Her academic credentials include: PhD in Computer Science, University College Dublin, 2018 Duriakova's research spans foundational work in parallel and distributed systems, with significant contributions to scalable graph processing architectures and machine learning applications. Her earlier investigations into distributed recommender systems established frameworks for efficient large-scale recommendation engines, while her current focus on secure data marketplaces explores cryptographic techniques and decentralised protocols to enable trustworthy data exchange without compromising user privacy. This trajectory demonstrates a consistent emphasis on solving scalability bottlenecks in data-intensive computing environments through innovative system design. Within the Recommender Systems research group, she collaborates on advancing algorithmic approaches that balance personalization with ethical data handling, contributing to the Centre's mission of developing human-centric data analytics solutions. No scientific awards, student mentorship records, or grant funding details are documented in the available materials.
Dr. Ramen Ghosh is a Researcher in the Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE) group at Atlantic Technological University, Sligo, where he has been working with Dr. Marion McAfee since July 2022. His research explores how complex systems behave when randomness, interaction, learning, and control intersect, with special focus on ergodicity principles. His academic background includes: PhD in Electrical Engineering, University College Dublin, Ireland (2018-2023) Master of Technology in Mathematics and Computing, Indian Institute of Technology Patna, India Master of Science in Mathematics, Chennai Mathematical Institute, India Bachelor of Science in Mathematics (Honours), Ramakrishna Mission Vidyamandira Belur Math, University of Calcutta, India Dr. Ghosh's research centers on ergodicity—the concept that a system's long-run behavior becomes independent of its initial state—and how this principle can fail, emerge, or be shaped through control and learning. His work spans nonlinear systems, iterated function systems, dynamic mode decomposition, and applications in power grids, environmental systems, and materials science. He approaches complex system behavior through the intersection of randomness, interaction, learning, and control mechanisms. His publication record shows consistent growth with 1 article in 2022, 4 in 2023, 1 in 2024, and 1 in 2025. His research demonstrates strong interdisciplinary connections between theoretical mathematics and practical applications across environmental science, materials engineering, and control systems. The fingerprint analysis of his work reveals significant contributions to Nonlinearity, Iterated Function Systems, Dynamic Mode Decomposition, and Stable State mathematics. Dr. Ghosh has teaching experience including MATH09010 - Introduction to Mathematical and Computational Modelling at Atlantic Technological University (2022), and multiple offerings of EEEN30150-Modelling and Simulation and EEEN30020-Circuit Theory at University College Dublin (2019-2021). His research activities include presentations on ergodicity, predictability, fairness, and control for societal-scale challenges.
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.
Dr. Saritha Unnikrishnan serves as a Lecturer in Computing and Principal Investigator in AI-driven Computer Vision at Atlantic Technological University (ATU) Sligo, Ireland. She maintains multiple research affiliations across the institution, including the Health and Biomedical Research Centre (HEAL) , the Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE) , and the Precision Engineering Materials and Manufacturing Research Centre (PEM Research Centre) . Dr. Unnikrishnan's research spans computer vision , medical imaging , and artificial intelligence applications with significant focus on healthcare and industrial quality assessment. Her work demonstrates strong interdisciplinary connections between computer science, biomedical engineering, and pharmaceutical sciences, particularly in the areas of micrograph analysis , brain tumor characterization , and AI-driven diagnostic solutions . Analysis of her recent publications reveals a clear trajectory toward applying AI techniques to solve complex problems in medical imaging and industrial applications. Her work increasingly focuses on deep learning approaches for image analysis, with notable contributions in glioma characterization , emulsion stability assessment , and educational technology solutions . Ireland's National AI Challenge 2024 award recipient Dr. Unnikrishnan has demonstrated exceptional grant acquisition capabilities, securing over €2 million in research funding to lead multiple national and EU projects. She has led major enterprise-funded AI research initiatives, including an AI-enabled computer vision solution licensed to GSK . Her collaborative work extends to European COST Actions and cross-border AI initiatives, highlighting her significant impact in the European research landscape. As Principal Investigator across multiple research centers at ATU Sligo, Dr. Unnikrishnan directs work in the Health and Biomedical Research Centre, the Mathematical Modelling and Intelligent Systems for Health and Environment initiative, and the Precision Engineering Materials and Manufacturing Research Centre, where she bridges computer science with practical healthcare and industrial applications.
JIA Xibin serves as a full Professor and doctoral/master's thesis supervisor at Beijing University of Technology's Faculty of Information Technology and Dublin International College. She holds editorial responsibilities for the TIIS journal and maintains active memberships in the China Computer Federation (CCF) and China Society of Image and Graphics (CSIG), including specialized committees for Machine Vision and Big Video Data. Her educational foundation spans a B.S. in Wireless Technology from Chongqing University (1991), M.S. in Measuring and Testing Technology from North University of China (1996), and Ph.D. in Computer Application Technology from Beijing University of Technology (2007). International experience includes visiting scholar positions at University of California Riverside (2015) and Flinders University (2009). Research focuses on intelligent medical imaging for liver disease diagnosis, affective computing in educational contexts, and cognitive behavior modeling through multimodal fusion techniques. Her methodology integrates representation learning with transfer and few-shot learning paradigms to address data scarcity in medical applications. Current publications demonstrate consistent focus on domain adaptation and medical image analysis , with significant contributions to multimodal MRI interpretation for non-alcoholic fatty liver disease and hepatocellular carcinoma. Her work bridges theoretical machine learning with clinical applications through deep neural network architectures. Active research leadership includes principal investigator roles for: National Natural Science Foundation grant on non-invasive liver disease assessment (2019-2022) Beijing Natural Science Foundation project on campus safety risk prediction (2020-2022) These projects emphasize big data analytics for healthcare and educational safety systems, reflecting her dual expertise in technical innovation and practical implementation.
Dr. Pinar Avsar serves as a Lecturer and Programme Director at the School of Nursing and Midwifery at the Royal College of Surgeons in Ireland (RCSI), where she leads academic programs and conducts research through the Skin, Wounds and Trauma Research Centre (SWaT). Her academic journey includes a PhD in Nursing from Gazi University Graduate School of Health Sciences, complemented by Master's and Bachelor's degrees in Nursing from Turkish institutions. Dr. Avsar's research expertise centers on pressure ulcer prevention, with particular focus on physiological differences related to skin tone, technology-enhanced risk assessment, and patient-centered care approaches. Her work bridges clinical practice and academic research, addressing critical gaps in wound care through systematic reviews, clinical trials, and innovative technology applications. She has published extensively on topics including pressure ulcer prevention strategies, patient experiences with chronic wounds, and the impact of skin tone variations on wound assessment. Her publication portfolio demonstrates a clear trajectory toward more sophisticated assessment methods, with increasing emphasis on technology integration (subepidermal moisture measurement, thermography, AI) and health equity considerations (particularly regarding skin tone assessment). Recent work shows growing attention to patient-centered outcomes and the practical implementation of research findings in clinical settings. Dr. Avsar has successfully secured competitive funding, including Science Foundation Ireland grants focused on preventing facial pressure ulcers among healthcare staff during the COVID-19 pandemic. She actively supervises research students and contributes to professional development through workshops and masterclasses on pressure ulcer prevention. Her professional activities extend to international collaborations and contributions to systematic reviews that inform clinical practice guidelines. She maintains active involvement in multiple research clusters focusing on Nursing & Midwifery and Population Health, with particular alignment with UN Sustainable Development Goal 3 for Good Health and Well-Being.
Dr. Hannah Wilson is a StAR Research Lecturer at the Royal College of Surgeons in Ireland's School of Nursing and Midwifery, where she leads the Skin Wounds and Trauma Research Team. She joined RCSI in October 2023 following her PhD completion at the same institution. Her clinical background in cardiology nursing informs her research on tissue viability and patient-centered care. Education: PhD, Royal College of Surgeons in Ireland (2020-2023) MSc Nursing, Royal College of Surgeons in Ireland (2017-2018) Postgraduate Diploma in Nursing, RCSI (2016-2017) BSc Adult Nursing, Queen's University Belfast (2012-2015) Her research focuses on three interconnected domains : 1) Advanced technologies for early pressure ulcer detection (including AI and subepidermal moisture scanning), 2) Physiological differences in skin injury response across diverse populations, and 3) Patient involvement frameworks for equitable wound prevention. This work aligns with UN Sustainable Development Goal 3 (Good Health and Well-Being) through its focus on reducing healthcare disparities. Dr. Wilson's recent publications demonstrate a strong emphasis on systematic reviews and clinical validation studies. Her 2024-2025 articles predominantly explore technological innovations in wound assessment, while her 2025 works focus on implementation science and diversity in healthcare research. Recurring themes include guideline implementation, skin physiology, and patient engagement methodologies. Active Grants: Evaluation of Standards for Advanced Nursing Education (NMBI, 2025-2026) Voices that Matter: Patient Partnerships in Nursing Research (RCSI PPI Ignite Network, 2024-2025) The Skin Wounds and Trauma Research Team develops evidence-based protocols for wound prevention and partners with clinical institutions to translate research into practice. Current projects examine subepidermal moisture correlations with ultrasound biomarkers and machine learning applications for diabetic foot ulcer prediction.
Hamail Ayaz serves as an Assistant Lecturer in Computing at Atlantic Technological University (ATU) Sligo, specializing in AI-driven computer vision while concurrently completing his PhD in the School of Computing at ATU, where his research focuses on eXplainable AI applications for diagnosing glioma brain tumours. His educational background includes: Bachelor of Computer Science (with distinction) from COMSATS University Islamabad Master of Computer Engineering investigating advanced imaging protocols and generative adversarial networks for food processing applications Hamail's research bridges computer vision and artificial intelligence with practical healthcare diagnostics and industrial applications, demonstrating particular expertise in medical imaging analysis and machine learning implementation across diverse domains including neuro-oncology and food technology sectors. He actively contributes to the STEM Passport initiative promoting women in STEM fields across Ireland, with his scholarly impact evidenced through publications in high-impact journals despite no specific advising roles or research grants being documented in available sources. As an integral member of ATU Sligo's School of Computing research ecosystem, Hamail collaborates on advancing AI-driven computer vision solutions while maintaining strong connections to both Pakistani academic roots and Irish technological innovation initiatives.
