Find Academic Supervisors

Hrvoje JasakView profile

Professor

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

• Probabilistic Verification
• Quantitative Model Checking
• Formal Methods
• +4 more

Marta Kwiatkowska is a Professor of Computing Systems at the University of Oxford and a Fellow of Trinity College. Her research focuses on probabilistic verification , quantitative model checking , and formal methods for complex systems including autonomous robots, medical devices, and biological systems. She leads the development of the PRISM and PRISM-games probabilistic model checkers. Key research areas: Probabilistic systems, formal verification, autonomous robotics, medical device analysis, systems biology Grants: ERC Advanced Grant VERIWARE, EPSRC Programme Grant Mobile Autonomy Awards: 2024 ETAPS Test-of-Time Tool Award for PRISM Students: Current and former advisees in topics spanning formal methods, robotics, and quantitative verification The PRISM-games extension enables verification of stochastic multi-player games with applications in network protocols, autonomous systems, and game theory. Her work bridges theory, algorithms, and practical implementation, with real-world applications in ubiquitous computing and nanotechnology.

Primoz SkrabaView profile

Professor

• Applied Topology
• Geometry
• Algebra
• +5 more

Dr. Primoz Skraba is a Professor in Applied and Computational Topology at the School of Mathematical Sciences, Queen Mary University of London. As Deputy Head of the Centre for Probability, Statistics and Data Science, he bridges theoretical topology with practical applications in data analysis, machine learning, and optimization. Education : PhD in Electrical Engineering from Stanford University (2009) Prior Roles : Positions at INRIA, France; Jozef Stefan Institute, Slovenia; University of Primorska; University of Nova Gorica His research focuses on applying topological methods to analyze complex data. Key areas include: Stability of persistence diagrams for quantitative control in finite sampling Variants of persistence (zig-zag, robustness, multiparameter) Algorithmic Complexity in computational topology Stochastic Topology for random geometric models (Poisson, Boolean) Recent publications emphasize persistent homology in random geometric complexes, universality theorems, and integrating topological methods into machine learning. He received grants from the Leverhulme Trust, EPSRC, and Alan Turing Institute for projects on topological universality and AI foundations. His advisee Gabryel Mason-Williams explores wireless sensor network applications of homology.

Mike GilesView profile

Professor

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

• Data Science
• Numerical Optimisation
• Medical Imaging
• +3 more

Raphael Hauser is an Associate Professor in Numerical Mathematics at the University of Oxford's Mathematical Institute, Director of Graduate Studies - Teaching, and Tanaka Fellow in Applied Mathematics at Pembroke College. His affiliations include membership in the Data Science, Numerical Analysis, and Mathematical and Computational Finance research groups, as well as a fellowship at the Alan Turing Institute. Education: PhD in Operations Research, Cornell University, Ithaca, USA Dipl. Math. ETH, Swiss Federal Institute of Technology (ETH Zurich), Switzerland Research interests span data science, numerical optimisation, medical imaging, distributed computing, and applied probability/statistics. His work integrates mathematical rigor with practical applications, particularly in optimization algorithms, machine learning theory, and medical imaging technology. Publications focus on optimization theory, stochastic processes, medical imaging systems, and computational finance, with recurring themes in non-convex optimization guarantees, PCA variants, and X-ray tomography innovations. Awards: Oxford University Teaching Award (2007) SIAM Optimization Prize (2005) SIAM Student Paper Prize (2000) Advising includes 15+ DPhil students and 40+ MSc students, with projects in optimization, finance, imaging, and machine learning. Current postdocs and students are affiliated with the Alan Turing Institute and industrial partners like Siemens and Macquarie Group. He leads teams in the Mathematical Institute's research groups and collaborates with the Alan Turing Institute on large-scale data science initiatives.

Peter PietzuchView profile

Professor

• Distributed Systems
• Cloud Computing
• Big Data Processing
• +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.

• Affective Computing
• Artificial Intelligence
• Machine Learning
• +3 more

Dr A. I. Shihab is a Senior Lecturer at Kingston University's Faculty of Engineering, Computing and the Environment, Department of Networks and Digital Media. He teaches programming languages (C++/Java), data structures, web development, and AI/machine learning. His research focuses on affective computing and machine learning applications including: Acoustic event detection in sports environments Audio signal analysis for tennis match modeling Multi-camera visual surveillance systems Medical imaging analysis using fuzzy clustering techniques Publications demonstrate expertise in combining audio/video modalities for sports analytics (tennis rallies, court-shots) and developing Markov models for sound event sequence analysis. Contact: a.shihab@kingston.ac.uk

• Programming Language Theory
• Semantics of Programming Languages
• Domain Theory
• +7 more

Gordon Plotkin is a Professor at the School of Informatics, University of Edinburgh, where he is affiliated with the Laboratory for Foundations of Computer Science (LFCS). His research lies at the intersection of theoretical computer science and programming language semantics, with a profound influence on the formal understanding of computation. His research interests include Programming Language Theory, Semantics of Programming Languages, Domain Theory, Operational Semantics, Lambda Calculus, Type Theory, Concurrency Theory, and Algebraic Effects. His seminal work on structural operational semantics and domain theory has laid the foundation for modern semantics of programming languages. His publications span over five decades, showing a sustained and evolving research trajectory from foundational work in lambda calculus and domain theory to recent contributions in algebraic effects, probabilistic computation, and biochemical systems modeling. The articles demonstrate a consistent focus on formal methods, mathematical rigor, and the algebraic structure of computational effects. He has collaborated with leading researchers including Martín Abadi, John Power, Glynn Winskel, and John Reynolds. His work continues to influence both theoretical and practical developments in programming languages and systems. Gordon Plotkin has made foundational contributions to computer science, particularly through his development of structural operational semantics and domain-theoretic models of computation. He has advised numerous researchers and supervised many influential PhD theses, though specific student names are not listed in the provided text. His work has been supported by long-standing affiliations with the Laboratory for Foundations of Computer Science and the University of Edinburgh, and he has contributed to major collaborative projects in programming language design and verification. He is associated with several research groups and labs, most notably the Laboratory for Foundations of Computer Science (LFCS), which serves as a hub for theoretical research in programming languages, semantics, and logic at the University of Edinburgh.

Leslie Ann GoldbergView profile

Professor

• Algorithms
• Complexity Theory
• Randomised Algorithms
• +5 more

Leslie Ann Goldberg is a Senior Research Fellow at St Edmund Hall and Professor of Computer Science at the University of Oxford. She currently serves as Head of the Department of Computer Science (on sabbatical 2025-26) and focuses on foundational problems in Algorithms and Complexity Theory , particularly randomised algorithms for network communication, machine learning, and statistical physics models. Her research includes solving Aldous' 1987 conjecture on backoff protocol instability (with John Lapinskas), developing rigorous mathematical analysis frameworks for algorithmic efficiency, and advancing approximate counting techniques via Markov Chain Monte Carlo methods (with Andreas Galanis and collaborators). Key projects involve graph homomorphisms , Moran process dynamics , and #BIS complexity class analysis. Recent publications (2023-2024) span topics like Sybil defense mechanisms, low-temperature sampling on random graphs, and parameterised subgraph counting modulo 2. Her work demonstrates cross-disciplinary impact in computational biology, statistical physics, and database theory. Scientific Awards include Best Paper Prizes at ICALP 2016, ICALP 2010, and IPEC 2017. She supervises PhD student Paulina Smolarova and collaborates extensively with researchers in Oxford and beyond.

Jens GrothView profile

Academic

• Cryptography
• Cryptographic Protocols
• Zero-Knowledge Proofs
• +5 more

Jens Groth is an Honorary Professor at the Department of Computer Science, University College London (UCL), and serves as Chief Scientist at Nexus. His primary research focuses on cryptography, with an emphasis on cryptographic protocols, zero-knowledge proofs, and privacy-preserving technologies. Groth has contributed significantly to advancements in digital signatures, homomorphic encryption, and secure multi-party computation. He holds a leadership role as Program Chair for the 15th IMA International Conference on Cryptography and Coding (2015) and has been a key figure in shaping modern cryptographic standards. His work often bridges theoretical foundations with practical implementations, emphasizing efficiency and security. Groth's research interests include but are not limited to: cryptographic protocol design, lattice-based cryptography, and the application of zero-knowledge proofs in real-world systems such as blockchain and voting systems. His publications span venues like CRYPTO, EUROCRYPT, and ASIACRYPT, reflecting his impact on the field. Notably, he advocates for open access to research and has contributed to strategies ensuring conferences adopt de facto open-access policies. His current focus at Nexus centers on verifiable computation and distributed cryptographic systems.

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

• Bayesian Statistics
• Computational Statistics
• Change Point Detection
• +5 more

Professor Paul Fearnhead is a leading academic in Statistics at Lancaster University 's School of Mathematical Sciences . His research focuses on Bayesian and Computational Statistics , with applications in Anomaly Detection , Continuous-time Markov Processes , and Changepoint Analysis . Department: Mathematics and Statistics Academic Rank: Professor Email: p.fearnhead@lancaster.ac.uk His work bridges theoretical statistics and computational efficiency, notably through pruning techniques for change detection and novel Monte Carlo methods. Current projects include AI Hub initiatives, probabilistic AI foundations, and real-time anomaly detection in streaming data. Research outputs span Bayesian Analysis , Time Series Modeling , and Scalable Statistical Algorithms , with applications in fields like astronomy and epidemiology. Recent publications emphasize simulation-based composite likelihoods and efficient distributed changepoint detection. Scientific contributions include leadership roles in the STOR-i Centre for Doctoral Training and Data Science Institute (DSI) projects such as CoSInES and Statscale. He supervises PhD students including Dylan Bahia, Yuntang Fan, and Ziyang Yang.

Michael U. GutmannView profile

Senior Lecturer

• Machine Learning
• Bayesian Inference
• Likelihood-Free Inference
• +9 more

Michael U. Gutmann is a Senior Lecturer in Machine Learning at the School of Informatics, University of Edinburgh, and a member of the Institute for Adaptive and Neural Computation. His research lies at the intersection of machine learning, statistics, and scientific applications, with a focus on developing inference methods for complex and implicit models. Education: PhD in Computational Neuroscience, University of Tokyo MSc in Engineering and Applied Mathematics, Swiss Federal Institute of Technology (ETH) Zurich MSc, Ecole Centrale Paris His primary research interests include Bayesian inference, likelihood-free inference, optimal experimental design, unsupervised learning, and applications in computational biology and neuroscience. He is best known for introducing Noise-Contrastive Estimation (NCE), a foundational technique for training unnormalized statistical models. His recent work spans variational inference, density ratio estimation, flow models for missing data, and AI-driven experimental design in behavioral and biological sciences. His publications, including in NeurIPS , ICML , JMLR , and eLife , demonstrate a strong emphasis on methodological innovation for scientific discovery. He has contributed to open-source tools such as ELFI (Engine for Likelihood-Free Inference) and developed practical implementations of robust inference algorithms. Scientific Awards: No specific awards listed in the provided texts. Michael Gutmann actively supervises students and collaborates with leading researchers in machine learning and computational biology. He has secured research funding from EPSRC and BBSRC for projects in generative modeling and infectious disease epidemiology. He teaches advanced courses such as Probabilistic Modelling and Reasoning and Data Mining, reflecting his deep engagement with both theoretical and applied aspects of machine learning. Labs and Research Groups: Institute for Adaptive and Neural Computation (ANC), University of Edinburgh Former affiliations with Department of Mathematics and Statistics and Department of Computer Science at the University of Helsinki and Aalto University

• Stochastic processes
• Biodemography
• Survival analysis
• +4 more

David Steinsaltz is an Associate Professor of Statistics at the University of Oxford, affiliated with Worcester College. His research focuses on stochastic processes, biodemography, survival analysis, and Bayesian methods, with applications to aging, mortality, and population dynamics. He holds a PhD in probability theory from Harvard University, followed by postdoctoral work at UC Berkeley. His work bridges theoretical probability and applied statistics, addressing questions in demography, ecology, and epidemiology. Education: PhD in Mathematics (Probability Theory), Harvard University (1996); Postdoctoral Research, UC Berkeley (Departments of Demography and Statistics). Research interests include stochastic flows, Markov processes, and statistical methods for longitudinal data. He contributes to interdisciplinary projects, such as earthquake impact modeling and vaccine efficacy analysis. His collaborations span fields like biostatistics, ecology, and machine learning. He advises students on topics including survival analysis and demographic modeling.

Andrew RiceView profile

Professor

• Programming Languages
• Machine Learning
• Artificial Intelligence
• +4 more

Andrew Rice is a Professor of Computer Science at the University of Cambridge's Department of Computer Science and Technology, and holds the Hassabis Fellowship in Computer Science. He is also the Director of Studies in Computer Science at Queens' College. His research focuses on programming languages, software engineering, and machine learning applications in software development. He leads projects like Isaac Computer Science and ALTA (Automated Language Teaching and Assessment), advancing adaptive learning technologies. His work includes static analysis tools such as Error Prone at Google, energy efficiency studies in computing infrastructure, and contributions to the Computing for the Future of the Planet initiative. His teaching emphasizes practical skill development through flipped classrooms and video lectures, earning him the 2014 Pilkington Prize for teaching excellence. He has held visiting roles at Google and collaborated on energy consumption research for mobile devices and data centers. His research spans systems, networking, and natural language processing, with a strong focus on applying computational methods to real-world challenges. Key Projects: Isaac Physics/Computer Science, ALTA, Error Prone Static Analysis Research Themes: Programming Languages, Machine Learning, Energy Efficiency Awards: Pilkington Prize (2014)