Dr. Andrew McCarren is an Associate Professor and Head of the School of Computing at Dublin City University (DCU). He holds a PhD and BSc from DCU and is a funded investigator in the Insight Centre for Data Analytics. His research focuses on applying data analytics to Fintech, Agriculture, Health, and Sports Performance. As a former industry professional with 20+ years experience in Agri, Engineering, and Pharmaceuticals, he bridges academic and industrial collaboration. Professional Affiliations: Fellow of Royal Statistical Society and Advance HE Key Roles: PI on SFI/EI projects, Visiting Professor at Princess Nourah bint Abdulrahman University Research spans software engineering (microservices architecture), health informatics (exercise interventions), and agri-tech (automated food processing). Over 100 publications across data science, sports analytics, and engineering.
Anthony Kelly serves as a Postdoctoral Research Fellow in the Department of Electronic and Computer Engineering within the Faculty of Science and Engineering at the University of Limerick, Ireland, with his office located in E2-006. His affiliation spans both engineering and healthcare domains through interdisciplinary research initiatives. His research demonstrates dual expertise in artificial intelligence applications for healthcare and advanced power electronics. In healthcare AI, he develops interpretable mental health models, diabetes management chatbots, and comorbid condition interventions with emphasis on clinician trust and safety evaluation. In power systems, he pioneers digital control techniques for DC-DC converters, FPGA power management, and machine learning-integrated circuit designs. This bifurcated focus reveals a strategic transition from hardware-centric research (2005-2019) toward AI-health convergence (2024-2025). Analysis of his 15 most recent publications shows a pronounced shift toward healthcare AI since 2024, with 80% of current work addressing mental health modeling, diabetes chatbots, and comorbid condition management. Earlier publications (2009-2019) consistently focused on power electronics innovations including current-sharing algorithms, adaptive controllers, and FPGA-based systems, establishing foundational expertise later applied to healthcare technology development.
John Herbert is a Senior Lecturer in Computer Science at University College Cork (UCC), Ireland. His research focuses on pervasive computing, wireless sensor networks, and healthcare informatics, with notable projects like the CARA framework for falls assessment in the elderly. He holds a PhD in Computer Science from Cambridge University and has held visiting roles at institutions including SRI International and the University of Cambridge. Education: BSc (Experimental Physics), MSc (Experimental Physics), Postgraduate Diploma (Computer Science) from UCC; PhD (Computer Science) from Cambridge University. Research Grants: Led projects funded by Science Foundation Ireland (163,726) and the Irish Research Council (71,250), focusing on cloud computing and healthcare applications. His work emphasizes context-aware systems, data quality in medical environments, and real-time analysis. Selected awards include an International Fellowship from Digital Systems Research Center (1989) and visiting fellowships at Cambridge (2008-2009). Teaching: Courses include Advanced Software Engineering, Formal Methods, and Model-Based Software Development. Outreach: Advisor to award-winning student teams (e.g., IEEE mobile app contest) and collaborator with industry partners in China and Europe.
Siobhán Clarke is a Professor at the School of Computer Science and Statistics, Trinity College Dublin, specializing in software systems for smart urban environments . Her work addresses dynamic software adaptation in large-scale, mobile IoT ecosystems , with a focus on QoS optimization and collaborative agent models . Director, Enable : National SFI IoT Research Programme Director, Future Cities Centre for Smart & Sustainable Cities Co-Lead, ADVANCE : SFI Centre for Advanced Networks Co-PI, CONNECT (Future Networks) and Lero (Software Research) Her research spans smart city infrastructure , edge computing , and multi-agent coordination , informed by 15+ years of publications on service-oriented architectures , QoS prediction , and self-adaptive systems . Key project contributions include DIVERSIFY (2016) and TRANSFoRm (2015). Scientific awards include election to the Royal Irish Academy (2023) and a Best Student Paper at IEEE ICWS 2011. She has supervised 20+ PhD/MSc students, including Fan Li (2020: SLA Negotiation Systems), Gary White (2020: IoT QoS Forecasting), and Andrei Palade (2019: Stigmergic Optimization).
Professor Francesco Pilla is a Full Professor of Smart and Sustainable Cities at University College Dublin's School of Architecture, Planning and Environmental Policy, where he also serves as Co-Director of the Spatial Dynamics Lab. Previously, he held positions as MSc Director (2018-2019) and Undergraduate Director for the School of Architecture. With over 15 years of experience in GIS modeling and spatial data analysis, Professor Pilla's work bridges technology, urban planning, and community engagement to create more sustainable cities. Professor Pilla's research focuses on developing geospatial analysis tools and environmental pollution models (air, noise, water) using GIS platforms to facilitate interoperability between research teams and end users. His approach integrates environmental pollution models with pervasive and community sensing applications for calibration and validation. A key innovation in his work is the integration of co-design principles through Living Labs in urban planning and citizen science initiatives to improve city life from an environmental perspective. His recent publications reveal a strong focus on urban sustainability challenges, with significant contributions to air quality monitoring, sustainable transportation, climate adaptation, and citizen science methodologies. His work increasingly incorporates advanced AI techniques, including large language models and machine learning algorithms, to analyze urban systems and develop data-driven solutions for complex environmental problems. The research demonstrates a clear trend toward integrating diverse data sources and developing practical tools for urban governance and planning. Haagen-Smit Prize (2024) - Recognizing outstanding papers published in Atmospheric Environment Best eMobility Project - Electric Vehicle Awards 2024 All Ireland community & council awards - Best Community Transport Initiative 2024 Academic research award: tipping point for action from Financial Times (2023) Professor Pilla leads numerous significant research projects including iSCAPE (H2020-SC5) as Coordinator, OPERANDUM (Principal Investigator for UCD) focusing on flood risk reduction with nature-based solutions, WeCount (Principal Investigator for UCD), and Connecting Nature (Principal Investigator for UCD). He has also secured funding through SFI Investigator grants for 'Scalable, Privacy-Enhanced Analytics for sharing mobility systems' and several Environmental Protection Agency projects. His collaborative work extends to partnerships with MIT through Fulbright/EPA TechImpact awards and IBM through Faculty Awards. At the Spatial Dynamics Lab, Professor Pilla oversees research that combines geospatial analysis, environmental monitoring, and community engagement. His innovative 'Bike Library' initiative, which has expanded to multiple schools in Dublin, exemplifies his commitment to translating research into practical community solutions. The lab's work integrates advanced sensing technologies, GIS platforms, and citizen science to address urban environmental challenges through collaborative approaches.
Prof. Kathleen Curran is a Professor at University College Dublin (UCD) and director of the UCD machine learning in medical imaging and diagnostics innovative research lab ( https://www.ucd-ml-mi.com/ ). She serves as an Affiliated Principal Investigator in the Centre for Biomedical Engineering, an INSIGHT funded investigator, and a funded investigator in the Science Foundation Ireland centre for research training in machine learning (ML-Labs). Her research integrates artificial intelligence, computer vision, and clinical medicine to develop interpretable AI solutions for medical diagnostics. Key focus areas include fetal ultrasound imaging, cardiac MRI reconstruction, neuroimaging for Alzheimer's disease and multiple sclerosis, and biomarker discovery for conditions like lymphangioleiomyomatosis and placenta accreta spectrum. She pioneers techniques in diffusion models, explainable AI, and multi-modal learning to address challenges in low-data medical scenarios. Analysis of her recent publications reveals dominant trends in applying generative models for medical data augmentation, developing uncertainty-aware diagnostic systems, and creating interpretable clinical AI tools. Her work consistently targets high-impact clinical applications including fetal development monitoring, cardiovascular disease management, and neurological disorder detection, with strong emphasis on real-world clinical implementation. Scientific recognition includes: 2019 InterTrade Ireland FUSION Project Exemplar Award (with Axial Medical Printing Ltd.) Three Enterprise Ireland Commercialisation Fund awards as Principal Investigator Horizon Europe consortium funding for SMASH-HCM project (Stratification, Management, and Guidance of Hypertrophic Cardiomyopathy Patients using Hybrid Digital Twin Solutions) Prof. Curran leads significant research funding initiatives including Horizon Europe and multiple Enterprise Ireland awards. Her group actively collaborates with industry partners like Axial Medical Printing Ltd. and participates in national research centers such as INSIGHT and ML-Labs, driving translational AI research from bench to bedside. The UCD machine learning in medical imaging and diagnostics lab ( https://www.ucd-ml-mi.com/ ) serves as her primary research hub, fostering interdisciplinary collaborations between computer scientists, clinicians, and biomedical engineers to advance clinical AI solutions.
Ravi Reddy Manumachu is an Assistant Professor in the School of Computer Science at University College Dublin (UCD), Ireland. He holds a B.Tech from IIT Madras (1997) and a PhD in Computer Science from UCD (2005), specializing in high-performance heterogeneous computing and energy-efficient systems. His research focuses on optimizing performance and energy efficiency in modern heterogeneous platforms like clouds, grids, and supercomputers through novel models and algorithms. Key contributions include functional performance/energy models, energy-prediction frameworks, and extensions like Heterogeneous MPI and ScaLAPACK for heterogeneous clusters. He has published over 69 articles in top journals/conferences, with recent works addressing data transfer energy measurement, scalable allreduce algorithms (SUARA), and portable programming models (OpenH). Professional roles include Assistant Professor at UCD (2023–present), SEAI Research Fellow (2022–2023), and prior industrial experience at Ansys, Siemens, and IONA Technologies. He has certifications in university teaching, GDPR, and research integrity. Languages include English (fluent), Telugu, and Hindi. Research trends emphasize bi-objective optimization (performance-energy), hardware heterogeneity challenges, and scalable communication algorithms for deep learning. His work addresses energy non-proportionality in CPUs and GPU-CPU interactions, with practical solutions for real-world applications like matrix operations and gene sequencing.
Gianluca Pollastri is an Associate Professor in the School of Computer Science at University College Dublin (UCD). He leads a research group focused on machine learning applications in bioinformatics, particularly protein structure prediction and analysis. His academic roles include Associate Professor since 2016, Senior Lecturer from 2008, and Lecturer from 2003. He earned an MSc from the University of Florence and a PhD from the University of California, Irvine. His research integrates deep learning and neural networks to address challenges in protein subcellular localization, secondary structure prediction, and intrinsically disordered regions. Key tools developed include SCLpred, PaleAle, Porter, and PUNCH2. He has secured grants from Science Foundation Ireland, the Health Research Board, and UCD. Pollastri’s work emphasizes rigorous validation of machine learning methods in biology, as outlined in the DOME framework. His lab has produced over 100 peer-reviewed articles, with recent focus on leveraging pre-trained language models (PLMs) for protein analysis. Education: MSc, University of Florence PhD, University of California, Irvine Awards: Best M.Sc. Thesis in Artificial Intelligence (1999) Best Student Project in Artificial Intelligence (1997) His teaching includes modules on Bioinformatics, Connectionist Computing, and Programming. He coordinates research collaborations and maintains a lab with postdoctoral fellows and graduate students.
Thomas Chadefaux is a Professor of Political Science at Trinity College Dublin, The University of Dublin. He holds a Ph.D. in Political Science from the University of Michigan and an M.A. from the Graduate Institute of International Studies in Geneva. Prior to his current role, he served as a Visiting Assistant Professor at the University of Rochester and a Postdoctoral Researcher at ETH Zurich. His research focuses on the predictability of interstate conflict, leveraging advanced statistical methods, machine learning, and big data (e.g., satellite imagery, financial markets, news archives). He investigates decision-makers’ anticipation of war risks, the dynamics of conflict escalation, and the application of early warning systems. His work bridges empirical analysis with theoretical insights from game theory, contributing to top journals like the American Political Science Review and advising institutions such as the German Department of Foreign Affairs and the EU. Key achievements include awards for best paper (American Political Science Review, 2018), best conference paper (Oxford, 2012), and best visualization (Journal of Peace Research, 2014). His methodologies emphasize time-series clustering and pattern-based approaches to improve conflict forecasts. Chadefaux collaborates internationally on projects like the VIEWS Prediction Challenge and employs innovative tools such as dynamic synthetic controls for causal inference. His research underscores the interplay between public and private information in diplomatic crises, with implications for policy and international relations.
Madeleine Lowery is a Professor in the School of Electrical and Electronic Engineering at University College Dublin. She leads the Personal Sensing research group, focusing on engineering approaches to study the human nervous system in health and disease, with applications in therapies for impaired motor function. Her interdisciplinary research integrates neural engineering, electromyography, and biomedical signal processing. Specializes in neuromuscular systems and neural control of movement Develops myoelectric control systems for artificial limbs Designs high-density electrode systems for neural activity recording Investigates deep brain stimulation mechanisms in Parkinson’s disease models Her research spans neurodegenerative disorders (ALS, Huntington’s disease) and rehabilitation technologies , including wearable sensors for gait and sleep analysis. Key methodologies involve computational modeling , adaptive control systems , and biomedical signal analysis .
Honghui Du serves as a Research Fellow at the Insight Centre for Data Analytics, a leading Irish research institution specializing in data science with nodes across multiple universities. The role centers within the Decision Making research group, focusing on algorithmic solutions for dynamic environments. Research spans transfer learning in non-stationary data streams , recommender systems (notably news personalization using LLMs and diffusion models), and medical imaging under label scarcity. Key emphases include handling concept drift, optimizing active learning for medical diagnostics, and developing generative approaches for user-item interaction modeling. Emerging work explores gamification for sustainable behavior change and entity resolution via language models. Recent publications (2023-2025) reveal accelerating integration of diffusion models and transformers into recommendation frameworks, while maintaining core expertise in transfer learning for evolving data streams. Medical imaging research increasingly addresses practical constraints like limited annotations through adaptive curriculum strategies. The work operates within the Decision Making research group at the Insight Centre, which investigates algorithmic decision processes under uncertainty and dynamic conditions.
Rajkumar Sarma is a Research Fellow at the Department of Computer Science & Information Systems at Lero – the Research Ireland Centre for Software, University of Limerick. His work focuses on hardware design, VLSI systems, and optimization techniques for digital circuits. He specializes in areas such as low-power architectures, floating-point arithmetic, and evolutionary algorithms for automated design. His research emphasizes hardware-software co-design, reliability analysis under PVT (Process, Voltage, Temperature) variations, and efficient implementations of multiply-accumulate (MAC) units critical to digital signal processing and image processing applications. Key technical contributions include the development of the UCM algorithm for delay optimization, grammatical evolution for synthesizable HDL code generation, and novel approaches to reduce power consumption in MAC architectures. His work bridges theoretical algorithmic innovation with practical VLSI implementation challenges, addressing both performance and reliability under extreme operating conditions. Rajkumar’s publications span 2012–2025, with a strong focus on digital circuit design, including hybrid adders, low-power flip-flop implementations, and quantum gate-based reversible circuits. His research also extends to reliability analysis of electronic components like multi-layer ceramic capacitors. He has utilized advanced simulation tools such as Cadence ADE-XL for accelerated PVT analysis, demonstrating expertise in both computational modeling and hardware validation. While no specific awards or grants are listed, his extensive publication record reflects sustained engagement with cutting-edge challenges in computer architecture and VLSI systems design.
Dr. Muhammad Sajid is an Assistant Professor in Automotive Engineering (Fluid Mechanics) at the School of Mechanical and Materials Engineering, University College Dublin. He holds a B.Eng. from NUST (Pakistan), a Master's from ENSAM ParisTech (France), a PhD from University of Cergy Pontoise (France), and completed postdoctoral research at Texas A&M University Qatar and NUST. His research focuses on integrating AI/ML with mechanical engineering challenges, particularly in fluid dynamics, renewable energy systems, and smart building technologies. He coordinates courses like Computational Fluid Mechanics and Mechanics of Fluids. His work spans experimental and numerical studies in energy harvesting, HVAC optimization, and sustainable urban infrastructure. As PI of the AIMS laboratory, he leads projects on AI-driven mechanical systems. He actively participates in international conferences and has authored over 50 peer-reviewed publications. Education: Bachelor of Engineering (B.Eng.), National University of Sciences and Technology Master’s Degree, École Nationale Supérieure d’Arts et Métiers (ENSAM) Paris Tech PhD, University of Cergy Pontoise Postdoctoral Research: Texas A&M University at Qatar and NUST Research interests include cloud-based high performance computing for fluid dynamics simulations, solar/wind energy forecasting using machine learning, IoT sensor analytics for HVAC systems, and aerodynamic design optimization. His recent work emphasizes achieving net-zero energy buildings through smart environmental control systems.
Dr. Meisam Gordan is an Assistant Professor in Civil Engineering at University College Dublin (UCD), affiliated with the School of Civil Engineering. Previously, he held Postdoctoral Research Fellow positions at UCD (2022–2024) and the University of Malaya (2021–2022), and was a Graduate Research Assistant at the University of Malaya (2019–2020). He earned his PhD in Civil Engineering (2020) from the University of Malaya, specializing in Structural Health Monitoring, Data Mining, and Artificial Intelligence, supported by a High Impact Research Scholarship. **Education**: PhD in Civil Engineering, University of Malaya (2020) MSc in Civil Engineering, University Technology of Malaysia (UTM) **Research Interests**: Focuses on integrating Civil Engineering with Computer Science using Industry 4.0 technologies such as IoT, Big Data, Blockchain, Digital Twins, and Circular Economy. Key areas include Structural Health Monitoring, Machine Learning, Vibration Control, and Smart Infrastructure. Current projects include the Di-Rail project (railway fault diagnosis) and the Horizon 2020-funded PRECINCT (critical infrastructure resilience). **Publications**: Over 40 peer-reviewed articles, with recent work emphasizing hybrid digital twins, smart cities, and cybersecurity. His articles analyze trends in SHM with AI, edge-IoV networks, and resilience frameworks for critical infrastructure. **Awards**: High Impact Research Scholarship (2014), recognized as a reliable peer reviewer for journals like Structural Health Monitoring and IEEE Access. **Teaching**: Coordinates modules on Environmental Engineering, Transportation, and Structural Dynamics at UCD. Teaches courses like CVEN30170 Analysis of Structures 2 and CVEN20070 Computer Applications in Civil Engineering. **Grants/Projects**: Lead roles in PRECINCT and Di-Rail. Previously contributed to projects on recycled rubber energy dissipation systems and blockchain-based sensor data storage. **Labs/Teams**: Collaborates with the Structural Dynamics and Assessment Laboratory (SDA-Lab) at UCD and the PRECINCT Living Labs. Active in the UCD Civil Engineering research community.
Dr. Tai Tan Mai is an Assistant Professor at Dublin City University's School of Computing. He holds a PhD from DCU funded by the Irish Research Council and an MSc in Business Information Systems from University College Cork (2016) where he graduated as top-performing student. His research integrates data mining, learning analytics, and complex systems theory with applications in educational technology and business process management. Research interests focus on: Educational data mining and learning analytics Cryptocurrency market analysis using graph-based methods AI applications in education and societal risk assessment Complex systems approaches to programming education Prior to academia, he developed Business Process Management solutions for Vietnam's banking/financial sector.
