Find Academic Supervisors

• Materials Science
• Mechanical Engineering
• Metallurgy
• +3 more

Samaneh Isavand is a Researcher in the School of Engineering at the University of Limerick, specializing in advanced materials characterization and computational mechanics of metallic alloys. Her work bridges experimental metallurgy with predictive modeling to address critical challenges in structural materials performance. Her primary research focuses on deformation mechanisms in ferritic and austenitic steels, utilizing cutting-edge techniques including micropillar compression, in-situ MEMS-heating EBSD, and high-resolution digital image correlation. She integrates these experimental approaches with crystal plasticity finite element modeling to investigate strain localization, phase transformations, and microstructure-property relationships in complex steel microstructures such as P91 and ferrite-pearlite systems. Analysis of her 2013-2025 publications reveals consistent emphasis on thermo-mechanical behavior of functionally graded steel composites, reverse phase transformation pathways, and forming limit predictions. Key methodological trends show progressive integration of multi-scale experimental validation with computational frameworks to unravel slip system activities and damage evolution under diverse loading conditions. No scientific awards were documented in the provided source material. Information regarding graduate student supervision, research grants, or laboratory affiliations was not specified in the available text. Her collaborative work appears centered on advanced steel characterization projects, likely involving interdisciplinary teams in materials testing and computational mechanics.

• Signal Processing
• Video Processing
• Bayesian Inference
• +5 more

Professor Anil Kokaram is a distinguished academic and researcher in Electronic Engineering at Trinity College Dublin (TCD), Ireland. He holds a PhD in Signal Processing from the University of Cambridge (1993). As a Fellow of Engineers Ireland and recipient of an Academy Award (Oscar) for his work in video processing, he is renowned for contributions to digital video restoration, multimedia forensics, and video compression. From 2011–2017, he led the Media Algorithms Team at YouTube/Google, advancing cloud-based video transcoding and enhancement technologies. His research bridges academia and industry, with innovations in Bayesian inference, motion estimation, and neural network applications for video processing. **Education**: PhD in Signal Processing, University of Cambridge (1993). **Key Roles**: Former Associate Editor of IEEE Transactions on Video Technology and Image Processing. Founded GreenParrotPictures (acquired by Google), producing video enhancement software. **Research Focus**: Video compression artifacts, perceptual quality metrics (e.g., ViSQOL), and adaptive streaming algorithms. **Notable Projects**: Developed frameworks for automated sports broadcasting, noise reduction in medical imaging, and synchronization of user-generated videos. **Awards**: 2007 Science & Engineering Academy Award (Oscar), 2007 Fellow of Engineers Ireland. **Labs/Teams**: Leads the Signal Media Algorithms group at TCD, collaborating with industry partners like Google on large-scale video analysis systems. His work emphasizes practical applications of signal processing in creative industries, including film post-production and virtual production.

Brendan MullaneView profile

Research Fellow

• Electrical Engineering
• Semiconductor Design
• Digital Signal Processing
• +3 more

Brendan Mullane is a Senior Research Fellow at the University of Limerick , affiliated with the Faculty of Science and Engineering and the Department of Electronic and Computer Engineering . Research Focus: Advanced analog/digital converter design, dynamic element matching, and biomedical signal processing. Key Contributions: Development of high-order noise shaping techniques for DACs, hardware implementations for brain injury detection, and optimization of ADC/DAC architectures. Contact: brendan.mullane@ul.ie Research Interests: Brendan's work centers on precision semiconductor design , with a focus on error correction in digital-to-analog converters and bandpass filtering techniques . His research bridges VLSI optimization and biomedical diagnostics , particularly in applying qEEG analysis for clinical applications. He has extensively explored mismatch shaping , inter-symbol interference mitigation , and switched-capacitor filter performance , contributing to the field of embedded systems for signal processing . Scientific Contributions: His publications demonstrate a strong emphasis on high-speed, low-noise analog circuits and on-chip testing methodologies . Key trends include programmable error shaping , real-time FFT processing , and IEEE 1500 standard optimization for ADC/DAC testability.

• Lithium-ion Batteries
• Electrochemical Materials
• Nanowire Technology
• +4 more

Dr. Michela Ottaviani is an Assistant Lecturer in the Department of Applied Science at the Faculty of Applied Sciences and Technology . Her research focuses on advanced energy storage systems, particularly lithium-ion and lithium-metal batteries, leveraging nanowire technologies and machine learning for optimizing battery performance. Her work contributes to UN Sustainable Development Goals 7 (Affordable and Clean Energy) and 9 (Industry, Innovation, and Infrastructure) . Key research areas include nanomaterial synthesis (e.g., silicon nanowires), electrolyte compatibility, and machine learning-driven modeling of battery state-of-charge. She has collaborated internationally on electrochemical and material science projects. Recent trends in her articles (2023–2025) highlight advancements in: Machine learning algorithms for battery management systems, Nanowire architectures to stabilize lithium metal anodes, Electrolyte optimization for high-performance batteries. No scientific awards or grants are explicitly listed. Her research emphasizes interdisciplinary approaches to sustainable energy solutions.

• Battery Technology
• Electrochemistry
• Materials Science
• +4 more

Dominika Capkova is a Researcher in the Department of Chemical Sciences at the University of Limerick. Her research focuses on advanced battery technologies with particular expertise in lithium-sulfur batteries and electrochemical energy storage systems. She has established herself as a prominent researcher in the field of battery materials with numerous publications spanning from 2018 to 2025. Her research interests center around electrochemistry and materials science with specific focus on lithium-sulfur battery technology , metal-organic frameworks for energy storage , and machine learning applications for battery performance prediction . Her work addresses critical challenges in battery technology including the polysulfide shuttle effect, cathode material design, and state-of-charge/state-of-health estimation. An analysis of her recent publications reveals a strong trend toward integrating machine learning techniques with electrochemical battery research . Her work spans fundamental materials development (metal-organic frameworks, sulfur cathodes) to applied aspects (battery management systems, degradation modeling). The interdisciplinary nature of her research combines chemistry, materials science, and computational methods to advance next-generation energy storage solutions. Dr. Capkova has actively collaborated with researchers across multiple institutions, as evidenced by her co-authorship on numerous publications. Her research output shows consistent growth, with increasing publication counts from 2018 to 2022, demonstrating her active engagement in the field. While specific grant information isn't detailed in the provided text, her extensive publication record suggests successful funding acquisition to support her research activities. Her work appears to be conducted within research groups focused on advanced battery materials and electrochemical energy storage systems , with particular emphasis on solving the technical challenges associated with lithium-sulfur battery technology. The collaborative nature of her publications indicates she works within multidisciplinary teams addressing various aspects of battery technology development.

• Statistical Machine Learning
• Bayesian Nonparametrics
• Mixture Models
• +13 more

Keefe Murphy is a Lecturer in Statistics within the Department of Mathematics and Statistics at Maynooth University , Faculty of Science & Engineering, and is affiliated with the Hamilton Institute . He is an active researcher in statistical machine learning, Bayesian nonparametrics, and clustering/classification of complex, high-dimensional data. Education: PhD in Statistics, University College Dublin MSc in Statistics, University College Dublin BSc in Economics & Mathematics, University of Limerick Research Interests: His work centres on developing and extending statistical methodologies for supervised and unsupervised learning , with emphasis on mixture models, latent variable models, Bayesian nonparametrics, and computational statistics . He actively contributes novel algorithms and software implementations, including the R packages IMIFA , MoEClust , and MEDseq available on CRAN. Current projects include extensions to Bayesian Additive Regression Trees (BART) , handling missing data , modelling multivariate count data , and variable selection in model-based clustering. Publication Profile: His recent publications (2021–2025) demonstrate a clear trajectory in advancing Bayesian machine learning methods, with contributions to Gaussian process BART models , sparse factor analysis , and educational data mining . Collaborative work spans learning analytics and multi-omic prostate cancer biomarker discovery , illustrating broad interdisciplinary impact. Awards & Recognition: Distinguished Dissertation Award (The Classification Society, 2020) Service & Advising: He serves as Associate Editor for Statistical Analysis and Data Mining and on departmental committees (Course Committee, PR Committee). He has successfully supervised PhD student Mateus Maia (graduated 2024) and currently teaches modules such as Advanced R Programming , Introduction to Data Science , and Nonparametric Statistics . Labs & Collaborations: He is affiliated with the Hamilton Institute , which fosters interdisciplinary research in applied mathematics and statistics, providing a collaborative environment for advancing computational and methodological statistics.

• Aberration-Corrected Electron Microscopy
• Atomic-Resolution Imaging
• Nanomaterials Characterization
• +4 more

Dr. Lewys Jones is an Associate Professor in the Department of Physics at Trinity College Dublin . His research focuses on advancing Scanning Transmission Electron Microscopy (STEM) techniques for atomic-scale materials characterization, particularly in low-dose imaging, detector optimization, and 3D tomography. Key Research Areas: Aberration-Corrected Microscopy, Atomic-Resolution Imaging, Nanomaterials Analysis, and Machine Learning for Image Enhancement Instrumentation Expertise: Detector Calibration, Probe Drift Compensation, Low-Voltage Imaging, and Digital Pulse Read-Out Systems His work has resulted in multiple peer-reviewed publications (2013-2024) with a focus on electron microscopy innovations and nanostructured materials . Notable scientific awards include the Royal Society & SFI University Research Fellowship (2019) and the International Federation of Societies for Microscopy 'Young Scientist Award' (2014). He has contributed to open-access resources and educational tools for STEM data analysis, including the 'Smart Align' software.

• High Performance Heterogeneous Computing
• Energy Efficient Computing
• Parallel Computing
• +4 more

Alexey Lastovetsky is an Associate Professor at the School of Computer Science, University College Dublin (UCD), where he is the founding Director of the Heterogeneous Computing Laboratory (HCL). He holds a PhD from the Moscow Aviation Institute and a Doctor of Science (Habilitation) from the Russian Academy of Sciences. His research focuses on high-performance heterogeneous computing, energy-efficient computing, and parallel algorithms for modern architectures. He has published over 175 peer-reviewed papers and authored influential monographs in the field. Education: PhD (Moscow Aviation Institute), Doctor of Science (Russian Academy of Sciences) Affiliations: UCD since 2001; previously Senior Scientist at Lomonosov Moscow State University (1989–1994) and Principal Scientist at the Russian Academy of Sciences (1995–1999) His research interests span heterogeneous computing , energy-efficient systems , and high-performance parallel algorithms . He has developed novel methodologies for workload distribution optimization, energy modeling, and communication performance analysis. His work on the OpenH programming model and SUARA communication algorithm exemplifies his contributions to scalable parallel computing. He has secured over €4.5M in grants , including four prestigious SFI Investigator awards. His recognition includes being ranked in the Stanford/Elsevier Top 2% Scientists list since 2020 and a ScholarGPS Highly Ranked Scholar distinction. He has organized over 300 international conferences and serves on editorial boards of journals like Journal of Parallel and Distributed Computing . Teaching responsibilities include coordinating modules on High-Performance Computing , Parallel Computing , and UNIX Programming . His lab, HCL, is a global leader in heterogeneous computing research, advancing energy-efficient and scalable solutions for modern HPC platforms.

• Applied Artificial Intelligence
• Robotics
• Unmanned Aerial Vehicles (UAVs)
• +5 more

Eleni Mangina is a Full Professor at the School of Computer Science, University College Dublin (UCD), and Vice Principal (International) for the College of Science. Her research focuses on applied artificial intelligence (AI), robotics, unmanned aerial vehicles (UAVs), and extended reality (XR) technologies with interdisciplinary applications in energy systems and education. She holds a PhD from the University of Strathclyde (UK), an MSc in Artificial Intelligence from the University of Edinburgh, and an MSc in Agricultural Science from the Agricultural University of Athens. Education : PhD, University of Strathclyde (UK), 2001 MSc in Artificial Intelligence, University of Edinburgh (UK) MSc in Agricultural Science, Agricultural University of Athens (Greece) HDip in University Teaching & Learning, UCD Research Interests : AI-driven optimization for energy and materials Xr applications in healthcare and education Citizen science and open data practices Robotics in early childhood education Smart city technologies Awards & Honors : 2022 CEN/CENELEC Standards Innovation Award 2021 Athena SWAN Bronze Award (School of Computer Science) 2020 UCD President's Teaching Award 2022 StandICT.eu Fellowship Grants & Projects : Coordinator of EU H2020 projects: ARETE, AHA, and FANTASIA Principal Investigator in SFI Energy Systems Integration Programme Lead on multiple XR and energy-related grants (2017-2025) Labs & Teams : Her lab develops XR solutions for education and energy, collaborating with EU and international partners. Current focuses include ethical XR standards and AI integration with metaverse platforms.

Anh Vu VoView profile

Assistant Professor

• LiDAR
• Urban Science
• Spatial Data Management
• +4 more

Dr. Anh Vu Vo is an Assistant Professor at University College Dublin's School of Computer Science, specializing in high-scalability algorithms for LiDAR and urban spatial datasets. He holds a PhD from UCD, a Master's from the University of Melbourne, and a Bachelor's from HCMC University of Architecture. Roles : Lecturer/Assistant Professor since 2024, Postdoctoral Research Fellow (2019–2022), Research Scientist at NYU (2018–2019). Research Interests : LiDAR, Urban Science, Spatial Data Management, Distributed Computing, and Big Data. Key projects include CAMEO (Earth Observation platform), UrbanARK (flood risk assessment), and AIMVIE (mangrove mapping). He has authored over 30 peer-reviewed publications and secured grants such as the SFI Future Innovator Prize. Awards : 2022 EODisrupt Winner, 2015 IEEE GRSS Data Fusion Contest First Prize, Australian Endeavour Award. Teaching : Spatial Information Systems, Urban Sensing, and graduate-level courses. His work focuses on integrating LiDAR data with advanced computing frameworks to address urban challenges like flood risk and infrastructure planning.

• Machine Learning
• Algorithm Engineering
• Combinatorial Optimization
• +3 more

Dr. Deepak Ajwani is an Assistant Professor in the School of Computer Science at University College Dublin . His research focuses on leveraging machine learning techniques for solving combinatorial optimization problems, with expertise in algorithm design, algorithm engineering, and graph algorithms. He holds a PhD from the University of Saarland, Germany, and has held postdoctoral positions at Aarhus University (Denmark) and University College Cork (Ireland). He also worked at Nokia Bell Labs (2012–2018) on learning systems for unstructured content analysis. Dr. Ajwani has received over 50 peer-reviewed publications in top-tier conferences and journals. He serves on the editorial board of the Machine Learning journal and is a frequent senior program committee member for conferences like WWW, IJCAI, AAAI, and ALENEX. He is a funded investigator at the SFI Centre for Research Training in Machine Learning (ML-Labs) . His teaching excellence has been recognized through multiple nominations for the UCD Teaching and Learning Award . His students have won the Franz Geiselbrechtinger Medal (2023, 2024) for best final-year projects in theoretical computer science. Key grants include the Irish Research Council and IBM Research Grant (2010–2012) for graph partitioning techniques in exascale computing. His research explores integrating machine learning with optimization algorithms, graph neural networks, and algorithm engineering for real-world applications.

Stephen BlottView profile

Associate Professor

• Unix/Linux systems
• Computer networks
• Container technologies
• +2 more

Stephen Blott is an Associate Professor at the School of Computing, Dublin City University. He holds a BSc in Computing Science from Glasgow University and a PhD from the same institution. Previously, he worked as a Senior Research Associate at ETH Zurich and as a Principal Investigator at Bell Labs in New Jersey. His research focuses on Unix/Linux systems, computer networks, container technologies, DevOps, and educational tools for computer science. Key areas include operating system optimization, network security protocols, container orchestration, and pedagogical innovations in computer science education. Publications show consistent focus on network security, data management, and computational efficiency. Recent work emphasizes internet infrastructure, privacy-preserving technologies, and biomedical informatics, with strong methodological foundations in simulation and algorithm design.

• Theoretical Atomic Physics
• Optical Physics
• Quantum Dynamics
• +4 more

Dr. Lampros Nikolopoulos is an Associate Professor in the School of Physical Sciences at Dublin City University (DCU). His research focuses on theoretical atomic and optical physics, with an emphasis on strong laser-matter interactions and high-performance computing simulations. He has held prior positions at institutions such as the Max-Planck Institute for Quantum Optics and the Institute of Electronic Structure in Greece. Research Interests: Quantum systems in ultra-short laser fields, atomic structure theory, free electron lasers, and attosecond physics. His work includes developing computational methods (e.g., MCHF, CI-B-splines) and simulating dynamics in extreme environments. He has authored two books and numerous peer-reviewed articles.

• Plasma Physics
• Fusion Energy
• Tokamak Physics
• +3 more

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.

Conor McArdleView profile

Assistant Professor

• Telecommunications
• Data Centre Networks
• Optical Networking
• +3 more

Conor McArdle is an Assistant Professor in the School of Electronic Engineering at Dublin City University (DCU). His research focuses on telecommunications networks, datacentre architectures, and optical switching technologies, supported by EU and SFI grants. He has co-authored over 40 publications and co-supervised four PhD students and one MEng student in topics like high-speed optical networks and packet switching protocols. His teaching includes object-oriented programming, network modeling, and algorithms. Research Interests: Optical networking for data centers Energy-efficient network design Telecommunications security Network performance analysis Recent publications emphasize agile optical data center networks, reconfigurable architectures, and hybrid buffering systems. He collaborates with industry on network management and security aspects of mobile networks. Grants & Awards: Extensive EU/SFI-funded projects on network performance and energy efficiency. No specific awards listed. Teaching: Coordinates modules in Algorithms for Engineers (EE324), Network Analysis (EE517), and Programming (EE219).