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Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML

• Machine Learning
• Bayesian Optimization
• Gaussian Processes
• +3 more

Michael O'Boyle is a Professor at the University of Edinburgh's School of Informatics, where he serves as Director of the ARM Research Centre of Excellence and the EPSRC Centre for Doctoral Training in Pervasive Parallelism. Holding an EPSRC Established Career Research Fellowship, he leads pioneering work in compiler technology for heterogeneous architectures, bridging theoretical advances with practical high-performance computing applications. Professor O'Boyle's research spans multiple cutting-edge areas including heterogeneous code discovery and optimization, neural machine translation for program synthesis, deep neural network system stack optimization, software-defined hardware, and compiler/architecture co-design. His approach integrates constraint analysis, program synthesis, and machine learning to address complex challenges in high-performance computing across diverse hardware platforms. His recent publications reveal a strong trend toward integrating machine learning with traditional compiler techniques, particularly in neural program synthesis, tensor optimization, and architecture-aware compilation. This work represents a paradigm shift in compiler design, moving from rule-based systems to learning-based approaches that can automatically adapt to diverse hardware targets. IEEE/ACM CGO 2025 Distinguished Paper Award for 'Tensorize: Fast Synthesis of Tensor Programs from Legacy Code' IEEE/ACM CGO 2024 Test of Time Award ACM GPCE 2023 Best Paper Award for 'C2TACO: Lifting Tensor Code to TACOM' ACM ASPLOS 2021 Distinguished Paper Award IEEE HPCA 2021 Best Paper Award for 'Prodigy: Improving the Memory Latency of Data-Indirect Irregular Workloads' Professor O'Boyle has successfully mentored numerous PhD students who have secured prominent positions in academia (including at Cambridge, Edinburgh, Leeds, and McGill) and industry (including Meta, NVIDIA, Qualcomm, Huawei, and Microsoft). His research is supported by significant funding from EPSRC, ARM, and European projects including Bonseyes and Transmuter, demonstrating strong international recognition and industry impact. He leads the influential Compiler and Architecture Design (CArD) Group at the University of Edinburgh and is a founder of the HiPEAC Network of Excellence, which has grown into a major European initiative connecting researchers and practitioners in high-performance and embedded computing.

• Heterogeneous Code discovery and Optimisation
• Neural Machine Translation and Neural Synthesis
• Deep Neural Network system stack
• +7 more

Dr. Mo Adda is a Principal Lecturer at the University of Portsmouth's School of Computing, part of the Faculty of Technology. He holds a PhD in Distributed Systems and Parallel Processing from the University of Surrey. His research focuses on network security, distributed systems, wireless networks, and cybercrime prevention. He leads projects in the Centre for Cybercrime and Economic Crime, exploring fault management in networks, blockchain applications, and IoT forensics. With 16 supervised theses, he advises on topics like energy-efficient cloud systems and machine learning for environmental modeling. His work bridges academia and industry, addressing challenges in software-defined networks, traffic control, and secure data sharing in social networks. Education: PhD in Distributed Systems (University of Surrey) Affiliations: Centre of Excellence in Defence, Risk & Resilience; Portsmouth Centre for Advanced Materials and Manufacturing Research Interests Dr. Adda's research spans: Network security and fault detection mechanisms Blockchain applications in IoT and forensics Energy-efficient cloud data center optimization Machine learning for climate modeling Self-organizing network protocols Grants & Collaborations His projects include collaborations with industry partners on secure data leakage detection in cloud systems and resilient wireless protocols for harsh environments. He has pioneered fault classification systems using clustering algorithms and fuzzy logic. Labs & Teams He contributes to the Centre for Cybercrime and Economic Crime, focusing on digital forensics and network intrusion analysis. His team develops frameworks for privacy management in social networks and proactive routing in software-defined networks.

• Network Security
• Distributed Systems
• Wireless Networks
• +5 more

Dr. Jurgen Becque is an Associate Professor in Structural Engineering at the University of Cambridge's Department of Engineering. He specializes in cold-formed steel structures, stainless steel structural behavior, and stability analysis, with a focus on local-overall buckling interaction and innovative design methodologies. His work bridges experimental investigations with computational modeling and machine learning applications. Research Interests: Cold-formed steel structural systems Stainless steel column stability Local and overall buckling interaction Mechanics-based design optimization Machine learning for structural behavior prediction Recent publications demonstrate expertise in cross-sectional stability, connection mechanics, and composite systems like UHPC-confined stainless steel columns. His work addresses both monotonic and cyclic loading scenarios, contributing to Eurocode 3 design standards.

• Cold-formed steel structures
• Stainless steel structural behavior
• Local and overall buckling interaction
• +3 more

George Danezis is a Professor of Security and Privacy Engineering at University College London (UCL), affiliated with the Information Security Group. He is also a faculty fellow at the Turing Institute and has held part-time roles since 2018 to focus on commercialization. His research spans systems security, privacy, anonymous communications, and blockchain technologies. Danezis co-founded Chainspace (acquired by Facebook in 2019) and Mysten Labs, where he serves as Chief Scientist. He holds a PhD from the University of Cambridge (2004) and has held positions at Microsoft Research, K.U.Leuven, and the University of Cambridge. Education: PhD (Computer Laboratory, University of Cambridge, 2004), B.A. (Hons) in Computer Science (Queens' College, Cambridge, 2000). Research interests include blockchain consensus, decentralized systems, privacy-preserving technologies, and machine learning applications in security. He has advised numerous doctoral students and led projects funded by EU Horizon 2020, EPSRC, and industry partnerships. His work emphasizes practical solutions for privacy, such as Loopix (anonymity system), Narwhal/Tusk (blockchain consensus), and Coconut (privacy credentials). Danezis has received awards for best papers (PET 2002/2018), is a Fellow of the British Computing Society, and advises organizations like Privacy International and DeepMind. Publications focus on blockchain, anonymity, and privacy, with over 100 peer-reviewed papers. Grants include leadership in projects like DECODE (EU), PANORAMIX, and Glass Houses. Teaching includes courses on computer security and privacy technologies at UCL.

• Systems Security
• Privacy
• Anonymous Communications
• +8 more

Hrvoje Jasak is a Professor of Continuum Physics at the Department of Physics (Cavendish Laboratory), University of Cambridge. He holds a fellowship at Christ’s College. His academic journey includes a BSc in Mechanical Engineering from the University of Zagreb (1992) and a PhD in CFD from Imperial College London (1996). Prior to academia, he held engineering roles at CD-adapco (now Siemens PLM), Nabla Ltd, and Ansys-Fluent Inc., contributing to CFD software development. His research focuses on numerical simulation methods, continuum physics, multiphase flows, naval hydrodynamics, and software development. He co-created OpenFOAM, chairs its Numerics Technical Committee, and leads the Computational Continuum Mechanics (CCM) research group within the Laboratory for Scientific Computing. His work integrates advanced numerical techniques like the partially rotating grid method, finite volume algorithms, and multiphysics coupling frameworks. Jasak is a seasoned developer with 25+ years of C++ expertise, having authored ~1 million lines of code. His group’s projects include the Naval Hydro Pack , fluid-structure interaction solvers, and the Eulerian multi-fluid model for dense sprays. He actively collaborates on international initiatives like the NUMAP-FOAM Summer School and the OpenFOAM community. His teaching spans MPhil programs, PhD supervision, and specialized CFD courses. Current research explores wave-ice interaction, lubricated contact modeling, and open-source software innovation. The CCM group’s work bridges academia and industry, addressing challenges in marine engineering, energy systems, and computational mechanics.

• Computational Fluid Dynamics (CFD)
• Continuum Mechanics
• Numerical Simulation Methods
• +5 more

Professor Omar Matar is a Professor of Fluid Mechanics and RAEng/PETRONAS Research Chair in Multiphase Fluid Dynamics at the Department of Chemical Engineering, Imperial College London. He leads the Matar Fluids Group, focusing on interfacial fluid mechanics, multiphase flows, computational fluid dynamics (CFD), and applications in energy, manufacturing, and nanotechnology. His roles include Head of Department of Chemical Engineering, Director of the PETRONAS Centre for Engineering of Multiphase Systems (PETCEMS), and Editor-in-Chief of the Journal of Engineering Mathematics. Education: PhD in Chemical Engineering, Princeton University (1993) MEng Chemical Engineering, Imperial College London (1989) Research Interests: Interfacial fluid mechanics, multiphase flows, CFD, and machine learning 2D materials exfoliation and scale-up, immersive technologies (AR/VR) Applications in energy systems, nanotechnology, and personalized education Awards: Fellow of the Royal Academy of Engineering (2020) Recipient of the Imperial College President’s Medal (2020) EPSRC Programme Grant Principal Investigator (MEMPHIS, PREMIERE) Grants & Projects: MEMPHIS: £5M EPSRC-funded Programme Grant (2012–2017) PREMIERE: EPSRC Programme Grant (2019–present) PETCEMS: PETRONAS-funded Centre for Multiphase Systems Engineering Labs & Collaborations: Leads the Matar Fluids Group, collaborating with institutions like UCL, University of Edinburgh, and industry partners such as BP and First Light Fusion. Active in developing high-performance CFD codes (e.g., BLUE) and machine learning-driven models for multiphase systems.

• Interfacial Fluid Mechanics
• Multiphase Flows
• Computational Fluid Dynamics (CFD)
• +7 more

Judith Driscoll is Professor of Materials Science at the University of Cambridge in the Department of Materials Science & Metallurgy. She holds the prestigious Royal Academy of Engineering Chair in Emerging Technologies and serves as a Visiting Staff Member at Los Alamos National Laboratory. As the founding Editor-in-Chief of APL Materials, she has significantly contributed to the materials science community. Dr. Driscoll's research focuses on Energy Efficient Oxide Materials for Information and Communications Technologies and energy devices. Her work spans the development of non-volatile memory, resistive switching devices, and ferroelectric materials for neuromorphic computing applications. She investigates oxide thin films for applications ranging from data storage to energy generation and conversion, with particular emphasis on creating more energy-efficient device technologies to handle the exponential growth of data-centric applications. Her recent publications demonstrate strong trends in developing novel oxide-based memory devices with improved energy efficiency, particularly for AI applications. The work shows significant progress in hafnium-zirconium oxide ferroelectrics, resistive switching mechanisms, and vertically aligned nanocomposite structures for enhanced device performance. These innovations address critical challenges in reducing the unsustainable energy demands of modern computing, particularly for artificial intelligence systems. Fellow of the Royal Academy of Engineering Fellow of the Materials Research Society Fellow of the American Physical Society Fellow of IOM3, IOP, and Women Engineers Society Fellow of the American Academy of Arts and Sciences Recipient of ERC Advanced Grant Editor-in-Chief of APL Materials Dr. Driscoll leads a vibrant research group that has secured significant funding including her Royal Academy of Engineering Research Chair, an ERC Advanced Grant, and an ECCS-EPSRC grant in collaboration with researchers from the USA. She has founded the Cambridge Centre for Neuromorphic Computing (Neucam) in 2023. Her group operates world-leading growth equipment including pulsed laser deposition with RHEED control, high temperature oxide sputtering, and spatial ALD systems. She collaborates extensively across the University of Cambridge and with international partners to solve complex materials challenges, with her group's role often being to identify optimal materials for functional goals, predict fabrication methods, and then create and characterize these materials.

• Functional Materials and Oxides
• Materials for Energy Applications
• Spintronic and Multiferroic Materials
• +7 more

Mike Giles is a Professor of Numerical Analysis at the University of Oxford's Mathematical Institute and serves as Head of the Numerical Analysis Group. He is also a Professorial Fellow at Balliol College and a Fellow of the Royal Society (FRS). His academic career spans computational mathematics, scientific computing, and computational finance. Professor Giles' research primarily focuses on Monte Carlo methods, with particular emphasis on the development and numerical analysis of multilevel Monte Carlo methods over the past 15 years. His work has significant applications in computational finance, uncertainty quantification, and solving stochastic differential equations. He has also made substantial contributions to high-performance computing, especially in the exploitation of many-core GPUs for scientific computing applications. His research bridges theoretical numerical analysis with practical computational implementations. Analysis of his publication record reveals a consistent trajectory of innovation in Monte Carlo methodology, evolving from foundational work on path simulation to sophisticated multilevel techniques that dramatically improve computational efficiency. His research spans multiple disciplines including numerical analysis, computational finance, and high-performance computing, with a clear focus on developing practical algorithms that address real-world computational challenges in science and finance. Fellow of the Royal Society (FRS) Professor Giles actively teaches courses in numerical methods for the MSc in Mathematical and Computational Finance and is a leading educator in GPU programming, organizing an annual intensive course on CUDA Programming on NVIDIA GPUs. He has been instrumental in establishing JADE, Oxford's GPU supercomputer facility, which supports research in machine learning and scientific computing. His leadership extends to the Numerical Analysis Group at Oxford and the Mathematical and Computational Finance Group, where he fosters interdisciplinary research connecting mathematics, finance, and computer science. Through his educational initiatives and research leadership, Giles has significantly influenced both academic research and practical applications of advanced computational methods.

• Monte Carlo Methods
• Multilevel Monte Carlo Methods
• Scientical Computing
• +5 more

Peter Pietzuch is a Professor in the Department of Computing at Imperial College London, where he leads the Large-Scale Data & Systems (LSDS) group. He also serves as the Director of Research and is a Visiting Researcher at Microsoft Research Cambridge. Pietzuch holds a Ph.D. from the University of Cambridge and a B.A. from Girton College. His research spans distributed systems, cloud computing, big data processing, and systems security. Key interests include: Scalable architectures for cloud-native applications Efficient stream processing and machine learning systems Trusted execution environments and secure cloud infrastructure Optimization of serverless computing and distributed databases His recent publications focus on adaptive machine learning frameworks, secure cloud resource management, and high-performance stream processing systems. Trends show strong emphasis on hardware-software co-design, confidential computing, and fault-tolerant architectures. Awards include: Best Paper Award at Middleware'03 He actively advises PhD students and secures grants for projects like Faasm (serverless computing) and Teechain (blockchain security). His LSDS group collaborates with industry partners including Microsoft Research. Pietzuch teaches undergraduate and graduate courses including Scalable Systems for the Cloud and Operating Systems . He co-founded the ACM DEBS conference and serves on steering committees for EuroSys and Middleware.

• Distributed Systems
• Cloud Computing
• Big Data Processing
• +5 more

Dr Bahareh Zaghari (MSc, PhD, CEng, FHEA) is a Lecturer in the Electrical Power Engineering group at the University of Southampton's School of Electronics and Computer Science. Her research focuses on electrified aircraft systems, sensors with machine learning applications, electrical power systems, and nonlinear dynamics modeling. Current projects include acoustic sensing for temperature/flow measurement Co-design of electrical machines for aircraft electrification Research activities span hybrid-electric aircraft design (FutPrint50 Horizon project), fully electric aircraft development (EnabEl Innovate UK), and smart sensing systems for aerospace components. Industrial collaborations include KISTLER, iNetic, PALL Aerospace, Safran, ARUP, Embraer, and BAE Systems. Conference coordinator for IEEE Transportation Electrification Council's Electrified Aircraft committee Chair of IEEE/AIAA Electrified Aircraft Technology Symposium (2023) Panel moderator at AIAA Propulsion and Energy (2021) Her work demonstrates innovative applications of acoustic transducers in harsh environments, with contributions to temperature mapping, fault analysis, and energy harvesting systems. She currently supervises PhD student Shikhar Singh.

• Electrified Aircraft
• Sensors and Machine Learning
• Electrical Power Systems
• +4 more

Professor Wayne Luk is a Professor of Computer Engineering at the Department of Computing, Faculty of Engineering, Imperial College London. He leads the Programming Languages and Systems Section and the Custom Computing Research Group, and directs the EPSRC Centre for Doctoral Training in High-performance Embedded and Distributed Systems and the Centre for Advanced Financial Engineering. He previously served as a Visiting Professor at Stanford University from 2006 to 2009. His research spans FPGA acceleration, quantum computing, deep learning optimization, and algorithm-hardware co-design, with affiliations to the CRUK Convergence Science Centre and the Engineering Secure Software Systems group. His research interests include computational modeling for particle physics, causal discovery in agent-based systems, and high-throughput digital electronics. Notable contributions include FPGA-accelerated algorithms for neural networks, quantum circuit simulation, and Bayesian optimization frameworks. His work emphasizes practical applications of reconfigurable hardware in fields like medical imaging, high-energy physics, and financial systems. Professor Luk is a Fellow of the Royal Academy of Engineering, IEEE, and BCS. His publications focus on advancing hardware-aware machine learning, FPGA-based acceleration techniques, and scalable design methodologies. His research bridges theoretical computer science with applied engineering, addressing challenges in real-time systems, embedded computing, and next-generation computing architectures. His academic leadership includes directing interdisciplinary centers and training programs, fostering collaboration across computing, engineering, and physics. Current projects explore quantum computing tools, causal inference systems, and high-performance graph neural networks for particle physics applications.

• Computer Hardware
• FPGA Acceleration
• Artificial Intelligence
• +8 more

Sebastian Schemm is a Heisenberg Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, a position regarded as equivalent to a non-permanent Associate Professor. He leads research within the Atmosphere-Ocean Dynamics group and previously held an ERC Starting Grant-funded Assistant Professorship (without tenure track) at ETH Zurich. Education and Career Path PhD (2013) and MSc (2010), ETH Zurich, Switzerland Postdoctoral researcher, University of Bergen, Norway (2014–2017) Postdoctoral researcher, Laboratoire de Météorologie Dynamique, ENS Paris (2017–2018) Assistant Professor (ERC Starting Grant), ETH Zurich (2020–2024) Heisenberg Fellow, DAMTP, University of Cambridge (2025–present) Research Focus Schemm’s work centres on atmospheric and climate dynamics, spanning turbulence to planetary scales. Core themes include the physics of extratropical cyclone life cycles, jet-stream and storm-track dynamics, Rossby waves and teleconnection patterns, high-resolution atmospheric modelling, and the integration of machine-learning techniques for parameter estimation, data assimilation, and kilometre-scale global simulations. He also contributes to large-scale initiatives such as ECMWF’s WeatherGenerator. Scientific Awards and Editorial Service DFG Heisenberg Fellowship (2025) ERC Starting Grant (2020–2024) European Meteorological Society Young Researcher Medal (2019) Co-Editor, Weather and Climate Dynamics (EGU) Co-Editor, Quarterly Journal of the Royal Meteorological Society PhD Supervision & Funding He currently supervises PhD students at both Cambridge and ETH Zurich, with funding streams including the Cambridge CREATES Doctoral Training Partnership and Swiss/EU grants. Ongoing students explore reinforcement-learning parameterisations, jet-stream–storm-track relationships, mid-latitude eddy energetics, machine-learning ensemble forecasting, and Bayesian parameter estimation in LES. Active Projects EU Horizon project WeatherGenerator (led by ECMWF) PASC HiRAD-Gen : High-Resolution Atmospheric Downscaling Using Generative Models

• Atmospheric Sciences
• Extratropical Cyclone Dynamics
• Jet Stream Physics
• +7 more

Dr. Michael J Pekris is a Senior Lecturer in Mechanical Engineering Sciences at the University of Surrey, serving as Director of Employability within the School of Engineering. He holds a MEng and DPhil from the University of Oxford (2004), with a thesis on liquid crystal heat transfer in turbine blade cooling. His career includes R&D roles at Rolls-Royce, focusing on advanced seal technology and engine efficiency. He is a Chartered Engineer (CEng), Fellow of the IMechE (2023), and Fellow of the Higher Education Academy (FHEA). His research spans sustainable aviation, fluid dynamics, and heat transfer, with a focus on brush seals, hydrogen-fueled aircraft, and transcritical CO2 systems. He leads the Surrey Aerothermal Test Facility (SATF) and collaborates on projects like the Surrey Hydrogen Aircraft Performance Evaluator (SHAPE). He also serves as IMechE Academic Liaison Officer and Royal Academy of Engineering Visiting Professor Scheme Champion. Research interests include environmental technologies for aero-engine seals, sustainable aviation (electric/hydrogen), fluid dynamics, thermodynamic cycles, and rotating machinery. His work addresses energy efficiency, waste heat recovery, and low-emission propulsion systems. Notable contributions include Rolls-Royce Innovation Award (2013) and patents on seal technology. He actively engages in professional development initiatives and industry-academia partnerships. Publications emphasize seal dynamics, CFD modeling, and thermal management. Recent work explores hydrogen-fueled aircraft viability and CO2-based power systems. Collaborations involve Rolls-Royce, ASME Turbo Expo, and Surrey’s interdisciplinary engineering teams. His academic roles include teaching Structural Vibrations and Engineering Management, and advising the Professional Training Year module.

• Environmental Technologies
• Sustainable Aviation
• Fluid Dynamics
• +7 more

Professor Peter Y. K. Cheung is a Professor of Digital Systems at Imperial College London, holding dual affiliations within the Department of Electrical and Electronic Engineering and the Dyson School of Design Engineering. His work focuses on reconfigurable systems, FPGA architectures, and high-level synthesis tools. He co-founded one of the UK's leading FPGA research groups with Professor Wayne Luk, addressing challenges in variability mitigation, reliability, and application-specific FPGA deployments. His research spans Field-Programmable Gate Arrays (FPGAs) Reconfigurable computing Neural network acceleration Cryptographic protocols Embedded systems He has pioneered techniques such as logic shrinkage for FPGA-based neural networks and developed frameworks like LUTNet for efficient inference. His contributions also include fault-tolerant FPGA designs and methodologies for distributed computation protocols. Key collaborations include work with the Department of Computing on FPGA-based AI acceleration and cybersecurity applications. His recent work explores edge computing, secure decentralized systems, and pandemic modeling using adaptive control strategies. Notable projects include the DSCS protocol for secure distributed computation, acceleration of gravitational wave detection algorithms, and energy-efficient CNN implementations. His research bridges hardware-software co-design with real-world applications in healthcare, finance, and aerospace.

• Electrical and Electronic Engineering
• Computer Hardware
• Computer Software
• +7 more