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Patrick Healy serves as an Associate Professor in the Department of Computer Science & Information Systems within the Faculty of Science and Engineering at the University of Limerick. He is an active member of Lero – the Irish Software Research Centre , contributing to Ireland's national software research initiatives. His research spans Information Visualization, Graph Drawing, Combinatorial Optimization, and Routing/Scheduling problems. He specializes in developing algorithms for graph layout, table formatting, and upward planarity testing, with recent work expanding into machine learning robustness, medical AI diagnostics, and occlusion handling in computer vision. His fingerprint analysis reveals deep expertise in digraph theory (100%), planarity (76%), edge optimization (71%), and combinatorial optimization (51%). Current research trends show a significant shift toward applied AI since 2022, with 12 of his 15 most recent publications focusing on neural network robustness, medical diagnostics, and data augmentation techniques. Earlier work established foundations in graph theory and document engineering. He has supervised numerous research projects through Lero and maintains active collaborations across European institutions, particularly in software engineering and AI applications. His laboratory work centers on the Visualisation and Algorithm Design Group at UL, focusing on interpretable AI systems and robust visualization frameworks.

• Information Visualization
• Graph Drawing
• Combinatorial Optimization
• +3 more

Nikola Nikolov is an Associate Professor in the Department of Computer Science & Information Systems at the University of Limerick. He holds memberships in the Centre for Research Training in Foundations of Data Science, the Data-Driven Computer Engineering Research Centre, and Lero – the Irish Research Centre for Software, reflecting his deep integration into Ireland's national research infrastructure. His research spans Machine Learning, Natural Language Processing, and Graph Drawing, with specialized expertise in deep learning architectures, matrix factorization techniques, and feature extraction methodologies. Current work focuses on multilingual NLP applications for social media analysis, convolutional neural network optimizations, and collaborative filtering systems, demonstrating consistent innovation across computational linguistics and graph theory domains. Analysis of his 2023-2025 publications reveals a dominant focus on combating online toxicity through advanced NLP systems for racism and hate speech detection across multiple languages, alongside significant contributions to computer vision efficiency and recommendation system architectures. His work bridges theoretical advancements with real-world applications in healthcare analytics and autonomous systems. Prof. Nikolov actively contributes to UN Sustainable Development Goals through data science applications while maintaining strong industry connections via research centres focused on software innovation and data-driven engineering solutions.

• Machine Learning
• Natural Language Processing
• Graph Drawing
• +5 more

Muhammad Haris Kaka Khel is a part-time lecturer at Atlantic Technological University's Department of Computing in Ireland while pursuing his Ph.D. in pedestrian trajectory prediction. His research focuses on developing adaptive systems that analyze human behavior, social interactions, and environmental dynamics to forecast multiple possible pedestrian paths using the PATHS framework. He works under supervisors Dr. Kevin Meehan and Dr. Paul Greaney at ATU Donegal, with additional guidance from Dr. Marion McAfee (ATU Sligo) and Dr. Sandra Moffet (Ulster University). B.S. in Electrical Engineering (Communication specialization) from UET Peshawar, Pakistan (2019) Research master's at University of Kuala Lumpur, Malaysia (completed 2022) with Erasmus exchange experience at Politecnico di Torino, Italy Ph.D. candidate at Atlantic Technological University (expected completion 2026) Research Interests Khel's work addresses critical gaps in pedestrian trajectory prediction by modeling multiple possible paths rather than single deterministic outcomes. This approach enhances robustness in dynamic environments like urban streets and public spaces, with applications in: Autonomous vehicle navigation safety Urban planning and walkability improvements Event crowd management systems Smart surveillance for anomaly detection Accessible navigation for visually impaired Public transportation optimization Teaching & Collaboration As a part-time lecturer at ATU Donegal, he contributes to computing education while maintaining external collaborations including: Sponsored research with Saudi Arabia's Deputyship for Research and Innovation Graduate research assistant role at University Kuala Lumpur-British Malaysian Institute Former intern at Center of Intelligent Systems and Networks Research

• Pedestrian Trajectory Prediction
• Crowd Monitoring Systems
• Computer Vision
• +6 more

Pepijn Van De Ven is a Professor in the Department of Electronic and Computer Engineering at the University of Limerick, with affiliations to multiple research centers including the Centre for Research Training in Foundations of Data Science, Data-Driven Computer Engineering Research Centre, and Lero – the Research Ireland Centre for Software. His work bridges artificial intelligence with healthcare applications, particularly focusing on mental health interventions, mobile app development, and ambient assisted living technologies. PhD in AI Techniques for Marine Vehicles (UL, 2005) MSc in Electronic Engineering (Eindhoven University of Technology, 2000) His research interests center on leveraging machine learning and mobile ICT to enhance mental health care, with a specific emphasis on eMental Health, treatment prediction models, and context-aware intervention systems. He also explores computer vision applications in waste classification, medical imaging, and Industry 4.0 safety systems. The trends in his recent publications highlight interdisciplinary work combining computer vision (e.g., instance segmentation, contamination detection), mental health (e.g., depression, diabetes comorbidity), and AI-driven clinical tools (e.g., interpretable models, Bayesian analysis). Subfields like ecological momentary assessment, graph-based learning, and human-AI trust mechanisms are recurrent. Pepijn has contributed to teaching through modules on Android programming and has held leadership roles such as Course Leader for the National MSc in Artificial Intelligence at UL and Past-President of the International Society for Research on Internet Interventions (ISRII). He leads significant research projects including the PROACTIVE (UK-MRC), PERSONAE (Innovation Fund Denmark), and BINSPECT (Science Foundation Ireland) grants. Current research group: D2iCE (Design, Intelligence, and Computer Engineering) Collaborations: King’s College London, University of São Paulo, University of Bristol

• Machine Learning
• Artificial Intelligence
• eHealth
• +7 more

Paul Greaney is a Lecturer in Computing at Atlantic Technological University, affiliated with the Department of Computing. He is a Principal Investigator in the Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE) research unit. His work aligns with UN Sustainable Development Goals, particularly in advancing technologies for environmental and health applications. Greaney holds an h-index of 38 with over 2025 citations, reflecting his research impact. Research interests span machine learning, artificial intelligence, IoT security, and materials science. His recent projects include pedestrian trajectory prediction using neural networks, resume parsing with BERT-based models, and IoT trustless environments. He has also contributed to studies on dielectric membranes and quantum dot cytotoxicity. Greaney’s publications emphasize interdisciplinary approaches, blending computational methods with real-world applications. His work on spatial-temporal attention models and generative frameworks demonstrates expertise in AI-driven solutions for complex systems. No advising or grant details are provided in the text. He is part of the MISHE team, focusing on mathematical modeling and intelligent systems for societal challenges.

• Machine Learning
• Artificial Intelligence
• IoT Security
• +5 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.

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

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.

• Distributed Systems
• Graph Processing
• Machine Learning
• +2 more