Dr. Budi Zhao is a Lecturer/Assistant Professor in the School of Civil Engineering at University College Dublin since August 2020. He holds a PhD from City University of Hong Kong (2017) and has held academic positions at Imperial College London (Research Associate, 2019–2020) and King Abdullah University of Science and Technology (Postdoctoral Fellow, 2017–2019). His research focuses on multi-physics processes in soil and rock, employing advanced techniques like X-ray micro-tomography (μCT), microfluidics, and numerical modeling (e.g., DEM and CFD-DEM). Key areas include crushable sands, desiccation cracking, fines migration, and energy geotechnics. He serves on ISSMGE committees TC105 and TC308, and is a member of the editorial board of the Journal of Rock Mechanics and Geotechnical Engineering . His research outputs span topics such as 3D printed composites, microplastic transport, and internal erosion mechanisms. Notable projects include grants on multi-scale analysis of clays and salt precipitation effects. Dr. Zhao coordinates modules like Geotechnical Engineering and Soil Mechanics at UCD, emphasizing innovative teaching methods like flipped classrooms. His work bridges fundamental science and engineering applications, with a focus on sustainable geotechnical solutions. Education: PhD (City University of Hong Kong, 2017), B.Eng (Chongqing University), Professional Certificate in University Teaching (UCD). Grants: Includes funding for offshore wind energy anchors and carbon geological storage projects. Advising: Supervises multiple PhD students in geomechanics and energy geotechnics. Labs/Teams: Leads research using state-of-the-art facilities for μCT imaging and microfluidics.
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.
Padraig MacCarron is an Associate Professor in the Department of Mathematics and Statistics at the University of Limerick, specializing in complex networks. He is affiliated with the Centre for Research Training in Foundations of Data Science and the Mathematics Applications Consortium for Science and Industry (MACSI). Education: BSc in Astrophysics from University College Cork; PhD from Coventry University on social networks in narratives. His research focuses on interdisciplinary applications of complex networks, including social polarization, criminal networks, and trust dynamics in health research. He collaborates with Psychology, Law, and Health departments to model social phenomena like community formation and fragmentation. Recent work explores structural properties of social networks through diverse domains such as Twitter analysis for political polarization, participatory health research partnerships, and arts-based methods for migration health co-production. This spans network theory, social media analytics, and data-driven policy modeling. He is a member of MACSI and the Centre for Research Training, emphasizing computational methodologies and team-based collaborations. Padraig actively accepts PhD students for projects involving network science and social systems modeling.
Lars Pforte is a Lecturer in the Faculty of Science & Engineering at Maynooth University, affiliated with the Mathematics and Statistics department. He holds a PhD in Mathematics and a Masters Degree in Geocomputation. PhD in Mathematics Masters in Geocomputation His research spans both pure mathematics and applied geospatial analysis. Key areas include: Representation theory of finite groups Urban airspace traffic management (UTM) Road safety analysis Data imputation in space-time series While his recent publications focus on algebraic structures like symplectic modules for the Klein-four group and permutation module vertices, he also applies Bayesian statistical methods to urban analytics and transportation safety. No scientific awards are explicitly mentioned in the available information.
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).
Brendan Murphy is a Full Professor in the School of Mathematical Sciences at University College Dublin since 2015, previously holding a Professor role at the same institution (2007-2015) and a Lecturer position at Trinity College Dublin (1999-2007). He is a Principal Investigator at the Insight Centre for Data Analytics and actively works in Machine Learning & Statistics. Research Focus: His work centers on Model-based clustering Mixture models Applications in sports analytics, food science, microbiome studies, and public health Bayesian statistical methods High-dimensional data analysis Article Trends: Murphy's recent publications (2024-2025) emphasize soft clustering techniques, Bayesian mixture models, and their applications across diverse domains including metabolomics, political science, and oceanography. His work addresses challenges in variable selection, robust classification, and multi-omics integration.
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.
Natalia Kopteva is a Full Professor (Chair) in Applied Mathematics at the Department of Mathematics and Statistics, University of Limerick, Ireland. She holds a Ph.D. and M.Sc. in Computational and Applied Mathematics from Moscow State University. Her career includes academic positions at Moscow State University, University College Cork, and University of Strathclyde. Education: M.Sc. in Applied Mathematics, Lomonosov Moscow State University Ph.D. in Computational Mathematics, Lomonosov Moscow State University Her research focuses on numerical analysis of partial differential equations , particularly time-fractional and singularly perturbed subdiffusion equations , with emphasis on a posteriori error estimation , adaptive discretization methods , and maximum norm analysis . She has contributed to discontinuous Galerkin methods and Green's function estimates for convection-diffusion problems. Recent publications highlight trends in graded meshes for fractional calculus, pointwise error bounds , and time stepping adaptation for non-smooth data. Her work bridges applied mathematics and computational science with applications in reaction-diffusion systems and convection-diffusion equations . She serves as editor for SIAM Journal on Numerical Analysis and Advances in Computational Mathematics , and has held editorial roles for 8 international refereed journals. Additional affiliations include membership in the Centre for Research Training in Foundations of Data Science and the Mathematics Applications Consortium for Science and Industry (MACSI) .
Alessandro Ragano is a Postdoctoral Researcher at the Insight Centre for Data Analytics , where he has been investigating Quality of Experience (QoE) aspects of audio archives and developing data-driven approaches for QoE estimation and audio restoration using deep learning since 2018. Education: MSc in Computer Science and Engineering from Politecnico di Milano (Italy) BSc in Computer Engineering from Università Degli Studi di Salerno (Italy) His research integrates machine learning , audio signal processing , and multimedia quality assessment to improve speech enhancement, audio restoration, and perceptual modeling. Recent trends in his publications focus on self-supervised learning , objective quality metrics , and audio dataset generation with applications in speech separation, music representation, and audio inpainting. He actively contributes to open-source tools like Binamix and AQP for audio research and quality evaluation.
Dr. Shawn Day serves as Head of Department and Lecturer in Digital Humanities at the Department of Geography, University College Cork (UCC), Ireland. He holds a PhD in Cultural Geography from UCC and an MA in History from the University of Guelph. His interdisciplinary research spans Cultural Geography of Irish Craft Beer, 19th Century Economic History, Spatial Humanities, and the History of Health and Medicine. He has pioneered digital humanities projects like the Digital Humanities Observatory (DHO) and contributed to global initiatives such as the Dariah Project and NeDiMAH. Education: PhD Cultural Geography, UCC (2024) MA History, University of Guelph (2004) BA Management Economics, University of Guelph (1988) Research focuses on spatial humanities methodologies (GIS, statistical modeling) applied to Victorian public mental hospitals and Canadian economic history. He has co-authored key works on DH infrastructure and led projects like the 1871/1891 Canadian census databases. His work bridges academic scholarship with entrepreneurial ventures, including founding software companies in Canada. Professional roles include Director of the European Health Futures Forum and Open Knowledge Foundation Ireland, and Coordinator of the Canadian Network for Economic History. He actively collaborates with institutions like Trinity College Dublin and the Royal Irish Academy.
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.
Trish O'Connell is a Lecturer in Maths & Statistics at the Department of Biopharmaceutical & Medical Science. She chairs the MSc in Biopharmaceutical Manufacturing program and serves on program boards for Medical Science (Years 1-3) and Applied Biology & Biopharmaceutical Science (Years 2 & 4). She holds professional memberships in the International Society for Pharmaceutical Engineers and the American Society for Quality. Her research integrates agile methodologies, Scrum frameworks, and quality management, with a focus on trust dynamics in software teams. She employs constructivist grounded theory to analyze collaboration and knowledge-sharing practices in technical environments. Her publications consistently explore Agile/Scrum implementation, emphasizing trust-building, feedback loops, and customer collaboration in software projects. Recent work (2018-2019) investigates psychological and structural enablers of effective Scrum teams. External Roles: External Examiner at Technological University of the Shannon Midlands Midwest (Athlone, 2023–present) Consultant in statistical quality tools (SPC, PCA, DOE) and product development techniques (QFD, FMEA)
Ruth Lennon is a Lecturer in Computing at the Department of Computing in Donegal, Ireland. She actively contributes to computer science education, focusing on curriculum development, gender diversity in computing, and intercultural learning. Her research bridges academic and industry practices in cloud computing, DevOps, and infrastructure security. External Roles: ISO/IEC JTC 1/SC 38/WG 6 Convenor, External Examiner at Munster Technological University, and former roles at Galway-Mayo Institute of Technology and Dundalk Institute of Technology. Research Focus: Cloud computing, DevOps best practices, and educational innovation are central to her work. She explores statistical models for student success and security frameworks for infrastructure-as-code deployments. Scientific Awards: ACM History Committee Fellowships (2022) ACM SIGPLAN Student Research Competition Prize (2005) IEEE SA Emerging Technology Award (2022) IEEE Volunteer Leadership Training (VoLT) Program - 2nd Prize (2024) Masters Teacher of the Year 2024 Finalist (2024) Conference Leadership: She chairs events like the IEEE UK and Ireland International Leadership Summit (2025), ISO/IEC JTC 1/SC 38 Plenary (2025), and the ACBSP Region 3 Conference (2024). Her work spans collaborations across Western Europe and North America.
Joseph Timoney is a professor at the Department of Computer Science , Maynooth International Engineering College , Maynooth University. He teaches undergraduate programs in Computer Science and Music Technology, with expertise in audio signal processing, musical sound synthesis, and digital modeling of analog subtractive synthesis. His research spans sound synthesis algorithms, audio watermarking, and ubiquitous music ecosystems.
Nicolae-Viorel Buchete is an Associate Professor of Theoretical & Computational Nano-Bio Physics at University College Dublin's School of Physics within the College of Science. He currently serves as Vice Principal for Graduate Studies for the College of Science and Director of the UCD MSc in Computational Physics Programme. His academic journey includes postgraduate degrees from Boston University (USA) and institutions in the EU (Al. I. Cuza University of Iasi, Romania, and the University of Patras, Greece), with a PhD from Boston University and research fellowships at the National Institutes of Health. His educational background includes: PhD from Boston University Research Fellowships at National Institutes of Health (Bethesda, MD, USA) Postgraduate degrees from Boston University, Al. I. Cuza University of Iasi (Romania), and University of Patras (Greece) Buchete's research focuses on theoretical and computational approaches to understanding biomolecular systems. His work spans theoretical and computational biological physics, chemical physics, and nanoscience , with specific emphasis on statistical mechanics and molecular dynamics of biomolecular systems, systems biology, structural bioinformatics, and multiscale modeling of biomolecules and complex fluids. His group employs advanced computational techniques including Markov State Models, Milestoning, and replica exchange molecular dynamics to study protein conformational dynamics, amyloid formation, and molecular mechanisms relevant to diseases like cancer and Alzheimer's. His research output reveals a progression from fundamental biophysics toward increasingly translational applications. Early work focused on protein conformational dynamics, while more recent publications demonstrate expansion into nanomedicine applications, computational toxicology of nanomaterials, and physics-based modeling frameworks for drug delivery systems. A significant portion of his research involves studying conformational transitions in proteins relevant to cancer (such as K-Ras4B and Abl kinase) and neurodegenerative diseases (particularly amyloid systems), with growing emphasis on computational approaches to nanosafety and sustainability. His scientific contributions have been recognized with numerous awards: Certificate of Appreciation from the American Chemical Society Publications Division (2012) Top 20 JCP Reviewer for 2010 from the American Institute of Physics NIH Fellows Award for Research Excellence (FARE) in 2006 and 2007 ACS Chemical Computing Group Excellence Award (2003) Multiple teaching and research awards from Boston University including the Outstanding Teaching Fellow Award (1998) and Feldman Award (2001) Buchete has mentored numerous graduate students through their MSc and PhD research, with students successfully defending theses on computational physics and biomolecular modeling topics. His teaching philosophy emphasizes "research-oriented teaching," integrating research experiences into undergraduate and taught Master's level education. He has secured research funding including the UCD OBRSS Research Support Scheme (2016-2023) and has directed multiple educational programs including the UCD International Pre-Masters Programme (2013-2022) and served as School Head of Teaching and Learning (2021-2022). His research group is affiliated with the UCD Complex & Adaptive Systems Laboratory (CASL), where they develop and apply advanced computational methods to study complex biomolecular systems. The group has organized multiple CECAM workshops on biomolecular modeling and simulations, demonstrating leadership in the computational biophysics community. They collaborate extensively across disciplines, working with experimentalists to validate computational findings and address challenging problems in biophysics and nanomedicine.
