Dr. Suzanne Little is an Assistant Professor in the School of Computing at Dublin City University and an SFI Funded Investigator within the Insight Centre for Data Analytics. She also serves as Co-Director of a research initiative at DCU. Her research centers on multimedia semantics , leveraging artificial intelligence, machine learning, computer vision, and information retrieval to advance content-based media analytics. This work focuses on developing intelligent systems that extract semantic meaning from multimedia content through interdisciplinary computational approaches. As an SFI-funded researcher, Dr. Little contributes to Ireland's national data analytics infrastructure through the Insight Centre. Her Co-Director role demonstrates active leadership in research program development and execution at DCU. She is embedded within the Insight Centre for Data Analytics, a major collaborative research environment driving innovation in data science applications across multiple sectors.
Prof. Mark Keane has served as Chair of Computer Science at University College Dublin since 1998. A cognitive psychology expert with a PhD from Trinity College Dublin, his career spans multiple institutions including the University of London, Open University, Cardiff University, and TCD. He has held leadership roles such as Director of ICT at Science Foundation Ireland (2004-2006) and Director General at SFI (2006-2007), overseeing €700M+ research investments. His work focuses on explainable AI, counterfactual reasoning, and sustainable agriculture applications. BA (UCD), PhD (TCD) in Cognitive Psychology Over 200 publications, H-index 45, 10,800+ citations Key advisor for Ireland's €3.7B Science, Technology & Innovation Strategy His research explores counterfactual explanations , semi-factual reasoning , and XAI in domains like dairy farming and climate resilience. Recent work combines machine learning with cognitive models to improve grass growth prediction and mastitis detection . He also investigates surprise theory through computational models. As Vice-President of Innovation & Partnerships at UCD (2007-2009), he drove academic-industry collaborations. Current affiliations include the Insight Centre for Data Analytics, where he leads AI research teams advancing case-based reasoning and deep learning integrations for explainable systems.
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.
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.
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.
