جست‌وجوی استاد و پژوهشگر

• Mobile Computing
• Distributed Systems
• Decentralized Applications
• +۴ مورد دیگر

Dr. Dimitris Chatzopoulos is an Assistant Professor (Ad Astra Fellow) at the School of Computer Science, University College Dublin. He holds a PhD from The Hong Kong University of Science and Technology and academic qualifications in Computer Engineering from the University of Thessaly, Greece. His research focuses on privacy-preserving systems, AI-driven decentralized applications, mobile computing, blockchain technology, and combating fake news. Education: PhD in Computer Science and Engineering, The Hong Kong University of Science and Technology (2013–2018) MSc in Computer Engineering and Communications, University of Thessaly, Greece Professional Diploma in University Teaching & Learning, University College Dublin Research Interests: Mobile and distributed systems Decentralized AI and blockchain applications Privacy-enhancing technologies Extended reality (XR) interfaces Metaverse infrastructure Awards & Recognition: Heidelberg Laureate Forum Participant (2017) Grants & Projects: "AI-enabled decentralized sensing for mobile and distributed systems" (2023–2026) "DIGITISE: Cross-sector services for societal empowerment" (2024–2027) Ad Astra Fellowship (2020–2027) Teaching & Supervision: Module coordinator for courses such as Blockchain & Decentralisation, Cloud Computing, and Python OOP PhD thesis supervisor

Soumyabrata Devمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Remote Sensing
• +۶ مورد دیگر

Soumyabrata Dev is an Assistant Professor in the School of Computer Science at University College Dublin (UCD), where he leads the THEIA lab focusing on interdisciplinary research in computer vision, machine learning, and remote sensing. His work addresses challenges in climate science, environmental monitoring, solar forecasting, and healthcare. He holds a PhD from Nanyang Technological University (Singapore) and has held postdoctoral roles at Trinity College Dublin and ADAPT SFI Research Centre. His education includes a B.Tech. (summa cum laude) from National Institute of Technology Silchar, India, and a visiting doctoral experience at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. Prior to academia, he worked as a network engineer at Ericsson India (2010–2012). Research interests span image processing, environmental data analytics (e.g., air/water quality), solar energy forecasting, and AI-driven solutions for sustainable development. He collaborates globally with institutions in academia, industry, and government, aligning with UCD’s strategic goals for impactful innovation. He is an SFI Funded Investigator at ADAPT SFI and a UCD Climate Fellow (2024–2026). His scientific awards include the 2024 Stanford/Elsevier Top 2% Scientists List. He advises numerous PhD/MSc students on topics like coastal monitoring, air quality modeling, and AI for sustainability. THEIA Lab’s projects include solar irradiance forecasting, knowledge graph-based climate data platforms, and blockchain-enhanced healthcare systems. He teaches modules on Operating Systems, Wireless Sensor Networks, and Augmented/Virtual Reality. His work bridges theory and application, emphasizing real-world impact in climate action and renewable energy.

• Plasma Physics
• Fusion Energy
• Tokamak Physics
• +۳ مورد دیگر

Dr. Huw Leggate is a Senior Research Fellow in the School of Physical Sciences at Dublin City University, specializing in plasma physics and fusion energy research. His work focuses on advancing the understanding of plasma behavior in fusion devices such as JET and MAST-U, with particular emphasis on edge plasma dynamics, transport modeling, and ITER-related experiments. He holds a postdoctoral researcher role and contributes to multidisciplinary projects in fusion science. His research interests include plasma surface interactions, impurity transport, and the optimization of fusion reactor components. He has collaborated extensively with international fusion programs, including contributions to JET’s preparation for deuterium-tritium operation and the development of diagnostic tools for edge plasma characterization. His work often intersects with computational plasma physics and the integration of advanced simulation codes (e.g., JOREK, DIVIMP) to model plasma behavior under extreme conditions. Dr. Leggate’s publications reflect a deep engagement with topics such as scrape-off layer filament dynamics, heat transport scaling, and the mitigation of edge localized modes (ELMs). He has also explored the implications of tungsten divertor materials and their degradation under fusion conditions. His research aligns closely with global efforts to achieve practical fusion energy through ITER and DEMO projects. He is affiliated with the Glasnevin Campus at DCU, conducting research in laboratory S120. His contributions to the field include both experimental diagnostics and theoretical modeling, bridging the gap between fundamental plasma physics and applied fusion engineering challenges.

Kieran Herleyمشاهده پروفایل

مدرس

• Parallel Algorithms
• Distributed Computing
• Multicast Routing
• +۲ مورد دیگر

Dr. Kieran Herley is a Lecturer in the School of Computer Science and Information Technology at University College Cork. Educated at UCC and Cornell University, he has taught at UCC since 1990. His research centers on parallel and distributed computing, algorithm design, and network routing. His key interests include multicast routing on mesh networks, shared memory implementation, and comparative analysis of parallel computation models like BSP and LogP. Dr. Herley's work addresses fundamental challenges in deterministic algorithms and network efficiency for high-performance systems. Publications highlight innovations in parallel backtrack search, shared memory architectures, and communication models. Consistent themes include optimization of distributed algorithms and theoretical frameworks for parallel computing. Dr. Herley teaches courses on algorithms, databases, and compilation. He has served on program committees for Euro-Par and ESA conferences, contributing to advancements in parallel computing methodologies.

Tania Malikمشاهده پروفایل

مدرس

• High-Performance Heterogeneous Computing
• Parallel and Distributed Computing
• Performance Optimization
• +۳ مورد دیگر

Dr. Tania Malik serves as an Assistant Lecturer in the School of Informatics and Cybersecurity at Technological University Dublin (TU Dublin). She holds a PhD from University College Dublin (UCD) earned in 2017 under the prestigious Irish Research Council Enterprise Partnership Scheme (IRC-EPS) award, establishing her expertise in high-performance computing and related disciplines. Her academic foundation includes doctoral research at University College Dublin, where she was supported by competitive fellowship funding. PhD, University College Dublin (UCD), 2017 (IRC-EPS award) Dr. Malik's research focuses on optimizing computational systems for modern data-intensive applications. Her core interests span High-Performance Heterogeneous Computing, Parallel and Distributed Computing, Performance Optimization, Energy-Efficient Computing, High-Performance Big Data Analytics, and HPC for Machine Learning. She investigates techniques to enhance computational efficiency while reducing energy consumption in large-scale systems, with direct applications in machine learning and big data processing pipelines. Her scholarly recognition includes competitive fellowship support for doctoral research. Irish Research Council Enterprise Partnership Scheme (IRC-EPS) award With extensive experience across Irish and international institutions—including University College Dublin, Dublin City University, National College of Ireland, COMSATS, and NUST Pakistan—Dr. Malik has cultivated significant industry partnerships with Fidelity, IBM, Whizz Systems USA, PSEB, and PYB. Her research program has secured competitive funding from the Irish Research Council (IRC), Science Foundation Ireland (SFI), and European Cooperation in Science and Technology (e-Cost). She actively engages in teaching, student mentorship, and industry collaboration within her academic role. Dr. Malik also contributes to diversity initiatives through active membership in Women in Technology and Science Ireland (WITS), Women in High-Performance Computing (WHPC), Women Leaders in Higher Education (WLHE), Women in Technology United (WITU), and UCD's Women@CompSci group.

• Sustainable Orchestration in Edge-Cloud Environments
• Multi-Cloud Workload Distribution
• Large Language Models (LLMs) Deployment
• +۳ مورد دیگر

Dr. Hadi Tabatabaee Malazi is an Assistant Professor at the University of College Dublin (UCD), leading the Sustainable Orchestration in Computing Continuum (SOC² Lab). He holds an Orcid identifier (0000-0002-2960-6896) and has extensive academic experience across institutions like Maynooth University, Shahid Beheshti University, and Trinity College Dublin. His research focuses on sustainable computing, edge-cloud continuum orchestration, AI/ML deployment (e.g., LLMs), and energy efficiency. He is a Senior Member of IEEE, serves as an Associate Editor for IEEE Access, and chairs the COST Action CA22151 (CYPHER) on decarbonizing energy-intensive industries. Education: PhD (2012): University of Isfahan (Computer Engineering) MSc (prior to 2012): University of Isfahan Research Visit (2010-2011): Delft University of Technology Research Interests: Sustainable edge-cloud systems, AI-driven workload optimization, carbon-aware resource management, distributed LLMs, and IoT interoperability. Key contributions include over 20 journal articles in IEEE, Elsevier, and Springer venues, with topics ranging from vehicular network security to smart city event processing. Professional Activities: Committee member of COST Action CA22151, IEEE Access Associate Editor, and Technical Program Committee member for IEEE conferences. Authored certifications on research leadership and research integrity from Epigeum. Labs/Teams: SOC² Lab (UCD), leading projects on edge-cloud sustainability and AI application deployment.

• Data Analytics
• Social Network Analysis
• Recommender Systems
• +۴ مورد دیگر

Neil Hurley is an Associate Professor and Head of School in the School of Computer Science at University College Dublin. He holds a BSc and MSc from University College Dublin and a PhD from Trinity College Dublin. Before academia, he worked at the Hitachi Dublin Laboratory from 1989 to 1999, leading research in parallel computing and knowledge-based systems. He joined UCD in 1999 and founded the Information Hiding Laboratory in 2001, focusing on digital content security. His research spans recommender systems, social network analysis, high-performance computing, and data hiding technologies. He has secured over €1 million in research funding from agencies like Enterprise Ireland and the EU. His teaching includes coordinating modules on Artificial Intelligence, Recommender Systems, and Computational Science. He has reviewed for journals such as IEEE Transactions on Image Processing and serves on the EMPS Graduate School Board. His recent work emphasizes scalable recommendation algorithms, privacy-preserving distributed systems, and strategic network analysis.

• Robotics
• Automation
• Advanced Manufacturing
• +۳ مورد دیگر

Philip Long is a Lecturer in Automation, Robotics & Advanced Manufacturing at the Department of Mechanical & Industrial Engineering, University of Galway. He serves as a Principal Investigator in the Engineering Research Group (ERG), focusing on robotics and advanced manufacturing systems. Research Interests: Active in robotics engineering, his work spans non-prehensile manipulation, sensor fusion for obstacle detection, wrench capability analysis in parallel robots, and human-supervised operations in extreme environments. His research integrates mechanical design, control theory, and AI to enhance robotic performance. Recent Publications: His 2023 contributions include methods for state-triggered constraints in manipulation tasks, polytope-based performance metrics, and multi-sensor fusion techniques. Earlier work (2021) explores humanoid robot safety in radioactive environments. Collaborations: Collaborates with researchers like Dr. Taskin Padır (Northeastern University) and Dr. Stéphane Caro (CNRS). Affiliated with institutions in Ireland and the US.

Martin Craneمشاهده پروفایل

استاد

• Computational Finance
• Complex Systems
• Time Series Analysis
• +۱ مورد دیگر

Martin Crane is Professor and Head of School at Dublin City University School of Computing. Funded Investigator in ADAPT Centre, with PhD in Mechanical Engineering from Trinity College Dublin. Research spans computational finance, complex systems modeling, and high-performance computing applications. Key research areas: Time series analysis in finance/biology Complex systems in finance, immunology, and lifelogging Wavelet methods for market analysis Parallel computing for large-scale simulations Current projects examine cryptocurrency dynamics, healthcare analytics, and brain tumor segmentation using deep learning. Leads FinTech Fusion initiative exploring financial technology applications. Authored significant works on random matrix theory filters for portfolio optimization and agent-based HIV modeling.

Geoff Hamiltonمشاهده پروفایل

دانشیار

• Program Transformation
• Automated Theorem Proving
• Formal Verification
• +۳ مورد دیگر

Dr. Geoff Hamilton is Associate Professor at Dublin City University's School of Computing. His research develops program transformation hierarchies for applications in theorem proving, verification, optimization, and energy-efficient computing. The distillation algorithm—a key contribution—achieves orders-of-magnitude efficiency gains over standard techniques. Work spans automated verification tools, termination provers, security protocol analyzers, and complexity bounds calculators. Recent publications focus on side-channel attacks, concurrency optimization, and tight complexity bounds for loop programs. Teaching includes comparative programming languages, cryptography, and formal methods. Research consistently advances transformation-based approaches for parallelization, verification, and resource analysis. Current directions include smart contract verification and energy-aware program optimization methodologies.

John O'Mullaneمشاهده پروفایل

مدرس

• Serious Games
• Medical Informatics
• Educational Technology
• +۳ مورد دیگر

Dr. John O'Mullane is a Lecturer in the School of Computer Science and Information Technology at University College Cork (UCC). He holds a PhD from UCC and a Postgraduate Diploma in Teaching and Learning in Higher Education. His research focuses on serious games, medical informatics, educational technology, and statistical methodologies. He has contributed to projects involving mobile health solutions, healthcare data analysis, and blended learning environments. Education: BSc in Computer Science (UCC) MSc in Computer Science (UCC) PhD in Computer Science (UCC) Postgraduate Diploma in Teaching and Learning in Higher Education (UCC) Research Interests: Dr. O'Mullane explores the application of technology in healthcare and education. His work spans serious games for assessment, mobile health solutions for chronic disease management, and innovative statistical techniques for data analysis. He emphasizes the intersection of technology and pedagogy, particularly in developing self-regulated learning systems and blended learning frameworks. Notable Achievements: Recipient of the UCC President's Award for Innovative Teaching (2008, 2013) Contributions to medical imaging systems and telemedicine Leadership in statistical education initiatives and student experience surveys Grants & Advising: He has advised multiple postgraduate students and contributed to research projects funded by various institutions. His work includes analyzing student experiences across colleges at UCC and developing feedback systems for academic performance. Labs/Teams: Involved with interdisciplinary teams in medical informatics, education technology, and statistical research, including collaborations with the School of Medicine and the Student Experience Office.

Andrew Keaneمشاهده پروفایل

استاد

• Power Systems Planning and Operation
• Integration of Distributed Energy Resources
• Renewable Energy Grid Integration
• +۵ مورد دیگر

Andrew Keane is a Full Professor and Director of NexSys and the Energy Institute at University College Dublin (UCD). He leads research in power system planning and operation, with a focus on distribution networks, renewable energy integration, and multi-energy system co-simulation. He has held leadership roles including Head of the School of Electrical & Electronic Engineering at UCD and founded NovoGrid, a grid automation company. His research spans power electronics, voltage stability, and smart grid technologies. Key areas include Renewable energy impacts on transmission networks Green hydrogen for grid flexibility Discrete control device optimization Network topology identification Recent publications highlight trends in voltage control strategies, renewable hosting capacity analysis, and electricity-gas network interdependencies. Awards include the 2022 Industry Partnership Award, NovaUCD Licence of the Year, and the 2012 Intel Early Career Faculty Honor Program. Teaching activities at UCD include coordinating modules on Energy Climate Change & Policy Grid Engineering and Modeling Optimization Techniques Power System Optimization Grants fund his work on energy system integration, green hydrogen, offshore wind, and smart grid test beds across multiple projects from 2012 to 2026. He serves as Senior Member of IEEE and contributes to technical standards committees.

• Structural Biology
• NMR Spectroscopy
• Antimicrobial Peptides
• +۷ مورد دیگر

Associate Professor Chandralal Hewage is a structural biologist at the School of Biomolecular and Biomedical Science, University College Dublin, with over 30 years of expertise in NMR spectroscopy. His research focuses on biologically active peptides for diabetes therapy, antimicrobial drug development, and liver disease metabolic studies. Research Interests: Structural analysis of diabetes-related peptides Design of GIP and endothelin receptor drugs NMR-based antimicrobial peptide studies Liver cell metabolic profiling in disease states Development of computational peptide modeling tools Scientific Contributions: Organizer of international conferences EUROMAR 2012 and ICMRBS 2018. Author of >100 publications. Developer of bioinformatics tools APPTEST and ENNAVIA for peptide structure/activity prediction. Professional Affiliations: ICMRBS Council Member European Peptide Society Council Member Chairman of Irish Peptide Society

Erika Duriakovaمشاهده پروفایل

پژوهشگر ارشد

• Distributed Systems
• Graph Processing
• Machine Learning
• +۲ مورد دیگر

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.