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
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.
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. 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.
