Find Academic Supervisors

• Machine Learning
• Deep Learning
• Computer Systems
• +3 more

Alexandros Koliousis is a Professor of Computer Science at Northeastern University London, affiliated with the CoMENS Faculty and the Khoury College of Computer Sciences. He is a Turing Fellow at the Alan Turing Institute and a Senior Faculty Research Scientist at the Institute for Experiential AI. PhD in Computing Science (University of Glasgow, 2010) MSc in Advanced Computing Science (University of Glasgow, 2005) Undergraduate in Computer Engineering (Alexander Technological Educational Institute of Thessaloniki, 2003) His research focuses on the intersection of computer systems and machine learning, emphasizing responsible AI systems, scalable data-parallel processing, and hardware accelerator optimization. Prior work includes GPU-based deep learning (Crossbow), data streaming (Saber), and wireless sensor networks. Selected Trends: His publications highlight advancements in multi-GPU deep learning systems, hybrid stream processing for heterogeneous architectures, and hardware-software co-design for efficiency. Scientific Awards Turing Fellow, Alan Turing Institute Koliousis supervises final-year and Master’s projects and serves as Director of Graduate Studies in Computer and Data Science. Contact: alexandros.koliousis@nulondon.ac.uk

Christos-Efthymios KotselidisView profile

Associate Professor

• Computer Architecture
• Parallel and Distributed Computing
• High-Performance Computing
• +4 more

Christos-Efthymios Kotselidis is an Associate Professor in the Department of Computer Science at the University of Manchester. His work focuses on runtime systems, heterogeneous computing, and high-performance computing. He leads research in optimizing Java programs using frameworks like TornadoVM and explores GPU acceleration for cryptography (FHE). His contributions include advancements in vectorization for RISC-V architectures and memory analysis of Java benchmarks. Education: PhD in Computer Science (2011) from the University of Manchester, supervised by Prof. Steve Furber and Dr. Chris Kirkham, with a thesis on Distributed Software Transactional Memory. Research Interests: Runtime environments, parallel programming models, compiler optimization, and hardware-software co-design. His work aligns with UN SDG 9 (Industry, Innovation, and Infrastructure) through digital futures research. Notable Achievements: Recipient of the Best Paper Award (2017) Principal Investigator in the Centre for Digital Trust and Society (2021–present) Over 49 peer-reviewed publications, including books and conference contributions on topics like FHE acceleration and runtime systems Lab/Team Affiliations: Member of the Advanced Processor Technology group and collaborates on the Digital Futures research beacon at the University of Manchester.

• Theorem Proving
• Runtime Verification
• Automated Reasoning
• +3 more

Giles Reger is a Lecturer in the Formal Methods Group within the School of Computer Science at the University of Manchester. His primary roles include research and teaching in theorem proving, runtime verification, and formal methods. He has been actively involved in projects such as the Centre for Digital Trust and Society and the SCorCH project focusing on secure code verification. Educational background: Giles holds a BA in Computer Science from the University of Cambridge (2009), an MSc in Advanced Computer Science from the University of Manchester (2010) with the Highest Achiever of the Year Award, and a PhD from the University of Manchester (2014) titled Automata based monitoring and mining of execution traces . Research interests focus on two core areas: theorem proving (quantifier reasoning, first-order theories, collaborative proof search, and integration of SAT/SMT solvers) and runtime verification (temporal specifications, monitoring algorithms, violation explanation, and benchmarking). His work emphasizes practical applications in tool development and real-software analysis. Notable awards include the ACM SIGSOFT Distinguished Paper Award (2024) for his paper on LLM-generated invariants. His research contributions span over 50 publications, with active involvement in funded projects like SCorCH (Secure Code for Capability Hardware) and the Centre for Digital Trust and Society. Advising and grants: Reger welcomes PhD/MSc students in theorem proving, program verification, and related fields. He has supervised multiple research students and leads collaborative projects involving international researchers. His lab, the Formal Methods Group, develops tools like the Vampire theorem prover and contributes to runtime verification frameworks.

• Software Engineering
• Cloud Computing
• Web Services
• +3 more

Professor Hong Zhu is a Professor of Computer Science at the School of Engineering, Computing and Mathematics, Oxford Brookes University. He chairs the Cloud Computing and Cybersecurity research group, established in 2020, and teaches courses on cloud computing, software engineering, and distributed systems. His research focuses on software engineering methodologies for cloud-native applications, formal methods for service-oriented systems, and automated testing techniques. Key projects include the development of the CAOPLE programming language and CIDE integrated development environment for cloud software. He has organized major conferences like IEEE AITest, SOSE, and EDGE, and contributes to academic leadership roles. Research interests include software development methodologies, cloud-native software languages, formal specification (e.g., SOFIA), and testing frameworks (e.g., Morphy). He actively supervises PhD students and leads initiatives in edge computing, AI testing, and cybersecurity patterns. His work bridges theory and practice, emphasizing automation, quality assurance, and innovative software tools.

Marios KogiasView profile

Assistant Professor

• Operating Systems
• Networking
• Distributed Systems
• +4 more

Marios Kogias is an Assistant Professor in the Department of Computing at Imperial College London, conducting research at the intersection of operating systems, networking, and distributed systems with a focus on tail-tolerance, fault-tolerance, and confidentiality using emerging datacenter hardware. He holds a PhD in Computer Science from EPFL (2020) supervised by Ed Bugnion, where his thesis won the Dennis M. Ritchie Award and received an Honourable Mention for the Eurosys Roger Needham PhD Award. His undergraduate degree in Electrical and Computer Engineering is from NTUA. His research spans operating systems, networking, distributed systems, cloud computing, and confidential computing, with significant contributions to microsecond-scale computing, serverless architectures, and eBPF applications. Current work emphasizes tail-latency optimization in datacenter environments and confidential computing security. Recent publications reveal strong trends in microsecond-scale systems, confidential computing, and serverless workflows, demonstrating expertise in leveraging hardware innovations for performance and security guarantees across distributed environments. His scientific awards include: Dennis M. Ritchie Award (2021) Honourable Mention for the Eurosys Roger Needham PhD Award (2021) IBM Fellowship Microsoft Swiss JRC Fellowship Distinguished Artifact Award (ASPLOS 2021) Best Student Paper Award (Eurosys 2020) Dr. Kogias actively mentors PhD students including Konstantinos Prasopoulos (co-advised with Ed Bugnion), Scofield Liu, Farzad Mohammadi, and Adel Sefiane. He teaches Networked Systems, Computer Networks and Distributed Systems, and Object-Oriented Design at Imperial, supported by fellowships from IBM and Microsoft during his doctoral research. His collaborative work spans EPFL, Microsoft Research Cambridge, and Imperial College London, focusing on systems architecture for next-generation datacenter applications.

Nikos NtarmosView profile

Senior Lecturer

• Distributed Systems
• Big Data Management
• NoSQL Databases
• +3 more

Nikos Ntarmos is a Senior Lecturer at the University of Glasgow's School of Computing Science, with affiliations to the IDEAS research lab. He holds a PhD in Computing Engineering & Informatics from the University of Patras and specializes in distributed systems and large-scale data management. Research focuses on: Distributed data storage and indexing for NoSQL systems Efficient query processing for graph databases Scalable algorithms for big data analytics Recommendation systems and collaborative filtering Publications demonstrate evolving interests from distributed hash tables to contemporary graph processing and recommendation systems. Recent work increasingly addresses practical applications in education technology alongside core database research.

• High Performance Computing
• Heterogeneous Accelerators
• FPGAs
• +7 more

Dr. Charles James Gillan is a Senior Lecturer at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, part of the Faculty of Engineering and Physical Sciences. His research focuses on high-performance computing (HPC), heterogeneous accelerators (FPGAs and GPUs), and their applications in diverse fields such as clinical physiology, electromagnetic fields, and image analysis. He collaborates extensively with the QUB Medical School on data analytics and machine learning for ICU patient monitoring. Gillan has led multiple projects, including an InterTrade Ireland-recognized Fusion project with CreVinn Ltd, which transferred FPGA programming knowledge to industry. His work spans academic and industrial partnerships, emphasizing innovation in computing systems and real-world problem-solving. His research interests include KTP projects, EPSRC funding, and H2020 initiatives. He has contributed to high-impact projects like the HPC-NI center and handheld olfactory detection systems. Awards include an InterTrade Ireland award for his Fusion project. Gillan has also engaged in outreach, training graduates in OpenCL programming and fostering industry-academia collaboration. Publications highlight advancements in neural networks for blood pressure prediction, exascale computing algorithms, and AI-driven healthcare solutions. His work bridges theoretical computing and practical applications, with a focus on edge computing architectures and transparency in food systems.

Deniz GunduzView profile

Professor

• Wireless Communications
• Information Theory
• Machine Learning
• +3 more

Deniz Gündüz is a Professor of Information Processing at Imperial College London's Electrical and Electronic Engineering Department, leading the Information Processing and Communications Lab. He also serves as Deputy Head of the Intelligent Systems and Networks Group and holds a part-time faculty position at the University of Modena and Reggio Emilia. His research focuses on wireless communications, information theory, machine learning, and privacy, with significant contributions to semantic communication systems and AI-native networks. He has held visiting roles at Princeton University and the University of Padova. Dr. Gündüz is an Area Editor for IEEE Transactions on Communications and IEEE JSAC, and a former Distinguished Lecturer of the IEEE Information Theory Society. His awards include the IEEE Communication Society Early Achievement Award (2017), ERC Starting Grant (2015), and multiple best paper recognitions. He has organized major conferences and workshops, including the first MLCOM workshops on machine learning for communications. Education: B.S. (2002) from METU, Turkey; M.S. and Ph.D. (2004, 2007) from NYU Polytechnic School of Engineering. Prior roles include Research Associate at CTTC Barcelona, Consulting Assistant Professor at Stanford, and postdoctoral positions at Princeton. His work spans theoretical foundations and practical implementations, emphasizing interdisciplinary solutions for next-generation networks. Research Interests: Semantic communications, distributed learning, 6G systems, privacy-preserving techniques, and AI integration in communication networks. His recent work explores neural compression for cloud RAN, over-the-air computation, and federated learning optimizations. Awards and Roles: IEEE JSAC Series Editor (Machine Learning in Networks), IEEE Transactions on Wireless Communications Editor, and organizer of major conferences. His team's achievements include breakthroughs in joint source-channel coding and adversarial jamming defenses.

Mahdi Boloursaz MashhadiView profile

Researcher

• Signal Processing
• Wireless Communications
• Machine Learning
• +3 more

Mahdi Boloursaz Mashhadi is a researcher at Imperial College London's Department of Electrical and Electronic Engineering, affiliated with the Information Processing and Communications Lab. He holds a Ph.D. in Electrical Engineering from Sharif University of Technology (2018), with prior research roles at the University of Central Florida and Queen's University. His expertise spans signal processing, wireless communications, machine learning applications in communication systems, and biomedical signal processing. Dr. Mashhadi's research focuses on massive MIMO channel state acquisition , deep learning-driven pilot design , and semantic communication frameworks . His recent work explores token-domain multiple access, generative AI integration in communication systems, and federated learning optimizations. He has contributed to foundational studies in sparse signal reconstruction (e.g., iterative adaptive thresholding methods) and wearable health monitoring via PPG signals. Key achievements : Best Paper Award at EWDTS 2012, multiple grants (IEEE, national/regional), and patents (e.g., US Patent 9729160). Current projects include semantic-aware power allocation in generative communications and latency optimizations in distributed deep learning frameworks. Labs/Teams : Member of the Intelligent Systems and Networks (ISN) group at Imperial, collaborating on AI-driven communication systems and edge computing solutions. His work bridges theoretical signal processing with practical implementations in 5G/6G networks, biomedical devices, and distributed machine learning ecosystems.

• Computational Neuroscience
• Artificial Intelligence
• Cognitive Science
• +3 more

Jascha Achterberg serves as a Career Development Research Fellow at the University of Oxford, holding dual affiliations with the Department of Physiology, Anatomy and Genetics (DPAG) and St John's College. He actively contributes to the Costa Group research collective, focusing on the critical intersection of neuroscience and artificial intelligence to decode the principles of intelligence. Academic Background: PhD in Cognitive Neuroscience from the University of Cambridge (MRC Cognition and Brain Sciences Unit), supervised by John Duncan and Matthew Botvinick with collaborative projects at Google DeepMind and Intel. Research Vision: Dr. Achterberg pioneers investigations into how the brain's system-level architecture enables flexible cognition, with particular emphasis on specialized submodules, circuit patterns, and communication topologies. His work bridges biological intelligence and artificial systems through brain-inspired computing, targeting scalable distributed architectures that replicate cognitive flexibility. This transdisciplinary approach integrates computational neuroscience, AI engineering, and cognitive theory to establish general principles applicable to both neuroscientific modeling and next-generation computing systems. Publication Trajectory: Recent publications (2023-2025) demonstrate a cohesive research program advancing brain-inspired AI through three converging fronts: (1) modeling neural dynamics for complex problem-solving (frontal lobe networks), (2) developing heterogeneous expert architectures that mimic biological processing pathways, and (3) establishing spatially embedded neural networks that reconcile structural and functional neuroscience data. These works consistently reveal how biological constraints can drive efficiency in artificial systems, particularly in domain-general cognition and scalable learning. Research Collective: As a core member of the Costa Group at Oxford, Achterberg operates within a dynamic ecosystem focused on systems neuroscience and computational modeling of brain function, leveraging institutional resources from DPAG and cross-sector collaborations with industry partners like Intel and Google DeepMind.

Owen OsborneView profile

Lecturer

• Evolutionary Biology
• Ecology
• Speciation
• +12 more

Dr. Owen Osborne is a Lecturer in Zoology at the School of Environmental and Natural Sciences, Bangor University. He is an active researcher in evolutionary biology and ecology, focusing on speciation, adaptation, hybridization, and their impacts on biodiversity. His work spans diverse biological systems including plants, fungi, amphibians, fish, birds, insects, and host-associated microbiomes. His research interests center on understanding how rapid evolutionary change affects ecological interactions, particularly host-microbiome dynamics, and how these interconnected processes generate and maintain biodiversity. He employs a multidisciplinary approach combining evolutionary genomics, experimental ecology, metagenomics, and spatial ecology. Notably, he develops computational tools, including software for hypothesis testing in spatial ecology and bioinformatics, such as the R package fauxcurrence for generating null species distributions. The trends in his recent publications (2021–2025) show a consistent focus on parallel adaptation, speciation mechanisms, phylogenetic and biogeographic modeling, host-microbiome coevolution, and the development of computational methods. His work frequently appears in top-tier journals such as Nature Ecology and Evolution , PNAS , Molecular Biology and Evolution , and The ISME Journal , often in collaboration with leading researchers in the field. Dr. Osborne has no listed scientific awards in the provided texts, but his extensive publication record and software development indicate significant scholarly contributions. He advises students and likely leads or contributes to research grants, though specific names and funding details are not mentioned. His GitHub profile (ogosborne) hosts several repositories related to his research, such as fauxcurrence , Silene.uniflora.genome.project , and fasta_alignment_filters , indicating active involvement in open science and computational research. Dr. Osborne is part of research teams investigating evolutionary processes in natural populations, with a particular emphasis on integrating genomic data with ecological and spatial analyses. His lab or research group likely involves interdisciplinary collaboration and the use of bioinformatics pipelines for large-scale data analysis.

Tobias GrosserView profile

Associate Professor

• Compilers
• Programming Languages
• Performance Programming
• +7 more

Tobias Grosser is an Associate Professor in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on rethinking performance programming by bridging the gap between developers and compilers. He holds a PhD from École Normale Supérieure Paris and has held positions including Reader at the University of Edinburgh and Ambizione Fellow at ETH Zurich. His research interests span compilers, programming language design, static/dynamic analysis, and the integration of machine learning into compiler development. He emphasizes making compilation more modular, automatic, and trustworthy, with applications in quantum computing, climate science, and open-source hardware. Key projects include xDSL (a Python-native compiler framework), LoopOpt, and the Open Earth Compiler for climate simulations. Recent publications highlight advancements in multi-level intermediate representations (IR), formal verification in MLIR, and performance optimization for GPUs and FPGAs. His work often addresses barriers between programmers and compilers, aiming for intuitive collaboration between developers and automated systems. Tobias mentors a dynamic team of PhD students, postdocs, and researchers, including notable contributors like Siddharth Bhat, Arjun Pitchanathan, and Mathieu Fehr. His lab focuses on compiler toolchains for domain-specific hardware accelerators, quantum computing ecosystems, and verified compilation techniques.

Vladimir StankovicView profile

Senior Lecturer

• Dependability and Security Assessment
• Software Fault Tolerance
• Database Replication Protocols
• +4 more

Dr. Vladimir Stankovic is a Senior Lecturer in the Department of Computer Science at City, University of London, where he has been employed since 2003. He progressed through academic ranks from Research Assistant to his current position as Senior Lecturer (since August 2019). From August 2019 to July 2024, he served as Deputy Head of Department of Computer Science, including a transitional period as Head in autumn 2024. He has held several leadership roles including NSS and Employability Director (2019-2023) and Undergraduate Programme Director (2017-2019). His educational background includes a PhD in Software Engineering (2008) and a BEng in Computing (2003), both from City, University of London. He is a member of professional organizations including IEEE, ACM, and is a Fellow of the Higher Education Academy. Dr. Stankovic's research focuses on dependability and security assessment of computer-based systems, software fault tolerance, diverse redundancy, distributed systems algorithms, and database replication protocols. His work contributes to the department's research areas in Software and Systems Engineering. He has been particularly active in designing and evaluating diverse database replication protocols and security assessment methodologies. His recent publications (2011-2025) show a consistent focus on database replication, security assessment, and distributed systems. The research trend demonstrates increasing integration of machine learning techniques with traditional dependability approaches, particularly in security applications. His work spans theoretical modeling, empirical studies, and practical implementations with growing emphasis on real-world applications in critical infrastructure protection. Patent EP2470994 (EU) for Improvements Relating to Database Replication Protocols Patent 8793216 (USA) for Database replication Dr. Stankovic has supervised multiple PhD students including Saidu Sokoto (focused on decentralized networks), Kaleem Peeroo (focused on Data Distribution Service performance), and Konstantin Pozdniakov (focused on security penetration testing). He has served as Principal Investigator for the EPSRC-funded DIDERO-PC project and co-investigator in several EU-funded projects including AQUAS, SESAMO, and AFTER. His enterprise activities include co-founding ResilSoft Ltd (2012-2021), a university spin-off company focused on dependability solutions. He has been actively involved in the academic community as Program Committee member for conferences including CRITIS (2020-present), IEEE Cyber Security and Resilience (2025), and ISSRE (2015), and as reviewer for premier journals including IEEE Transactions on Dependable and Secure Computing and IEEE Security & Privacy.

Qicheng YuView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Cyber Security
• +1 more

Dr. Qicheng Yu is an Associate Professor (Enterprise) at the School of Computing and Digital Media, London Metropolitan University. As Subject Standard Board Chair of Computer Science and Applied Computing, he leads the Data Analytics MSc and Information Technology (Distance Learning) MSc programs. His research spans AI, cyber security, and big data, with a focus on practical applications in education, finance, and urban development. His research interests include Artificial Intelligence , Machine Learning , and Cyber Security , with projects ranging from fraud detection to student performance prediction. Recent work highlights multimodal data analysis for fake review detection and data dashboard development for business intelligence. Fellow of the Higher Education Academy (FHEA) Member of Data Science Association and Cyber Security Systems Research Centre Recipient of Innovate UK grants (2022: £131,296; 2019: £19,200; 2015: £115,000) He supervises PhD students in data science and AI , while contributing to cross-disciplinary initiatives like the Empowering London Lab and SME cyber security clinics.

• Computer Software
• Computation Theory
• Pure Mathematics
• +3 more

Martin Berger is an Associate Professor in Foundations of Computation at the University of Sussex's School of Engineering and Informatics. His research bridges theoretical computer science and practical systems engineering, with focus areas including: Formal methods and program verification Programming language design and semantics Hardware security vulnerabilities and mitigations GPU-accelerated computation and program synthesis Automated reasoning and logic-based systems With 51 publications, his recent work demonstrates strong emphasis on: Developing high-performance simulation tools (Pydrofoil) GPU-based acceleration of formal methods Security analysis of programming language ecosystems Automated inference systems for regular expressions He maintains active research collaborations across Europe and welcomes inquiries via his institutional email.