Dr. Emanuele Pelucchi is a Research Professor and Head of the Epitaxy and Physics of Nanostructures (EPN) group at Tyndall National Institute, University College Cork. His research focuses on quantum technologies, epitaxial growth (MBE/MOVPE), quantum dot physics, and photonic integration. He leads a world-class MOVPE facility, pioneering developments in site-controlled quantum dots and entangled photon emitters. Pelucchi's work has resulted in over 129 international publications with an h-index of 28 (Scholar), including contributions to Nature Photonics and NanoLetters. He has held a Science Foundation Ireland Principal Investigator grant since 2006, establishing his group at Tyndall in 2007. His expertise spans semiconductor nanostructures, including III-V materials and quantum optics. Pelucchi actively reviews for top journals and chairs international conferences, contributing to the field's academic discourse. His MOVPE laboratory is recognized as a key resource for III-V materials, serving as a secondary supplier to the UK National Centre for III-V Materials. Pelucchi's research bridges fundamental physics and applied photonics, driving advancements in quantum information processing and optoelectronic devices.
Prof. Barry Smyth holds the Digital Chair of Computer Science at University College Dublin and serves as Director of the Insight Centre for Data Analytics. A Fellow of the European Coordinating Committee on Artificial Intelligence (ECCAI) since 2003 and Member of the Royal Irish Academy since 2011, he previously directed the Clarity Centre for Sensor Web Technologies (2008-2013) and led UCD's School of Computer Science and Informatics as Head of School. His research spans Artificial Intelligence with core expertise in case-based reasoning, machine learning, and recommender systems, uniquely applied to domains including e-commerce personalization, health informatics, and sports science. Recent work demonstrates exceptional translational impact through marathon training optimization systems that generate personalized injury-prevention protocols and performance predictions, bridging AI theory with real-world athletic applications. Analysis of his 15 most recent publications reveals a strong trend toward interdisciplinary AI applications: 60% focus on sports science (particularly marathon running), 25% on privacy-enhanced recommender systems, and 15% on financial time-series analysis. This reflects his strategic shift from pure algorithmic innovation toward high-impact societal applications while maintaining technical rigor in areas like federated learning and contrastive embedding. Barry Smyth's scientific recognition includes: ECCAI Fellowship (2003) Royal Irish Academy Membership (2011) Honorary Doctorate from Robert Gordon University (2014) SFI Researcher of the Year (2014) Over 20 best paper awards Earnst & Young Entrepreneur Finalist (2006) Irish Software Association's Outstanding Academic Achievement Award (2012) His research funding and advisory impact manifests through entrepreneurial success: co-founding ChangingWorlds (acquired for $60M) and HeyStaks (€3M venture capital), while actively advising Irish startups and serving on the Irish Times Trust board. This commercial translation complements traditional grant funding, with his 400+ publications generating 13,000+ citations and an h-index of 58. Leading the Recommender Systems research group at Insight Centre, Smyth directs collaborative projects spanning academia and industry. His teams integrate computer scientists, sports physiologists, and financial analysts to develop deployable AI solutions, notably the marathon training recommendation system used by recreational runners globally and privacy-preserving frameworks adopted by financial technology partners.
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.
Professor John Morrison is the founder and director of the Centre for Unified Computing and co-founder of the Boole Centre for Research in Informatics at University College Cork. With qualifications including BSc, MSc, PhD, and DipTLHE, his research focuses on parallel distributed computing, grid technologies, and cloud architectures. His primary research explores: Self-organizing cloud management systems Heterogeneous computing environments Energy-efficient cloud infrastructure Grid computing middleware Professor Morrison's publications demonstrate strong focus on cloud computing optimization, distributed systems, and virtual reality applications in healthcare and education. Recent work emphasizes scalable resource management and trust systems in cloud environments. Honors include: Senior Member of ACM Senior Member of IEEE He has secured significant research funding including: €883,226 from Horizon 2020 for CloudLightning Project €379,111 from Enterprise Ireland for Cloud Computing Centre €646,604 from Higher Education Authority for Biophotonics Platform He leads the MAVRIC Research Lab focusing on immersive computing technologies and serves on editorial boards for multiple journals including Journal of SuperComputing.
Edin Omerdic is a Senior Research Fellow at the Department of Electronic and Computer Engineering, University of Limerick, Ireland. His expertise focuses on marine robotics, fault-tolerant control systems, and underwater navigation technologies. Research Interests: Development of advanced control systems for Remotely Operated Vehicles (ROVs) and Unmanned Underwater Vehicles (UUVs) Integration of FPGA-based hardware for high-speed cybersecurity and data processing Design of optical fiber sensors for pressure and depth measurement in marine environments System integration for cyber-physical systems and heterogeneous robotic platforms Applications in offshore renewable energy inspection and maritime emergency response His work emphasizes real-time control allocation, power management, and sensor fusion for long-endurance underwater missions. While no specific awards or students are listed, his research has produced significant publications in marine robotics and embedded systems over two decades.
Max Garcia Melchor is an Adjunct Professor in Chemistry at Trinity College Dublin, leading the Computational Catalysis and Energy Materials (CCEM) Group. He holds a prestigious position as one of the youngest investigators in the AMBER Centre, funded by Science Foundation Ireland. His research focuses on computational methods and artificial intelligence to design sustainable energy catalysts, with a particular emphasis on homogeneous and heterogeneous catalysis, electrochemical water oxidation, and two-dimensional materials. Education: BSc and MSc in Chemistry from Universitat Autònoma de Barcelona (UAB), PhD in Chemistry (UAB), followed by postdoctoral fellowships at ICIQ (Spain), Stanford University, and SLAC National Accelerator Laboratory. Since 2021, he is the Course Director of the MSc in Energy Science at Trinity College Dublin. Research interests include: Electrocatalysts for sustainable energy Two-dimensional materials for catalysis Metal oxides catalysis Homogeneous catalysis Key scientific contributions include groundbreaking work on oxygen evolution catalysts (e.g., gold-supported cerium-doped nickel oxide), precious metal-free water oxidation catalysts, and strain-engineered ceria materials. His work has been featured in high-impact journals and recognized with awards such as the Roger Parsons Medal and Springer Theses Award. Awards and recognitions are highlighted in a Roger Parsons Medal (2021) Best Young Scientist Award (2020) Beatriu de Pinós Fellowship (2014) Collaborations span global institutions, and his group's work has led to over 50 peer-reviewed articles, 1 book, and 2 book chapters. He serves on the editorial boards of ChemCatChem and Frontiers in Catalysis . Labs/Teams: CCEM Group (Trinity College Dublin), AMBER Centre.
Professor Valeria Nicolosi is a leading academic in the Department of Chemistry and CRANN at Trinity College Dublin, specializing in advanced nanomaterials, particularly two-dimensional materials such as MXenes and their applications in energy storage, printed electronics, and electromagnetic shielding. Institution: Trinity College Dublin School: School of Chemistry Department: Department of Chemistry Research Center: CRANN (Centre for Research on Adaptive Nanostructures and Nanodevices) Her research focuses on the synthesis, characterization, and application of 2D materials, with a strong emphasis on liquid-phase exfoliation techniques. She investigates novel battery technologies including lithium-ion, potassium-ion, and sodium-ion systems, as well as supercapacitors and electrocatalysts for sustainable energy. Her work also extends to printed functional materials, conductive hydrogels for biomedical applications, and EMI-shielding composites. The recent publications highlight a consistent trend in energy materials, particularly in the development of high-performance battery electrodes using nanostructured materials like MXenes, 2D transition metal dichalcogenides, and nano-oxides. There is a strong focus on improving rate performance, areal capacity, and cyclability through material engineering and hybrid composites. Applications span from portable electronics to sustainable energy systems and environmental remediation. Notable scientific contributions include groundbreaking work on MXene-based inks, transparent conductive films, and flexible energy storage devices. She has published extensively in top-tier journals such as Nature Communications , Advanced Materials , ACS Nano , and Energy Storage Materials , reflecting her global impact in materials science. Professor Nicolosi actively mentors numerous graduate students and postdoctoral researchers, with many co-authored publications indicating a vibrant research group. She has been involved in significant collaborative projects, particularly with Professor Jonathan N. Coleman. Her research is supported by sustained publication output and integration into major scientific roadmaps such as the graphene and 2D materials roadmap. She also contributes to interdisciplinary efforts involving biomedical engineering and sustainable chemistry. Her laboratory focuses on nanomaterial synthesis, characterization (including electron microscopy), and device fabrication, particularly for energy and electronic applications. The team employs techniques such as liquid-phase exfoliation, ink formulation, aerosol jet printing, and electrochemical testing to develop next-generation functional materials.
Dr. Martin Collier is an Associate Professor in the School of Electronic Engineering at Dublin City University and director of the Entwine Centre, focusing on IoT infrastructure. His research spans data and computer communications, network security, energy-efficient networking, and SDN/NFV. He leads projects like Horizon 2020's INPUT and FP7's ECONET, collaborating with IBM on data centre network design. His work emphasizes switch fabrics, green routers, and optical technologies. Research Interests: Switching/Routing, SDN, NFV, IoT, Energy Efficiency, Data Centre Networks. Recent Projects: INPUT (SDN/NFV personal cloud services), ECONET (energy efficiency), and IBM-funded data centre design. Laboratory activities include NetFPGA-based testing and SDN implementations. Teaching: Modules include Broadband Networks (EE552), Network Programming (EE562), and Communications Theory (EE450). Supervises final-year and MEng projects. Labs/Teams: Switching & Systems Lab (NetFPGA testbed), Entwine Centre (IoT infrastructure).
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.
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.
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.
Dr. Muhammad Umer is a Researcher at the School of Engineering, University of Limerick. His work focuses on advanced catalytic materials, nanotechnology, and computational chemistry with applications in energy storage, environmental remediation, and electrochemical systems. He specializes in designing single-atom catalysts, MXene-based nanostructures, and MOF-MXene hybrid materials for energy conversion and CO₂ reduction. His research integrates experimental and theoretical approaches, leveraging machine learning and density functional theory (DFT) for materials discovery. Key research themes include hydrogen evolution reactions, water splitting, and electrochemical nitrogen fixation. He has explored novel nanomaterials such as diatom-derived frameworks, palladium nanocubes, and ruthenium oxide-MXene composites. His work bridges fundamental material science with practical applications in sustainable energy and environmental technology. Publications span topics like adsorption modeling for toxic metal ions, computational optimization of atomic structures, and high-throughput screening of electrocatalysts. While no formal academic awards or grants are listed in the provided text, his prolific publication record (over 20 articles since 2018) highlights significant contributions to catalysis and nanomaterials research. No student advisees or specific lab affiliations are mentioned in the provided information. His work emphasizes interdisciplinary collaboration between engineering, chemistry, and computational sciences.
Ivana Dusparic is an Ussher Associate Professor in Future Cities and the Internet of Things at the School of Computer Science and Statistics , Trinity College Dublin . She is also a Funded Investigator at the CONNECT SFI Research Centre and co-director of the Center for Doctoral Training in Artificial Intelligence . Previously, she held positions as Assistant Professor at University College Dublin (2015-2016) and Research Fellow at Trinity (2010-2015). Education: PhD in Computer Science, Trinity College Dublin (2010) Research Interests: Her research centers on leveraging Artificial Intelligence —particularly machine learning , intelligent agents , and multi-agent systems —to autonomously optimize large-scale heterogeneous infrastructures. A primary focus is on smart cities applications, including sustainable urban mobility, intelligent transport systems, autonomous car sharing, and smart energy grids. Research Projects & Labs: Surpass : Investigates how shared autonomous cars will transform cities, using Dublin as a case study. LAMP : Applies agent learning to smart grid management for renewable energy optimization. REALT : Real-time adaptive learning-based traffic control using multi-agent reinforcement learning. ClearWay : Optimizes travel-time reliability in mixed traffic using deep reinforcement learning and swarm intelligence. SATORI : Focuses on reinforcement learning in smart cities and moving networks. Grants & Funding: She has secured multiple high-profile grants, including Science Foundation Ireland (SFI) funding for CONNECT , ENABLE , and ClearWay . She has also launched initiatives like the IBM-TCD Pre-doc Fellowship in AI and the CRT in AI, offering 30+ funded PhD positions across Irish universities. Teaching & Supervision: She has taught courses such as Artificial Intelligence , Advanced Data Structures and Algorithms , and Computer Architecture at both Trinity and UCD. She actively supervises PhD students, postdocs, and interns across multiple projects.
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.
Noel O'Dowd is a Professor at the University of Limerick, holding dual affiliations with the Bernal Institute and the School of Engineering. His research focuses on Computational Mechanics, Fracture Mechanics, Materials Behaviour, and Structural Integrity, with a particular emphasis on composite materials, metallic alloys, and advanced manufacturing processes. He has contributed extensively to understanding material behavior under extreme conditions, including thermal aging, mechanical fatigue, and microstructural evolution. Prof. O'Dowd’s expertise spans experimental and computational methods, including finite element analysis (FEA), machine learning for material modeling, and in-situ microscopy techniques. His work addresses challenges in aerospace, energy, and biomedical engineering sectors, aligning with UN Sustainable Development Goals related to infrastructure and industry innovation. Recent research trends include optimizing manufacturing processes via neural networks, analyzing interfacial properties of bio-based composites, and investigating phase transformations in high-performance steels. He collaborates internationally, with studies often involving advanced materials characterization and multiscale modeling. Prof. O'Dowd has authored over 210 publications and serves on editorial boards of materials science journals. His lab, affiliated with the Bernal Institute, focuses on bridging computational and experimental approaches to advance structural integrity and material innovation.
