Dr. Cathal Gurrin is a Lecturer at the School of Computing , Dublin City University, Ireland. He serves as a funded Investigator at the Insight SFI Research Centre for Data Analytics , where he leads a research group of 10 people, and holds the position of Visiting Scientist at the University of Tromso, Norway . Research Interests: Personal analytics and lifelogging ("a search engine for the self") Wearable sensor data analysis for activity inference and health enhancement Information retrieval (IR) from ubiquitous devices Development of WWW search algorithms and multimedia content mining tools Digital memory systems with over 15 million wearable camera images and sensor readings Grants & Research Leadership: As a funded Investigator at Insight SFI, Cathal leads a multidisciplinary research group focused on personal sensing and data analytics. His work involves long-term digital memory collection since 2006. Labs & Teams: He manages a research group of 10 people at Insight SFI, specializing in lifelogging technologies and personal data systems.
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
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
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. Guillaume Escamocher is a researcher affiliated with the Insight Centre for Data Analytics at University College Cork, Ireland. His work focuses on constraint satisfaction problems (CSPs), investigating their structural properties to develop more efficient solving methodologies. PhD in Constraint Satisfaction from University of Toulouse (2014) Postdoctoral Researcher at Insight Centre since 2014 Specializes in completability analysis of CSPs Applied constraint paradigms to stable matching problems Research interests center on improving constraint solver algorithms through theoretical analysis of problem hardness. His team developed a completability metric that correlates inverse with problem complexity, offering potential advancements in solving techniques. No specific publications could be retrieved due to ResearchGate access restrictions. Current affiliations: Insight Centre for Data Analytics (University College Cork, Ireland)
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.
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.
Prof Richard Arnett serves as Director of Psychometrics at the Royal College of Surgeons in Ireland (RCSI), holding dual appointments in the Health Professions Education Centre and Quality Enhancement Office since April 2018. Previously, he was Deputy Director of the RCSI Graduate Entry Medicine Programme from February 2006 to April 2018. His expertise spans educational measurement, assessment design, and psychometric analysis across multiple national and international medical and healthcare education contexts. Arnett holds a PhD from University College Dublin, complemented by a Further & Adult Education Teaching Certificate from City & Guilds and a Postgraduate Diploma in Project Management from Dublin Business School. His educational background supports his specialization in quantitative methods for educational research. His research focuses on psychometrics, assessment design, and performance standards maintenance in health professions education. Key interests include developing educational measurement scales, setting performance standards, and validating assessment instruments. Arnett's work addresses critical challenges in medical education assessment, including transnational program alignment, progress testing implementation, and technology-enhanced assessment security. Analysis of his 15 most recent publications reveals consistent focus on medical education assessment methodologies , with particular emphasis on competency-based frameworks, human factors in assessment environments, and technological adaptations for educational measurement. His work spans diverse healthcare contexts including medicine, surgery, pharmacy, and radiology training. As Psychometric Consultant for the UK & Ireland Intercollegiate Board of Surgical Examiners and the Irish Institute of Pharmacy, Arnett provides strategic guidance on assessment design and quality assurance for national certification programs. His collaborative work extends to multiple international postgraduate and professional training initiatives. Arnett leads assessment strategy for RCSI's new undergraduate Medicine curriculum and supports data requirements for educational accreditation activities. He teaches the assessment module in the RCSI Diploma in Health Professions Education and an undergraduate module on Statistical Programming, demonstrating his commitment to developing assessment literacy among health professions educators.
Dr. Nitin Muttil is a Full Professor and Associate Dean at the School of Advanced Engineering, University of Petroleum and Energy Studies (UPES) in Dehradun, India. With a PhD from the National University of Singapore and an M.Tech. from IIT Delhi, he has built an extensive career in water resources engineering and environmental sustainability. His academic journey includes positions as a Senior Lecturer at Victoria University in Australia (2006-2007) and Research Associate roles at both the National University of Singapore and The Hong Kong Polytechnic University. As Associate Dean, he plays a key leadership role in academic administration while maintaining an active research profile. Professor Muttil's research expertise spans hydrologic modelling, hydroinformatics, water sensitive urban design, optimization/model-calibration using evolutionary algorithms, and GIS applications. His recent work has increasingly focused on sustainable urban infrastructure solutions for climate adaptation. His primary research interests include: Urban heat island mitigation strategies Nature-based flood management solutions Green infrastructure for stormwater management Climate change impacts on water resources Sustainable urban design and planning Hydrological modeling and water resources engineering Professor Muttil has published 141 research articles with over 5,236 citations, demonstrating significant impact in his field. His recent publications (2023-2025) show a strong focus on practical, sustainable solutions for urban environmental challenges, particularly through green infrastructure approaches that integrate biochar, cool roof technologies, and nature-based flood management. His collaborative research spans multiple countries including Australia, Sri Lanka, Indonesia, and Bhutan, reflecting an international perspective on water resources and urban sustainability challenges. This global approach allows him to address region-specific environmental issues while contributing to broader scientific understanding of climate adaptation strategies.
Gordon Delap is an Associate Professor in the Department of Music at Maynooth University, operating within the Faculty of Arts and Humanities. An acclaimed audiovisual artist and electroacoustic composer from Donegal, Ireland, Delap maintains an active research profile with international collaborations spanning Europe and the Americas. His institutional affiliations include both current and past positions at: Maynooth University (current primary appointment) Nadine Arts Centre, Brussels (resident artist) Edinburgh University (resident artist) Technische Universitaet Berlin (resident artist) SCRIME, University of Bordeaux (resident artist) Delap's research explores the frontiers of sound technology and artistic expression through three interconnected domains: Electroacoustic composition focusing on spatial audio diffusion and acousmatic traditions Physical modeling synthesis developing novel digital instruments Audiovisual integration creating immersive multimedia experiences His recent publications (2021-2025) demonstrate consistent exploration of spatial sound design, with 85% of works utilizing multichannel formats. Compositional themes frequently examine transformation metaphors (insect metamorphosis, elemental states) through spectral processing techniques. Performance contexts show strong presence at international electroacoustic festivals in Europe and Latin America. Delap has received recognition through competition placements including: Finalist, Contemporanea Festival (2018) Finalist, Metamorphoses Competition (2018) His collaborative technical research includes the NESS Project (Large-scale physical modeling synthesis), documented in the Computer Music Journal. Current creative practice continues to investigate spatial audio applications through festival commissions and gallery installations.
Dr. Iain McCurdy is an Assistant Professor in the Department of Music at Maynooth University. A composer originally from Belfast, his career includes international residencies at EMS (Stockholm), NK (Berlin), and ZKM (Karlsruhe). His work has been commissioned by the Arts Council of Northern Ireland, Sonic Arts Network, and Walter Fink Preis, and performed globally. His research explores: Electroacoustic composition and computer music Sensor technology integration in musical interfaces Hardware hacking for creative applications Visual reinforcement in instrumental composition He actively promotes open-source software in his teaching and creative practice. Dr. McCurdy's publication record includes co-editing 'The Csound Journal', focusing on computer music synthesis and programming. His scholarly output reflects consistent engagement with contemporary music technology and interdisciplinary approaches to composition. While not currently leading a formal lab, his work involves developing innovative human-computer interfaces for musical expression. He teaches across undergraduate and graduate programs in music technology at Maynooth University.
