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Zi-Qiang ZhuView profile

Professor

• Electrical Engineering
• Power Electronics
• Electric Machines and Drives
• +5 more

Professor Zi-Qiang Zhu is a distinguished academic and researcher in electrical engineering at the University of Sheffield, holding a Personal Chair and leading the Electrical Machines and Drives Research Group. He earned his BEng and MSc from Zhejiang University and his PhD from the University of Sheffield. His career spans over 35 years, focusing on high-efficiency permanent magnet machines and drives for electric vehicles, renewable energy, and industrial applications. He has secured over £30M in research funding and founded key research centers like the Sheffield Siemens Gamesa Renewable Energy Research Centre. Research interests include novel machine designs (Halbach, switched-flux, magnetically geared), control strategies (PWM, sensorless), and thermal management. His work has resulted in over 100 patents and 1000+ papers, including 400+ IEEE Transactions contributions. Notable awards include the 2021 IEEE Nikola Tesla Award and multiple best paper prizes. He has chaired major conferences and delivered over 26 keynote speeches globally. Education : BEng (Zhejiang U, 1982), MSc (Zhejiang U, 1984), PhD (Sheffield U, 1991) Affiliations : Royal Academy of Engineering/Siemens Chair, Founding Director of CRRC Electric Drives Tech Centre His research spans from fundamental electromechanical theory to industrial partnerships with Rolls Royce, Siemens, and others. Key areas of impact include wind power generators, EV propulsion, and energy-efficient domestic appliances.

• Quantum Entanglement
• Photonics
• Quantum Information Science
• +3 more

Natalia Herrera Valencia serves as a Research Associate at the Institute of Photonics and Quantum Sciences within Heriot-Watt University's School of Engineering & Physical Sciences. Her work centers on experimental quantum photonics, specifically manipulating high-dimensional quantum states for advanced information processing. Her research spans quantum entanglement engineering , photonic quantum computing , and noise-resilient quantum communication . Key specialties include structured light control, optical fiber networks for entanglement distribution, and tomographic reconstruction of complex quantum states. She investigates fundamental limits of quantum systems under losses and decoherence. Recent publications (2024-2025) reveal a concentrated effort in scalable quantum photonic architectures , with emphasis on multiplexed networks, inverse-designed optical circuits, and parallelized Bell tests. These works collectively advance high-dimensional quantum information protocols using integrated photonics platforms. Dr. Herrera Valencia actively collaborates with the quantum photonics group at Heriot-Watt, particularly within Mehul Malik's research team. Her experimental work leverages the university's state-of-the-art quantum optics facilities for generating and characterizing entangled photon states in complex media.

Diptesh KanojiaView profile

Senior Lecturer

• Natural Language Processing
• Machine Learning
• Low-resource Languages
• +4 more

Affiliations Senior Lecturer in People-Centred AI at the Surrey Institute for People-Centred Artificial Intelligence (PAI) and School of Computer Science and Electronic Engineering, University of Surrey. Visiting Faculty at IIIT Lucknow. Former Postdoctoral Research Fellow at the Centre for Translation Studies (CTS), University of Surrey. Education PhD, IIT Bombay & Monash University (2021) - Dissertation on distributional similarity for cognate detection and computational phylogenetics. BTech in CSE, Uttar Pradesh Technical University (2013). Research Interests Focused on NLP and ML, particularly in low-resource languages, machine translation quality estimation, cognitive NLP, and multimodal information processing. Leads research in Human-Machine Interaction at PAI and the NLP subgroup in the Nature Inspired Computing and Engineering (NICE) group. Publications & Awards Over 30 publications in top conferences (ACL, EMNLP, EACL) including Best Paper Honourable Mention at EACL 2021. Awards include CISCO Fellowship (2016-2020) and Teaching Assistant of the Semester (2017, 2019). Teaching Leads NLP module for undergrad and postgrad students at Surrey. Co-teaches NLP at IIIT Lucknow. Integrates cutting-edge topics like multimodal processing into coursework.

Pedro Porto Buarque de GusmãoView profile

Lecturer

• Computer Vision
• Navigation under adverse conditions
• Sensor Fusion
• +4 more

Pedro Porto Buarque de Gusmão is a Lecturer in Computer Science at the University of Surrey, affiliated with the Computer Science Research Centre within the School of Computer Science and Electronic Engineering. His research focuses on Computer Vision, Navigation under adverse conditions, Sensor Fusion, and Distributed Machine Learning. He leads projects in federated learning, autonomous systems, and robotic navigation, contributing to frameworks like Flower, a widely used federated learning library. His work emphasizes advancing federated learning techniques for privacy-preserving distributed systems, with notable achievements including winning the First Prize in the NIST Privacy-Enhancing Technologies Prize Challenge (2023). His publications span conferences like ICCV, USENIX Security, and IEEE Transactions, addressing topics such as federated learning optimization, 3D lane detection, and thermal-inertial SLAM. Key contributions include developing methods like L-DAWA for federated visual learning and FedVal for federated model validation. His research also integrates multimodal sensor data for autonomous navigation systems, leveraging mmWave radar and thermal-inertial fusion. He actively contributes to open-source projects and collaborates with industry on privacy-aware machine learning solutions.

• Big Data & AI
• Data Envelopment Analysis
• Data Mining
• +3 more

Ali Emrouznejad is a Professor and Chair in Business Analytics at Surrey Business School, University of Surrey. He leads the Centre for Business Analytics in Practice and directs the "Sustainability Analytics, Metrics, and NetZero" program. His research focuses on performance measurement, efficiency analysis, AI, and big data. He holds a PhD in Operational Research from Warwick Business School and has served as head of the Business Analytics Group at Aston University. His work includes pioneering contributions to Data Envelopment Analysis (DEA), with over 250 journal articles and multiple authored books. Emrouznejad has led major research projects funded by organizations including the UK Department for Education, WHO-Africa, and the Royal Academy of Engineering. He serves as editor of Annals of Operations Research and holds editorial roles in over 10 journals. His awards include recognition as a top 2% global scientist and fellowship from FIMA, Institute for Sustainability, and PAI. His recent work addresses renewable energy transitions, additive manufacturing cybersecurity, and NetZero strategies for OECD countries. Key Projects: Cost efficiency analysis in higher education (DfES-funded) Hospital efficiency in African healthcare systems (WHO-Africa) Manufacturing efficiency with CO2 emissions (Royal Academy) Sugarcane grower efficiency in Africa (British Council) Milk yield optimization in dairy supply chains (KTP-funded) Awards & Recognition: World Top 100 Business Scientists 2024 Stanford's Top 2% Global Scientists Fellow of FIMA, Institute for Sustainability, and PAI Labs/Teams: Leads the Surrey Business Analytics Research Group and coordinates international conferences like DEA45. Active in editorial boards of Springer's Business Analytics in Practice series and multiple journals.

Chuan FohView profile

Senior Lecturer

• Computer Networks
• Wireless Communication Protocols
• 5G/6G Networks
• +6 more

Dr Chuan Foh is a Senior Lecturer at the University of Surrey, affiliated with the Institute for Communication Systems within the School of Computer Science and Electronic Engineering. His research focuses on advanced communication protocols and performance analysis across diverse networks, including 5G/6G, vehicular networks, and satellite systems. He explores topics such as network security, federated learning, and reconfigurable intelligent surfaces to enhance communication efficiency and reliability. Dr Foh has contributed to over 50 peer-reviewed publications since 2015, with recent work addressing Open RAN vulnerabilities, NOMA-enhanced 5G protocols, and AI-driven network automation. His research interests span protocol design for wireless networks (e.g., IEEE 802.11bd, V2X systems), low-latency access mechanisms, and secure edge computing architectures. Collaborations involve industry standards bodies and academic institutions. He actively publishes in top-tier venues like IEEE Transactions and Vehicular Technology Conferences, with a focus on practical solutions for emerging telecom challenges. Key contributions include scalable resource allocation in MIMO-LEO satellite networks, ML-based fault detection in cellular systems, and energy-efficient UAV communication via RIS. His work bridges theoretical advancements with real-world deployment scenarios, emphasizing network security and automation in 5G/6G ecosystems.

• AI Safety
• Healthcare Informatics
• Machine Learning Applications
• +3 more

Yan Jia is a Lecturer in the Department of Computer Science at the University of York. Their research focuses on advancing AI safety and assurance in healthcare applications, particularly in clinical decision support systems and autonomous medical technologies. They explore critical issues like human-AI collaboration, safety frameworks for machine learning in healthcare, and ethical implications of AI deployment in medical settings. Key research areas include AI safety cases for medical applications, risk management in autonomous systems, and improving transparency of machine learning models for clinician trust. Their work bridges technical AI development with real-world clinical needs, emphasizing both technical rigor and human-centered design principles. Recent publications highlight contributions to AI safety in critical care scenarios, such as ventilator weaning protocols and sepsis treatment optimization. They also address legal and liability challenges emerging from AI integration in healthcare workflows. No awards or grants are explicitly listed in the provided materials. Their advising roles or student supervision details are not documented here.

Richard ConnorView profile

Professor

• Similarity Search
• Metric Spaces
• High-Dimensional Geometry
• +4 more

Richard Connor is a Professor of Computer Science at the University of St Andrews' School of Computer Science. He holds a BSc, PhD, and postdoctoral fellowships from the same institution, with prior roles including Director of Research and Head of Department at the University of Strathclyde. His research focuses on similarity search, high-dimensional geometry, and metric spaces, with contributions to algorithms like nSimplexZen and BitPart. He teaches distributed systems and advises PhD students, including Ben Claydon. Connor has engaged in tech startups and contributed to Scotland's computing education curriculum. His work addresses challenges in large-scale data search, leveraging geometric principles and innovative indexing techniques. Education: BSc and PhD from University of St Andrews (1981–1996) Key Roles: EPSRC Advanced Fellow, Chair at Strathclyde, Head of Division at Stirling, and return to St Andrews (2020) Research Interests: Connor investigates similarity search in high-dimensional spaces, focusing on metric geometry, Hilbert spaces, and dimensionality reduction. His work bridges theoretical foundations and practical applications like image retrieval and machine learning optimization. Recent projects include polyadic queries and Ptolemaic partitioning mechanisms to enhance search efficiency. Publications: His 2024 and 2025 works explore novel dimensionality reduction (nSimplexZen), cross-entropy loss correlations, and scalable polyadic queries, advancing the field through algorithmic innovation. Earlier contributions include BitPart for high-dimensional metric search and analysis of binary partitioning efficacy. Awards: Not explicitly listed, but his EPSRC Advanced Fellowship and academic leadership roles highlight recognition in the field. Advising & Grants: Supervised Ben Claydon’s PhD; led research funded via fellowships and institutional roles. Active in organizing academic events like Doors Open @ Computer Science. Labs/Teams: Engaged with MetricSearch research groups and collaborates internationally on similarity search projects.

Alice TonioloView profile

Lecturer

• Artificial Intelligence
• Argumentation Theory
• Human-AI Interaction
• +7 more

Dr. Alice Toniolo is a Lecturer in Computer Science at the University of St Andrews' School of Computer Science. Her research focuses on artificial intelligence, argumentation theory, human-AI interaction, and multi-agent systems. She leads projects exploring deliberation dialogues, planning frameworks, and collaborative intelligence analysis tools like CISpaces.org. Her work bridges formal models with real-world applications in education technology, healthcare, and spatial cognition. Research interests include argumentation schemes, provenance tracking, and normative systems in AI. She supervises PhD students in topics like diverse planning and AR-based learning. Toniolo has published over 40 peer-reviewed works, with recent contributions in CHI, IEEE Transactions, and AI journals. Her projects are funded by grants from the Scottish Funding Council and the Carnegie Trust. She collaborates internationally on tools for collaborative analysis, AR learning environments, and explainable AI systems. Notable contributions include the VocabulARy AR vocabulary tool, Deb8 dialogue analysis software, and the AIF-EL ontology for argument interchange. Current work emphasizes mitigating algorithmic loafing in AI teams and adaptive guidance in AR educational systems. She actively participates in initiatives like the UN Sustainable Development Goals through AI-driven solutions. Key Projects : Argumentation-Based Training (2020), Don't Make a Wave (2017), CISpaces Toolkit Tools : CISpaces.org, Deb8, Write Reason, AIF-EL ontology Grants : Scottish Funding Council, Carnegie Trust Labs/Teams: Core contributor to the CISpaces collaboration platform and the School's AI research group. Engages in interdisciplinary work across computer science, philosophy, and education.

• Astrophysics
• Stellar Formation
• Interstellar Medium Dynamics
• +3 more

Dr. Rowan Smith is a Lecturer in Astrophysics and Researcher at the School of Physics and Astronomy , University of St Andrews. His research focuses on star formation, interstellar medium dynamics, and galactic structure. He has supervised PhD student Zoe Faes and contributed to numerous peer-reviewed publications. His research interests include molecular gas conditions, magnetic fields in space, and the formation of high-mass stars. He has conducted studies on CO isotopologue analysis, interstellar flows, and 3D dust reconstruction using deep learning. His work often explores topics like molecular cloud evolution, galactic center dynamics, and the role of turbulence in star formation. Key contributions include analyzing the CO-to-H₂ conversion factors in M51, studying star formation in CO-dark dwarf galaxies, and investigating magnetic field interactions in star-forming cores. He has also developed computational models to study molecular cloud structure and interstellar medium properties. Dr. Smith collaborates internationally on projects like the PHANGS-JWST survey and the Cloud Factory simulations. His research bridges observational astronomy with advanced computational techniques, addressing fundamental questions about galaxy evolution and stellar formation processes.

Ivan SvetunkovView profile

Senior Lecturer

• Forecasting
• Statistical Modelling
• Marketing Analytics
• +5 more

Ivan Svetunkov is a Senior Lecturer at Lancaster University's Management Science Department, specializing in forecasting and statistical modeling. He holds a PhD from Lancaster Centre for Forecasting (2013) and previously worked at Higher School of Economics and Saint-Petersburg State University. His research focuses on state-space models, intermittent demand forecasting, complex-valued models, and marketing analytics. Education: MSc from Saint-Petersburg State University of Economics and Finance (2006), PhD in Economics (2013). Key contributions include the Complex Exponential Smoothing method and the development of R packages 'smooth', 'greybox', and 'legion'. He maintains academic presence through LinkedIn, Twitter, and a forecasting website. Research interests span intermittent demand modeling, multivariate forecasting systems, advanced estimators, time-varying parameters, promotional modeling, and likelihood-based approaches. He supervises PhD students in statistical forecasting, requiring strong math/programming skills (R/Python). Labs/Teams: Active in Lancaster Centre for Marketing Analytics and Forecasting. Collaborations include work with John Boylan on intermittent demand models and Demand Works on ARIMA implementations.

Ebroul IzquierdoView profile

Professor

• Multimedia Signal Processing
• Machine Vision
• Image Processing
• +7 more

Professor Ebroul Izquierdo holds a position at Queen Mary University of London's School of Electronic Engineering and Computer Science. His academic background includes a BSc, MSc, and PhD. He specializes in Multimedia Signal Processing, Machine Vision, and Mathematical models in image processing. His research focuses on video coding, computer vision, AI applications in agriculture, and security systems. He leads major grants such as the £305k Smart Farm project and the £370k SHIFT initiative. His work includes innovations in camera calibration for sports videos, self-supervised learning models, and federated learning architectures. He is affiliated with the Centre for Multimodal AI and collaborates with organizations like BBC and Innovate UK. His email is ebroul.izquierdo@qmul.ac.uk . Research Grants : Smart Farm and Agri-environmental Big Data Space (£305,116 EPSRC) SHIFT Project (£370,577 EPSRC) KTP: Machine Learning for Sport Broadcast Graphics (£214,061 Innovate UK) Labs & Teams : Centre for Multimodal AI, collaborating with BBC and EU Horizon 2020 initiatives.

• Complex Networks
• Network Science
• Ecological Networks
• +3 more

Dr. Athen Ma is a Senior Lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science. Their research focuses on complex networks, ecological systems, and interdisciplinary applications of network science. Key areas include ecosystem responses to global environmental change, ecological network resilience, and drought impacts on food webs. Teaching responsibilities include modules on Digital Circuit Design and Network Science, emphasizing probability, simulation, and reliability theory. Affiliations: Centre for Human-Centred Computing, Faculty of Science and Engineering Grants: £840,297 NERC Pushing the Frontiers Grant (2024–2027), EU SUNSET (£337,527) Research highlights include Nature Ecology & Evolution and Nature Climate Change publications on agro-ecosystem resilience and drought-induced food web rewiring. Current projects investigate sub-network structures in ecological systems under climate stress. Collaborations involve institutions like the University of Essex and the Macao Polytechnic Institute. Awards: Fellow of the Higher Education Academy (FHEA) Advising includes PhD students Lachlan Burton and Jorge Eduardo Castro Cruces, focusing on machine learning applications in ecological networks. The lab actively engages in interdisciplinary research, blending computational methods with ecological data to address global challenges.

David MguniView profile

Lecturer

• Multi-Agent Systems
• Reinforcement Learning
• Game Theory
• +3 more

Dr. David Mguni is a Lecturer in Computer Science at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science. His research focuses on multi-agent systems, reinforcement learning, game theory, and decision-making under uncertainty. He holds a PhD in applied mathematics from University College London and prior degrees from the University of Cambridge and the University of London. Before academia, he was a Principal Scientist at Huawei, leading the Multi-Agent Systems team in London. His work bridges theoretical foundations with practical applications, particularly in strategic decision-making and autonomous systems. He teaches the ECS656U/ECS796P Distributed Systems course and supervises students in reinforcement learning, game theory, and optimization. Research interests include non-zero-sum game frameworks, safe reinforcement learning (e.g., SAUTE RL), and reward shaping techniques. His publications span venues like AAMAS, NeurIPS, and IJCAI, addressing challenges in multi-agent coordination, adversarial environments, and algorithmic efficiency. Collaborations involve designing scalable methods for large systems and integrating language models into decision-making processes. David is part of Queen Mary’s Centre for Multimodal AI, contributing to projects on socially-attentive policies, risk-aware systems, and distributed optimization. His recent work emphasizes safety in multi-agent learning (e.g., SAUTE RL), variance reduction in estimator models, and frameworks for managing adversarial scenarios. He actively seeks PhD candidates to explore reinforcement learning and game-theoretic applications.

Gareth TysonView profile

Professor

• Computer Networking
• Social Computing
• Internet Measurements
• +3 more

Professor Gareth Tyson is a Professor of Computer Science at Queen Mary University of London's School of Electronic Engineering and Computer Science. He specializes in Internet Data Science, focusing on social computing, network measurements, and cybersecurity. Tyson has secured over £5 million in research funding and has been recognized with prestigious awards, including the IETF/IRTF Applied Networking Research Prize (2023) and the SIGSOFT Distinguished Paper Award (2023). His work spans interdisciplinary collaborations, addressing challenges in decentralized systems, content moderation, and AI-generated content analysis. His research emphasizes real-world impact, with coverage in media outlets like the BBC, MIT Tech Review, and The Washington Post. Tyson has organized major conferences such as ACM IMC and CoNEXT, and his contributions include foundational studies on adult video streaming, social media ecosystems, and blockchain transactions. He also leads initiatives like the Next Generation Networking Webinar Series and the UK's annual Coseners workshop. Awards include the Brendan Murphy Memorial Young Researcher Prize (2013), Distinguished Reviewer Awards (ACM CoNEXT 2022), and Outstanding Reviewer recognitions at ICWSM. His academic service includes roles on program committees for SIGCOMM, IMC, and WWW, underscoring his leadership in the field.