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Fabrizio BonatestaView profile

Senior Lecturer

• Thermofluids
• Internal Combustion Engines
• Computational Fluid Dynamics (CFD)
• +9 more

Fabrizio Bonatesta is a Reader in Thermofluids at Oxford Brookes University, affiliated with the School of Engineering, Computing and Mathematics. He holds a PhD from the University of Nottingham and has been a permanent academic staff member at Oxford Brookes since 2010. His research and teaching focus on thermofluids, internal combustion engines, and emissions reduction, with strong collaborations with Ford, Siemens, and other UK universities. Educational Background: PhD in Engine Research, University of Nottingham, UK Research Interests: Fabrizio Bonatesta's research centers on experimental and numerical modeling of combustion and emissions in gasoline and diesel engines, particularly focusing on particulate matter in modern GDI engines. He has extensive expertise in reactive flow and CFD modeling using commercial and open-source software. His work also extends to sustainable energy systems, including 3D CFD modeling of transpired solar collectors for energy-efficient building heating. His research group, Propulsion and Pollution Modelling (PPM), actively collaborates with Ford, Siemens, Loughborough University, and the University of Nottingham. Recent Research Trends: His recent publications (2015–2024) highlight a strong trajectory in CFD-based modeling of fuel injection, combustion, and soot formation in GDI engines, alongside interdisciplinary work on urban pollutant dispersion and solar thermal systems. The integration of chemical kinetics with CFD and the focus on real-world emission reduction strategies are recurring themes. Scientific Awards: Central Research Funds Award (2013-14) Central Research Funds Award (2014-15) Research Excellence Award (2015-16) Research Excellence Award (2016-17) Research Leadership and Grants: Dr Bonatesta leads significant research projects, including the APC6 DynAMO project (co-sponsored by the Advanced Propulsion Centre, £22M total, £1.35M to Brookes) with Ford, Loughborough, Bath, Siemens, and others. He is also Principal Investigator on the 'Cavendish' project (Innovate UK) developing zero-CO2 hydrogen combustion systems for heavy-duty transport. His sustained funding from internal and external sources reflects the impact and relevance of his work. Research Groups and Labs: He leads the Propulsion and Pollution Modelling (PPM) research group at Oxford Brookes and is affiliated with the Centre for AI, Culture and Society (CAICS). His lab conducts advanced CFD simulations and experimental work in collaboration with industry and academic partners. He also supports interdisciplinary research with the Architectural Engineering Research Group on sustainable heating technologies.

Fang WangView profile

Senior Lecturer

• Artificial Intelligence
• Nature-Inspired Computing
• Multi-Agent Systems
• +12 more

Dr. Fang Wang is a Senior Lecturer in the Department of Computer Science at Brunel University London, affiliated with the College of Engineering, Design and Physical Sciences. Holding a PhD in Artificial Intelligence from the University of Edinburgh, she transitioned from a senior researcher role at the BT Group's research center to academia in 2010. Education: PhD in Artificial Intelligence (University of Edinburgh) Professional Background: Senior Researcher at BT Group Her research spans Artificial Intelligence and its interdisciplinary applications, focusing on nature-inspired computing (swarm intelligence, evolutionary computing), multi-agent systems , neural networks , and computer vision with applications in network optimization, healthcare, and education. Recent work explores fault detection , deepfake exposure , and medical informatics . The 15 most recent publications reveal trends in machine learning for industrial diagnostics, multi-agent reinforcement learning with formal logic constraints, and vision-language systems for medical applications. Subfields include neural networks , temporal/spectral analysis , and human-computer interaction . Scientific Recognition : Gordon Radley Technical Premium Highly Commended award (BT) ACM Best Student Paper Award (International Conference on Autonomous Agents) As an educator, she teaches programming, algorithms, digital innovation, and project-based courses to undergraduates and MSc students, with class sizes ranging from 8 to 350. She supervises PhD students in topics like self-organizing agents, intelligent intrusion detection, and human action recognition.

Erwin PauwsView profile

Associate Professor

• Craniofacial biology
• Developmental genetics
• Medical genetics
• +5 more

Erwin Pauws is an Associate Professor in the Developmental Biology & Cancer Department at University College London's Great Ormond Street Institute of Child Health. His research focuses on molecular mechanisms of craniofacial birth defects like cleft palate and craniosynostosis, utilizing genetic mouse models and computational approaches. University College London (2009-present) Imperial College London (2004-2006) University of Amsterdam (1994-2003) Research Interests: Dr. Pauws investigates the pathogenesis of human mutations in the FGF signaling pathway affecting bone development and homeostasis. His lab focuses on cellular mechanisms of craniofacial anomalies to develop translational therapies. Current funding comes from Great Ormond Street Hospital Children’s Charity, Newlife Charity, and EPSRC. Publications and Impact: With over 40 peer-reviewed papers and an H-index of 25, his work spans craniofacial biomechanics, mouse modeling, and clinical translation. Recent studies examine mechanical loading effects on craniofacial development and computational tools for tissue segmentation. Scientific Recognition: Senior Fellowship Higher Education Academy (2022-present) Associate Fellowship Higher Education Academy (2016-2022) Recognised Practitioner in Advising (2025-present) Royal Society of Biology member (2024) Teaching and Leadership: He serves as Programme Director for MSc Paediatrics and Child Health and teaches across UCL's biosciences and medical school curricula. His educational philosophy emphasizes research-led teaching and scholarly discussion over passive knowledge transfer.

• Experimental Particle Physics
• Data Acquisition Systems
• Silicon Detectors
• +3 more

Dr. Karol Hennessy is a Research Associate at the University of Liverpool , School of Physical Sciences, Department of Physics. They work on Experimental Particle Physics with focus on Data Acquisition Systems and Silicon Detectors at CERN's LHCb experiment. Coordinates LHCb's VELO data acquisition team Involved in VELO upgrades for LHC Runs 1–3 Member of DUNE experiment's DAQ coordination Developing Machine Learning algorithms for FPGA-based particle tracking Research interests center on high-energy physics detector systems , particularly silicon tracking detectors in extreme radiation environments . Their work spans hardware development, firmware programming, and data acquisition optimization for large-scale experiments. Recent publications analyze B meson decays , CP violation , and charmonium production , while technical projects focus on FPGA acceleration using Intel's oneAPI framework. They supervise Liverpool students at CERN and collaborate across international teams including LHCb and DUNE.

Alan ChandlerView profile

Professor

• Sustainable construction
• Low-carbon materials
• Heritage conservation theory
• +3 more

Alan Chandler is a Reader in Architecture and Dean of Research at the University of East London’s Department of Architecture & Visual Arts, part of the School of Architecture Computing and Engineering. He co-leads the Sustainability Research Institute and holds roles such as Co-Chair of the RIBA Conservation Register Steering Group and Visiting Professor at Universidad Católica, Santiago de Chile. His work bridges academia and practice, focusing on sustainable materials innovation, heritage conservation’s social impact, and collaborative research aligning with UN SDGs. Education and teaching include prior appointments at the Architectural Association, Goldsmiths’ College, Cambridge University, and the Cass School of Art & Architecture. His research emphasizes fabric-formed concrete, low-carbon construction, and the political dimensions of architectural heritage. He coordinates interdisciplinary projects like the Sugarcrete initiative with Tate & Lyle and leads decarbonization initiatives funded by ERDF and Emerald programs. Research interests span material science (bio-composites, rammed earth), heritage theory (decolonial approaches, community engagement), and sustainable practice. Notable projects include COP26 Pavilion Proposals and Pollution Pods exhibitions. He has secured grants such as the Newton Fund (2017) and OfS SPICE bid (2020), and advises RIBA Regional Awards and conservation accreditation frameworks. Advising and leadership include directing PhD studies and leading REF panels (2014, 2021). His labs/teams collaborate with Heatherwick Studios, DOKA GmbH, and Tate, fostering knowledge exchange through CPD lectures and international conferences. Ongoing work integrates technical innovation with public engagement, exemplified by student internship programs and cross-sector decarbonization partnerships.

Luciano GerberView profile

Senior Lecturer

• Machine Learning
• Data Science
• Applied Machine Learning
• +4 more

Luciano Gerber serves as a Senior Lecturer in the Department of Computing and Mathematics at Manchester Metropolitan University (MMU), where he leads the MSc Computing Network Programme. He maintains an Honorary Researcher position at Northern Care Alliance NHS Foundation Trust and operates from room 3.24 in the Dalton Building, with office hours available via Calendly appointments. His academic credentials include a PGCE and MSc in Computing, forming the foundation for his teaching and research career. Gerber's research spans Machine Learning and Data Science with pronounced emphasis on applied implementations . Key focus areas include remote sensor data forecasting, emoji sense disambiguation in social media, and financial data processing for banking applications. His methodology bridges fundamental research with real-world problem solving across diverse sectors. Analysis of his publication trends reveals consistent application of ensemble learning and time series analysis to domain-agnostic challenges. Recent work demonstrates expertise in social media analytics (emoji prediction), trustworthy AI frameworks for SMEs, and cash flow forecasting from banking data - highlighting his practical approach to data science. Scientific awards: No specific awards or fellowships documented in available sources Teaching and supervision: Gerber delivers postgraduate modules including Principles of Data Science, Machine Learning Concepts, and Advanced Machine Learning. He serves as external examiner for Liverpool John Moores University's mathematics programmes, providing academic oversight for BSc Mathematics and BSc Mathematics with Finance degrees. Laboratory and team affiliations: Gerber collaborates with Northern Care Alliance NHS Foundation Trust on healthcare data science initiatives while contributing to MMU's Computing and Mathematics research groups focused on AI applications and digital systems development.

• Residual Stress Analysis
• Eigenstrain Theory
• Creep Mechanisms in Superalloys
• +5 more

Dr. Fatih Uzun is a Postdoctoral Research Fellow in Engineering Science at the University of Oxford, funded by the Marie Skłodowska-Curie European Fellowship. His research focuses on residual stress analysis, eigenstrain theory, creep mechanisms in superalloys, and applications of artificial intelligence in materials science. He holds a DPhil (PhD), MSc, and BSc in Chemical Engineering from Yeditepe University. Research Interests: - Residual stresses and eigenstrain reconstruction - Creep behavior of superalloys in high-temperature applications - AI-driven material modeling and fuzzy systems - Digital image correlation (DIC) and tensegrity structures His current project, RESTREIG, develops computational tools to analyze creep behavior in welded aerospace components using eigenstrain theory and experimental methods like diffraction and contour analysis. Collaborations involve ABAQUS finite element modeling and industry partnerships for high-temperature material applications. Key achievements include pioneering work in height DIC (hDIC) for triaxial surface deformation analysis and integrating AI with eigenstrain theory for residual stress evaluation in Inconel alloys. His research bridges computational mechanics, experimental materials science, and advanced manufacturing challenges. Grants/Funding: - Marie Skłodowska-Curie Fellowship (2016–present) - RESTREIG Project (2016–2025) Labs/Teams: - Solid Mechanics and Materials Engineering Research Group (Oxford) - Collaboration with Jenkin Building facilities for experimental and computational studies

• Distributed Machine Learning
• Edge Computing
• Federated Learning
• +2 more

Christos Anagnostopoulos is Reader (Associate Professor) in Distributed Computing & Data Engineering Systems at the University of Glasgow, where he directs the MSc Information Technology and MSc Software Development programmes. His research focuses on distributed computing, federated learning, and edge intelligence for large-scale systems. Research interests span: Distributed ML/AI for edge environments Federated learning optimization Proactive data management in pervasive systems Edge-centric predictive analytics Funded projects include EU Horizon grants TERRA, COIN-3D, ELLIE, and TRACE, focusing on intelligent platforms for climate services, 3D VLSI reliability, cultural heritage technologies, and logistics optimization. Additional support comes from EPSRC, Royal Academy of Engineering, and industry partners including BMW and NXP. Recent publications (2024-2012) demonstrate innovations in edge-based task allocation, federated learning efficiency, distributed query processing, and mobile edge offloading. Article trends show consistent development of resource-aware distributed intelligence algorithms. Serves as Editor-in-Chief of Open Computer Science and Associate Editor of IEEE Access, and General Chair of IEEE ICDCS 2025. Leads the Knowledge and Data Engineering Systems research group.

Yutian TangView profile

Assistant Professor

• AI+SE (Large-language Model+SE)
• Program Analysis
• Empirical Software Engineering
• +4 more

Dr. Yutian Tang is an Assistant Professor at the University of Glasgow's School of Computing Science. He holds a Ph.D. from The Hong Kong Polytechnic University and a B.Sc. from Jilin University. His research focuses on AI-driven software engineering, program analysis, and mobile app security, with over 40 publications in top venues like ESEC/FSE and IEEE TSE. He has received awards including the ISSRE'18 Best Industry Paper and Shanghai Pujiang Talent Program. Current research emphasizes automated testing, bug localization, and LLM applications in software engineering. Education: Ph.D., Department of Computing, The Hong Kong Polytechnic University B.Sc., Computer Science, Jilin University Research Interests: His work spans AI+SE (Large Language Models in Software Engineering), automated testing, privacy protection, and malware detection. Notable contributions include XDebloat (app debloating) and tools for analyzing Android vulnerabilities. He actively explores LLM applications for code generation and test suite optimization. Grants & Funding: EPSRC Peer Review College member National Natural Science Foundation of China (NSFC) grant (2023-2025) OpenAI API Researcher Access Program (2024) Google Cloud Research Credits (2024) Service & Leadership: Editorial Board Member of Frontiers in Computer Science , PC member of ICSE, ESEC/FSE, ASE, and other top conferences. Active in reviewing for IEEE Transactions and ACM journals. Future Directions: Tool support for automated bug detection, LLM integration in software lifecycle, and large-scale binary code analysis.

• Building Information Modeling (BIM)
• Construction Management
• Optimization Algorithms
• +5 more

Claudio Benghi is an Assistant Professor in the Architecture and Built Environment Department at Northumbria University. He holds a PhD in Engineering awarded in March 2004. His research focuses on Building Information Modeling (BIM), optimization algorithms, interoperability in construction technologies, and sustainability in construction. Key areas include geometric optimization of BIM models, collaborative workflows in construction projects, and development of interoperable BIM software tools like the xBIM Toolkit. Dr. Benghi has produced 18 research outputs including peer-reviewed articles, book chapters, and conference papers. His work addresses challenges in MEP project optimization, IFC model validation, and digital plan of work automation. He collaborates with international researchers on projects like geometric optimization algorithms and constraints in BIM component design. His contributions span theoretical advancements and practical tools, such as the XBIM.Essentials library for interoperable applications. Recent research emphasizes circularity data integration in BIM standards and leveraging big data for construction value optimization. His publications highlight trends in BIM interoperability, AI applications in interactive art installations, and 5D modeling systems linking cost, schedule, and 3D models. He actively participates in conferences and journals like Automation in Construction and The Journal of Open Source Software. Notable projects include longitudinal interoperability tests for BIM components and automated verification of collaborative workflows. His work bridges civil engineering, computer science, and construction management to advance digital construction practices and sustainable building strategies.

• Accelerator Physics
• Particle Physics
• Beam Dynamics
• +3 more

William Shields serves as a Lecturer in Accelerator Physics within the Department of Physics at Royal Holloway, University of London, and is affiliated with the Centre for Particle Physics and Astronomy. Holding an ORCID identifier (0000-0002-4825-0303), he actively contributes to accelerator science through research and teaching. His research spans Accelerator Physics , Particle Physics , and Radiobiological Applications , with specific focus on beam dynamics, radiation resiliency, and laser-hybrid acceleration systems. Notable work includes the Laser-hybrid Accelerator for Radiobiological Applications (LHARA) project, aiming to develop novel accelerator technologies for medical research. Recent publications demonstrate strong alignment with medical physics applications, particularly in developing accelerator systems for radiobiology and cancer treatment. His 2025 IPAC conference contributions highlight expertise in beam delivery simulation (BDSIM), storage ring engineering, and radiation-hardened components. Shields participates in three major STFC-funded projects: the John Adams Institute Core Grant (2025-2028), LhARA-ITRF bridging (2024-2025), and the completed LhARA-ITRF project (2022-2024). He collaborates extensively with Diamond Light Source, CEBAF, and the John Adams Institute for Accelerator Science. Within the Centre for Particle Physics and Astronomy, he contributes to teams advancing accelerator simulation frameworks and medical applications. His work supports UN Sustainable Development Goals related to health innovation through accelerator-based radiobiological research.

Eugene VasilievView profile

Research Fellow

• Galactic Dynamics
• Stellar Dynamics
• Supermassive Black Holes
• +12 more

Dr. Eugene Vasiliev is an STFC Ernest Rutherford Fellow at the School of Mathematics and Physics, University of Surrey, UK. He specializes in galactic and stellar dynamics, focusing on supermassive black holes, Milky Way modeling, and the use of Gaia astrometric data. He develops advanced computational tools such as AGAMA, Forstand, and SMILE for dynamical modeling and action-based analysis. His research spans: Dynamical modeling of galaxies using the Schwarzschild method Action-based modeling of the Milky Way and dark matter halos Interactions between the Milky Way and the Large Magellanic Cloud Tidal disruption events and black hole feeding rates Structure and evolution of globular clusters and nuclear star clusters His recent publications (2022–2024) show a strong focus on refining Milky Way models using Gaia data, exploring phase-space substructures from past mergers, and improving black hole mass measurements in nearby galaxies. He frequently collaborates with leading researchers like James Binney, Vasily Belokurov, and Monica Valluri. His work is instrumental in advancing our understanding of galactic formation and evolution in the era of large-scale surveys. He has developed key software tools widely used in the astrophysics community. STFC Ernest Rutherford Fellowship Dr. Vasiliev actively mentors and collaborates on projects involving dynamical modeling, data analysis, and software development. He has delivered numerous talks and outreach lectures on black holes, galactic dynamics, and Gaia science. He leads research efforts that integrate observational data with theoretical modeling to probe the structure and history of the Milky Way.

Laurence NevayView profile

Researcher

• Particle Accelerators
• Beam Physics
• Medical Applications of Particle Physics
• +7 more

Laurence Nevay is a Researcher at the John Adams Institute for Accelerator Science , affiliated with the School of Physical Sciences and Department of Physics at Royal Holloway, University of London . Since 2017, he has served as a Project Associate at CERN for the High Luminosity LHC project. His work bridges accelerator physics, radiation simulation, and medical applications. Education: MPhys in Physics, University of St Andrews (2002–2007) DPhil in Particle Physics, University of Oxford (2007–2011) Research Focus: Laurence specializes in beam simulation and particle accelerator design , particularly through his development of the BDSIM tool, which tracks particle interactions and radiation effects in accelerators. His work addresses critical challenges in high-energy physics (e.g., Large Hadron Collider upgrades) and medical applications, such as precise radiation dose mapping for cancer therapy. Article Trends: Recent publications highlight advancements in BDSIM software, applications of Geant4 for energy deposition modeling, and radiation mitigation strategies for future colliders. Subfields include cryogenic component protection, beam diagnostics, and integration of simulation frameworks in accelerator design. Teaching & Outreach: He lectures accelerator physics at Royal Holloway and engages in public outreach, co-developing hands-on demonstrations for electrostatic accelerators and cryogenic superconductors. In 2016, he delivered a public lecture on accelerator design to over 200 attendees. Labs & Collaborations: Laurence leads a team at Royal Holloway focused on background and energy deposition studies. He collaborates extensively with CERN (since 2017) and institutions like KEK in Japan (2007–2013), contributing to projects such as the HiLumi LHC and laser-based beam diagnostics.

Goran SoldarView profile

Senior Lecturer

• Large Scale Data Processing
• Secure Computer Systems
• Machine Learning
• +3 more

Goran Soldar is a Senior Lecturer at the University of Brighton, UK, affiliated with the School of Computing, Engineering and Mathematics. With over 20 years of higher education teaching experience, he specializes in programming, databases, operating systems, and enterprise middleware for large data processing. He leads several undergraduate and postgraduate computing courses and is involved in course design and student learning enhancement. Education: PhD, University of East Anglia (2008) MSc Distributed Systems, University of East London (1995) BSc (Hons) Information Systems, University of Belgrade (1985) Research Interests: Large-scale data processing frameworks Secure computer systems design Machine learning applications Transaction management in distributed systems Software engineering project management methodologies His work spans audio segmentation for health applications, agent-based risk management, and semantic web architectures. Research Trends: Recent work focuses on AI-driven healthcare diagnostics (e.g., cough analysis for disease detection) and automation of IT project risk management through multi-agent systems. Earlier contributions include foundational work in semantic web ontologies and distributed systems architectures. Collaborations: Co-led the Lankelma KTP project (2021-2023) focusing on geotechnical data analysis. Active in international conferences with publications in applied informatics, database systems, and semantic technologies. Teaching & Leadership: Course leader for multiple computing programs. Committed to innovative curriculum development and student-centered learning approaches.

• Steel and Composite Systems
• Perforated Beams
• Structural Topology Optimisation
• +7 more

Konstantinos Daniel Tsavdaridis is a Full Professor in Civil Engineering at City, University of London, leading the Steel, Steel-Concrete Composite and Hybrid Structures group. He holds dual roles as a researcher, educator, and industry consultant, with expertise in structural design, seismic resilience, and sustainable construction. His academic journey includes a PhD from City University London and post-doctoral appointments at Leeds University and City University. Education: BEng in Civil Engineering (City University London, 2005) MSc in Structural Engineering (Imperial College London, 2006) PhD in Civil/Structural Engineering (City University London, 2010) Research focuses on innovative steel and composite systems, including perforated beams, topology optimization, and seismic-resistant design. His group pioneered 3D-printed modular connections and lightweight composite flooring. Key contributions include over 160 peer-reviewed publications and patents in structural engineering. Professional roles: Senior Fellow, Royal Academy of Engineering (2019) Membership: Institution of Civil Engineers (Fellow), Engineering Council (Chartered Engineer), and multiple international engineering societies. Grants and funding: EPSRC grants for modular building resilience and energy-efficient design Royal Academy Fellowship for 3D-printed connections Labs/Teams: Leads the Steel-Composite Structures Group and the 3DMBC (3D-Printed Modular Building Connections) initiative, collaborating with industry and global partners.