جست‌وجوی استاد و پژوهشگر

Hans Holger Hundborg Kossمشاهده پروفایل

دانشیار

• Wind Engineering
• Structural Dynamics
• Atmospheric Icing
• +۱۲ مورد دیگر

Hans Holger Hundborg Koss is an Associate Professor in the Section for Structures and Safety within the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), Kgs. Lyngby. His work bridges structural engineering, environmental dynamics, and advanced materials, with strong emphasis on experimental and numerical simulation techniques. He leads research in wind engineering, atmospheric icing, urban climate, and sustainable construction via 3D printing. PhD in Civil Engineering, Ruhr-University Bochum, Germany (2000) Master of Civil Engineering, Ruhr-University Bochum, Germany (1994) European Research Network (HCM) on Wind Engineering, CSTB Nantes & DMI Lyngby (1994) His research focuses on wind effects on structures , particularly high-rise buildings and cable-supported bridges, using wind tunnel testing and computational fluid dynamics. He investigates atmospheric icing on bridge cables and wind turbine blades, including thermodynamic behavior of smart icephobic surfaces. His work extends into urban environmental engineering , assessing wind and thermal comfort, snow accumulation in Arctic regions, and exhaust dispersion. More recently, he has pioneered research in 3D additive manufacturing of structural components and developed biopolymer-based composites as sustainable building materials. His projects also include topology optimization and structural health monitoring under stochastic loads. The recent publications highlight a strong trend toward data-driven structural health monitoring and sustainable material innovation . His work combines experimental validation with computational modeling, particularly in vibration-based damage detection and rheological characterization of novel concrete mixtures. The integration of environmental variables and ambient excitation in monitoring systems reflects a modern, systems-level approach to civil infrastructure resilience. While no specific scientific awards are listed in the provided text, his sustained research output and leadership in multiple PhD projects indicate recognition within the field. Hans Holger Hundborg Koss actively supervises PhD students and leads several funded research projects, including those on structural health monitoring of offshore platforms, optimized 3D-printed constructions, wind load modeling via CFD, Arctic built environments, and aerodynamics of iced bridge cables. These projects reflect interdisciplinary collaboration with experts in materials, structural dynamics, and environmental engineering. He is involved in experimental work at DTU’s specialized Climatic Wind Tunnel and contributes to national and international research networks. His lab activities focus on wind tunnel testing, material characterization, and development of diagnostic methods for structural performance and safety.

Tatiana Kalganovaمشاهده پروفایل

استاد

• Artificial Intelligence
• Evolutionary Design and Optimisation
• Evolvable Hardware
• +۹ مورد دیگر

Professor Tatiana Kalganova is a faculty member at Brunel University London, affiliated with the Department of Electronic and Electrical Engineering and the College of Engineering, Design and Physical Sciences. With a career spanning over two decades at Brunel, she has established expertise in Artificial Intelligence, Evolvable Hardware, and Operational Research. Education: PhD in Evolutionary Computing, Napier University MSc (distinction) in Informatics, Belarusian State University of Informatics and Radio-Electronics Research-Engineer Degree, Belarusian State University of Informatics and Radio-Electronics Her research focuses on Evolutionary Design , Swarm Optimization , and Robotics , with applications in supply chain modeling, neuromorphic computing, and intelligent systems. Recent publications emphasize Large Language Models and data-efficient machine learning techniques. Scientific Awards: 2nd place in Caterpillar's Research and Innovation in Demand Strategy Competition (2012) AFWERX Challenge Award (2019-2020) Professor Kalganova has supervised numerous PhD students on topics like 3D Autorouting Systems and Ambidextrous Robot Hands . Her funded projects include the Horizon Europe Guarantee's ReCharged initiative for climate-resilient infrastructure and collaborations with Intel, Caterpillar, and the Nuffield Foundation.

Gareth Conduitمشاهده پروفایل

استاد

• Strongly correlated electron systems
• Machine learning for materials discovery
• Machine learning for drug discovery
• +۵ مورد دیگر

Gareth Conduit is a Professor-level Research Fellow in the Theory of Condensed Matter Group at the Cavendish Laboratory, University of Cambridge, and a Fellow of Gonville and Caius College. He leads a multidisciplinary programme that couples condensed-matter theory with modern machine-learning methodology to accelerate the discovery of functional materials and pharmaceuticals. Education & Affiliations: Fellow of Gonville and Caius College, Cambridge Research Fellow, Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge Research Interests: Conduit’s work spans two major themes. The first is strongly correlated quantum matter , where he employs quantum Monte-Carlo techniques to explore itinerant ferromagnetism, superconductivity and few-fermion systems, often in collaboration with ultracold-atom experiments. The second is AI-driven materials & drug discovery , where his group develops the Alchemite™ probabilistic deep-learning platform to impute sparse, noisy experimental data and design new alloys, superalloys, concrete formulations and therapeutic candidates. Projects have led to the experimental validation of five proprietary alloy families and are now commercialised through the spin-out Intellegens . Publication Trends: His 2022-2025 output demonstrates a strategic shift toward large-scale, multi-task deep learning deployed on industrial data sets—ranging from 3-D woven composites and superalloy microchemistry to concrete strength and nicotine pharmacokinetics—while maintaining foundational contributions to quantum many-body theory and metamaterial design roadmaps. Scientific Awards: No awards are listed in the supplied material. Funding & Commercial Impact: Conduit has secured substantial industrial funding ( Rolls-Royce, Johnson Matthey, AstraZeneca, Unilever, etc.) to translate AI predictions into certified materials and drug candidates. The Alchemite engine is now embedded in industrial R&D workflows for aerospace alloys, battery electrodes and pharmaceutical formulations. Teams & Facilities: He leads an active research group within the TCM theory division, maintains close ties with the Cavendish’s ultracold-atom experimental programme, and collaborates globally through the OPTIMADE consortium for materials data interoperability.

• Remote Sensing
• Geographic Information Systems
• Digital Agriculture
• +۶ مورد دیگر

Dr. Daniel Simms is a Senior Lecturer in Remote Sensing at Cranfield University's Environment Centre, specializing in applied remote sensing and GIS for land and agricultural information. With over two decades of experience, his career spans from GIS technician roles to leading significant international projects focused on environmental monitoring and agricultural applications. His educational background includes a degree from the University of Plymouth (2000), followed by an MSc in Geographical Information Management at Cranfield University. Dr. Simms has held various professional positions including Spatial Data Manager for Kent County Council and has been with Cranfield University since 2004. University: Cranfield University Department: Cranfield Environment Centre Academic Rank: Senior Lecturer Professional Recognition: Fellow of the Higher Education Academy (FHEA) Dr. Simms' research focuses on the integration of satellite and aerial imagery with ancillary spatial datasets for crop detection and cultivation estimation from field to regional scale. His work emphasizes developing methodologies for deriving accurate and timely information from remotely sensed data with minimal ground-based sampling requirements. Key research areas include Digital Agriculture, Monitoring and Environmental Informatics, and Natural Capital assessment. His publication record shows a clear progression from traditional remote sensing applications toward more advanced machine learning and deep learning techniques, particularly evident in his recent work (2022-2024) which heavily features convolutional neural networks, semantic segmentation, and hyperspectral analysis for agricultural monitoring and environmental assessment. Fellow of the Higher Education Academy (FHEA) Dr. Simms has extensive experience delivering training in remote sensing and GIS techniques, including to Afghan nationals under UN-sponsored capacity building projects and for the UK component of the 2013 EU LUCAS survey. His research has been supported by diverse clients including the UK Government, United Nations Office on Drugs and Crime, EPSRC, EU Framework Programme 7, and Energy Technologies Institute. His current research projects include Enhancing Potato Dormancy, RestREco (Restoring resilient ecosystems), OMENZ (Optimising Miscanthus Establishment), and opium cultivation monitoring in Afghanistan using Earth observation data.

• Autonomous Robotics
• Computational Intelligence
• Human-machine Interaction
• +۳ مورد دیگر

Dr. Wai-keung Fung is a Senior Lecturer in Electronics, Robotics & Control Engineering at Cardiff Metropolitan University's Cardiff School of Technologies. He holds a PhD in Mechanical and Automation Engineering from the Chinese University of Hong Kong (2001) and has held academic roles at the University of Manitoba (Canada) and Robert Gordon University (UK). He is Programme Director for the BEng/MEng in Electronics and Computer Systems Engineering and co-leads the EUREKA Robotics Centre. He is a Fellow of the Higher Education Academy (FHEA), IEEE Senior Member, and IET member. His research focuses on Autonomous Robotics , Computational Intelligence , and Human-machine Interaction , with recent work spanning gender bias in speech processing, underwater communication systems, and medical diagnostics via EEG analysis. His interdisciplinary contributions bridge robotics, machine learning, and engineering applications. Awards: Fellow of the Higher Education Academy (FHEA) since 2015 IEEE Senior Member since 2009 IET Member since 2017 Labs & Affiliations: EUREKA Robotics Centre (Cardiff School of Technologies) His research has been published in IEEE conferences and journals, addressing challenges in robotics, medical AI, and pipeline monitoring. He continues to advance applied technologies with industry and academic collaborations.

Zhanyang Zhangمشاهده پروفایل

استاد

• Wireless Communication and Networks
• Cyber-Physical Systems & IoT/Sensor Networks
• Numerical Modelling and Simulation
• +۶ مورد دیگر

Dr. Zhanyang Zhang is a Professor of Computer Science at the City University of New New York, jointly appointed at the College of Staten Island and the CUNY Graduate Center. His expertise bridges wireless networking, cyber-physical systems, IoT, numerical simulation and high-performance computing, with recent emphasis on 5G/edge intelligence, ocean oil-spill modelling, and assistive technologies for the visually impaired. Education: Ph.D. in Computer Science, City University of New York M.S. in Computer Science, College of Staten Island B.E. in Computer Engineering, Jilin University, Changchun, China Research Interests: Dr. Zhang's research integrates communication theory, data science and large-scale simulation. He develops protocols for next-generation wireless networks, designs energy-efficient algorithms for underwater and terrestrial sensor systems, and applies lattice-Boltzmann methods to environmental disasters such as oil-spill propagation and urban flooding. His group also explores deep-learning-driven anomaly detection in 5G and edge-computing offloading strategies using game-theoretic and reinforcement-learning frameworks. Publication Trends: Across the past decade his scholarly output has shifted from foundational sensor-network coverage and localisation toward interdisciplinary applications: assistive smart-city services, GPU-accelerated ocean-surface transport models, and AI-based traffic and flood prediction. A recurrent methodological thread is the fusion of stochastic modelling (Markov, game, queueing) with high-performance simulation to derive scalable, real-time solutions. Grants & Projects: He currently leads or co-leads projects funded by the U.S. National Science Foundation and multiple CUNY schemes, totalling more than USD 1 M since 2020. Topics range from assistive cyber-connections for the visually impaired to next-generation oil-spill forecasting assimilating large sensor datasets. Labs & Teams: Dr. Zhang heads the Wireless & Cyber-Physical Systems Research Group at CSI, maintains affiliations with the CUNY High-Performance Computing Center, and supervises doctoral students within the Ph.D. Program in Computer Science at the Graduate Center.

Cristian Romanمشاهده پروفایل

پژوهشگر ارشد

• Biomedical Engineering
• Wearable Technology
• Health Monitoring Systems
• +۳ مورد دیگر

Cristian Roman is a Senior Research Fellow at the Oxford Institute of Biomedical Engineering (IBME), University of Oxford. He leads research in biomedical signal processing and m-Health, focusing on wearable systems for clinical deterioration prediction and chronic disease management. His work integrates machine learning, mobile health applications, and real-time decision-making to support self-monitoring in conditions like hypertensive pregnancies and post-stroke care. He holds a PhD in scheduling algorithms for wireless networks and dual MSc degrees in Computer Science and Broadband Networks. Education: PhD: Scheduling Algorithms for Wireless Vehicular Networks MSc: Computer Science MSc: Broadband Networks His research interests emphasize developing wearable technologies and telemedicine solutions to improve patient outcomes. Key projects include the TASMIN5S trial for stroke/TIA blood pressure monitoring and the My Pregnancy Care Intervention for hypertensive pregnancies. He also contributed to the TrAFFIC study tracking atrial fibrillation post-intensive care and the +Stay-Active app for gestational diabetes management. His recent work addresses pandemic-era healthcare challenges, such as rapid implementation of blood pressure monitoring during the COVID-19 pandemic. Collaborative efforts include the Physician Optimised Post-partum Hypertension (POP-HT) trial, which evaluates long-term blood pressure control strategies post-hypertensive pregnancy. Labs & Teams: Part of the Biomedical Signal Processing & m-Health group led by Prof. Lionel Tarassenko, focusing on translational research bridging engineering and clinical needs.

Saber Mohamed Elsayed Elsayedمشاهده پروفایل

دانشیار

• Evolutionary Computation
• Swarm Control
• Planning and Scheduling
• +۴ مورد دیگر

Associate Professor Saber Mohamed Elsayed Elsayed is affiliated with the School of Engineering and Information Technology (SEIT) at the University of New South Wales (UNSW) Canberra, Australia. He holds a PhD in Computer Science from UNSW Canberra (2012). His research focuses on computational intelligence, swarm guidance, and optimization, with applications in defense, logistics, engineering, and business. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation and is a Senior IEEE member. Education: PhD in Computer Science, UNSW Canberra, 2012 Research Interests: Dr. Elsayed’s expertise spans evolutionary computation, swarm control, planning and scheduling, optimization, neural networks, and decision support systems. His work addresses challenges in autonomous systems, sensor placement, and multi-agent coordination, leveraging computational intelligence techniques. Awards: Winner of IEEE CEC/WCCI competitions (2022, 2020, 2016, 2014, 2011) Best student paper award (2021) UNSW Publication Fellowship and scholarships Grants & Mentorship: He leads or co-leads multiple grants, including ARC DP-funded research on electric vehicle logistics (2025-2027) and DSTG-funded projects on sensor placement and swarm control. He mentors numerous PhD and Master’s students, focusing on optimization, scheduling, and swarm intelligence. Current advisees include Ahmed Gad (PhD) and Setyo Tri Windras Mara (PhD). Teaching: Teaches courses such as Data Structures and Representation (ZEIT2103) and Introduction to Programming (ZEIT1102).

• Electric and Hybrid Vehicles
• Mechatronics & Advanced Controls
• On-Road Vehicle Dynamics
• +۷ مورد دیگر

Dr. Abbas Fotouhi is a Reader in Vehicle Engineering and Transport Systems at Cranfield University , with over 20 years of experience in systems modeling, simulation, optimization, and control. His work bridges artificial intelligence and vehicle engineering, focusing on electrified transport, battery systems, and autonomous vehicles. Expert in electric/hybrid vehicles , mechatronics , and low-carbon technology Pioneering research on lithium-sulfur battery state estimation , thermal management , and automotive AI/ML applications Led £20m+ in research bids , securing £2.2m as investigator Research Trends: His recent publications address lithium-sulfur battery thermal modeling, eco-driving control for connected vehicles, and AI-driven race strategy optimization. Subfields include battery health estimation, trajectory prediction, and hybrid powertrain design. Awards: Fellow of the UK Higher Education Academy (FHEA) Fellow of the Faraday Institution Senior Member of IEEE (SMIEEE) Advising: Supervised 9 PhD students and over 60 MSc students. Alumni include researchers in battery management , driver distraction detection , and fleet optimization .

• Fault Detection
• RISC-V Architecture
• Spiking Neural Networks
• +۵ مورد دیگر

Malachy McElholm is a Lecturer in Electrical Engineering at Ulster University, affiliated with the School of Computing, Engineering and Intelligent Systems at the Magee Campus in Derry~Londonderry. His work spans multiple research areas at the intersection of computer engineering and electrical systems. Dr. McElholm's research interests focus on Fault Detection in processor architectures, particularly RISC-V implementations, using innovative approaches like Spiking Neural Networks . His work extends to Structural Health Monitoring systems, Self-Repairing Hardware , and applications of artificial intelligence in Renewable Energy Systems , particularly in agricultural settings. His research contributes to multiple United Nations Sustainable Development Goals. Analysis of Dr. McElholm's recent publications reveals a strong focus on hardware reliability and fault tolerance mechanisms. His 2025 publications center on watchdog mechanisms for RISC-V architecture using neural networks, demonstrating his continued innovation in hardware security and reliability. Earlier work includes applications of spiking neural networks for structural health monitoring (2020) and self-repairing hardware systems (2016), showing a consistent research trajectory in bio-inspired computing approaches to hardware challenges. Modelling and Optimising on-farm Renewable Energy Generation and Consumption using Artificial Intelligence (2024-2028) Nervous Systems project (2022-2026) Heat and Energy Education and Empowerment for Rural Areas (2021-2024) iTEMID - Intelligent Total Energy Management in Dairying - Phase 2 (2020-2021) Multi-User Vector Network Analysis (VNA) Facility Core Equipment His research has resulted in a h-index of 4 with 59 citations, demonstrating meaningful impact in his fields of expertise. Dr. McElholm frequently collaborates with colleagues including Dr. Jim Harkin, Dr. Liam McDaid, and others across multiple projects and publications, with recent press coverage on renewable energy applications in dairy farming.

• Modelling dynamic and ultrasonic response of complex structures
• Design optimisation of multifunctional structures and metamaterials
• Structural health monitoring (SHM)
• +۲ مورد دیگر

Dimitrios Chronopoulos is an Associate Professor in the Department of Mechanical Engineering at KU Leuven , Faculty of Engineering Technology. His research focuses on structural health monitoring, design optimization of composite and metamaterial structures, and wave propagation analysis for damage prognosis. Unit: Mecha(tro)nic System Dynamics (LMSD), Ghent and Aalst Campuses Research Affiliations: Leuven Centre for Affordable Sustainable Technology, LIM - KU Leuven Institute for Mobility His work integrates physics-informed machine learning and computational modeling to address challenges in predictive maintenance of smart composite systems, including aircraft and wind turbine components. Recent projects emphasize digital twins, Bayesian frameworks, and ultrasonic sensor networks for real-time damage assessment. Publications highlight trends in deep learning for SHM , topological phononic crystals , and multi-scale damage detection using hybrid models. He actively supervises students in advanced ultrasonic monitoring and predictive analytics.

Pierre Parrendمشاهده پروفایل

استاد

• Artificial Intelligence
• Security of Complex Systems
• Software Security
• +۵ مورد دیگر

Pierre Parrend is Professor (HDR) of Computer Science at EPITA Strasbourg and a core member of the CSTB team within the ICube Laboratory . He previously served as Professor of Higher Education at ECAM Strasbourg-Europe (2019-2021) and University of Strasbourg (2016-2019), where he also headed the Computer Science & Mathematics Department and the Expertise & Recruitment Pole. Education & Academic Path PhD in Computer Science, INSA de Lyon, 2008 – “Software Security Models for Service-Oriented Programming (SOP) Platforms” Master Informatique de Lyon – Mastria Studies at INSA Lyon (Telecommunications, Services & Uses), UPC Barcelona (Spain), and Karlsruhe Institute of Technology (Germany) Research Focus His research lies at the intersection of Artificial Intelligence , Security of Complex Systems and Translational Bioinformatics . He designs secure architectures for the Industry of the Future , IoT health data and cyber-physical systems . Core themes include anomaly detection in AI ecosystems, evolutionary optimisation techniques, and component-based middleware security. Recent Articles & Trends Between 2007-2009 he produced pivotal works on OSGi security, vulnerability benchmarking and privacy-aware service integration. His publications reveal a consistent focus on static analysis of Java/OSGi components , runtime security for extensible platforms , and quantitative security metrics such as the Protection Rate. Grants & Projects HuMa (FUI AAP-19) – Human-centred analysis of massive data for security SENSAI – Sensor network security via artificial immune systems GenIDA – Secure social network for Medicine 4P 4P-Factory – Factory of the Future, UNESCO UNITWIN Complex Systems Digital Campus ComplexLean – Lean organisation modelling as complex systems RDR – Robust Digital Records with Cheikh Anta Diop University, Senegal EASEA – Evolutionary algorithms platform Doctoral Supervision & Labs He currently supervises four PhD students on explainable anomaly detection, autonomous evolutionary computers, magnetocaloric cooling optimisation and metaheuristic learning loops. Former graduates worked on industrial cybersecurity projects with IPLine, Hager and ComplexLean consortia. His laboratory hosts the BICS Platform and the e-laboratory of the UNITWIN UNESCO Complex System Digital Campus .

• Production management systems
• Supply-chain management
• Quality management
• +۴ مورد دیگر

Kenichi Nakashima is Professor at Waseda University’s School of Social Sciences, Tokyo, specialising in production management, supply-chain control, quality management and environmentally conscious manufacturing. He previously served as Professor and Senior Advisor to the President at Kanagawa University and as Visiting Assistant Professor at MIT’s Laboratory for Manufacturing & Productivity. He is Vice-President of the Japan Industrial Management Association, a board member of the International Foundation for Production Research, and serves on the Deming Prize Selection Committee. Education: Ph.D. in Engineering Research interests span social-systems engineering, supply-chain management, JIT/kanban systems, remanufacturing, closed-loop logistics, Industry 4.0 applications with IoT, and environmental management systems. His recent work integrates data-driven optimisation with sustainable manufacturing and digital-transformation strategies. Across 43 Scopus-listed papers (h-index 12, 518 citations) his publications analyse stochastic production control, multi-kanban policies, warranty/maintenance of sensor-embedded products, pricing of IoT-based services, and risk mitigation in global supply networks. He has co-authored multiple books including Introduction to Manufacturing Management and Responsible Manufacturing . Selected honours: Japan Industrial Management Association Encouragement Award 1996 He currently leads a Grant-in-Aid project on environmentally conscious SCM for a super-smart society (2020-2024) and has completed SIP and JSPS projects on supply-chain resilience, remanufacturing market diffusion, and green factory models. He teaches Production Systems, Global Supply-Chain Management, TQM and Informatics at both undergraduate and graduate levels.

Hao Chengمشاهده پروفایل

استادیار

• Artificial Intelligence for Geo-Science
• Computer Vision
• Autonomous Driving
• +۴ مورد دیگر

Dr Hao Cheng is an Assistant Professor (from 1 Oct 2024) in the Department of Earth Observation Science, ITC Faculty, University of Twente, the Netherlands. He currently holds a Marie Skłodowska-Curie European Postdoctoral Fellowship focused on vehicle-vulnerable road user interactions for safer intelligent transportation and autonomous driving systems. Education: Ph.D. (with distinction) in Civil Engineering & Geodetic Science, Leibniz Universität Hannover, Germany, 2021 M.Sc. (with distinction) in Internet Technologies & Information Systems, joint programme of TU Braunschweig, Leibniz Universität Hannover, TU Clausthal & University of Göttingen, Germany, 2017 Research interests lie at the intersection of Artificial Intelligence and Geo-Information Science , with principal themes: Deep learning & computer vision for road-user behaviour modelling Trajectory prediction and motion forecasting for autonomous driving Interaction & safety analysis between vehicles and vulnerable road users (pedestrians, cyclists) Ethical, explainable and accessible AI for geospatial applications Graph neural networks, diffusion models and transformer architectures applied to dynamic scene understanding His recent publication portfolio (2018-2025) demonstrates a strong methodological core in deep learning and computer vision deployed across transportation safety , 3D mapping , remote sensing and human-machine interaction . A notable trend is the migration from early LSTM-based traffic modelling toward contemporary transformer, graph and diffusion frameworks that deliver diverse, controllable and interpretable predictions for real-world autonomous-driving scenes. Scientific recognition: Marie Skłodowska-Curie Actions European Postdoctoral Fellowship (MSCA) – VeVuSafety project Grants & projects: Cheng is principal investigator of the MSCA-funded VeVuSafety project (Grant 101062870) which develops learning-based models of vehicle-VRU interactions to enable safer intelligent transport systems. Labs & teams: From October 2024 he will lead research activities within the Department of Earth Observation Science at ITC, University of Twente, collaborating with the broader ITC AI-for-Geo groups and transportation-safety institutes across Europe.

• Interventional Cardiology
• Coronary Physiology
• Fractional Flow Reserve
• +۵ مورد دیگر

Dr Timothy Lockie is a clinical academic at King’s College London whose research focuses on invasive coronary physiology, fractional flow reserve, and revascularisation strategies in high-risk cardiac patients. He has led or co-led three British Heart Foundation projects and co-authored 14 peer-reviewed articles accumulating 581 citations. Education: Not explicitly stated in the supplied text. Research Interests: His work spans interventional cardiology, microvascular dysfunction, cardiogenic shock, and risk prediction after cardiac arrest. He employs dual-sensor pressure-flow wires, wave-intensity analysis, and large multi-centre registries to optimise patient selection for percutaneous coronary intervention (PCI). Publication Trends: Recent papers address completeness of revascularisation in ischemic cardiomyopathy, early neurological prognostication after out-of-hospital cardiac arrest, and physiological mechanisms of angina relief with nitroglycerin, reflecting a blend of randomised trials, physiological studies, and big-data cohort analyses. Projects & Funding: Dual-sensor pressure and flow measurements in coronary microvascular assessment (BHF, 2011-2014) – Co-PI Dual pressure-flow wire prediction of ischaemia post-MI (2008-2010) – Co-I Post-conditioning in acute MI randomised trial (2007-2011) – Co-I Collaborations: He publishes extensively with the London Heart Attack Group and colleagues such as Professors Marber, Redwood, MacCarthy and Perera, indicating embedded involvement in high-impact cardiovascular research networks.