جستجوی استاد راهنمای دانشگاهی

• Sustainable Energy
• Energy Efficiency Technologies
• Renewable Energy Systems
• +۳ مورد دیگر

Dr. Xiaoli Ma is a Senior Research Fellow at the University of Hull, affiliated with the Energy and Environment Institute and the Centre for Sustainable Energy Technologies under the Faculty of Science and Engineering. Her research focuses on sustainable building services, renewable/sustainable energy systems, energy efficiency technologies, and instrumentation technologies. She has secured over £2.7 million in research funding from bodies such as the EU, EPSRC, Innovate UK, and the National Science and Technology Committee of China, leading or co-leading 26 projects. Her research interests include innovative cooling systems for data centers (e.g., super-performance dew point cooling achieving 90% energy savings), solar-driven energy systems, waste heat recovery, and thermoelectric technologies. Notable projects include the development of a novel Loop-Heat-Pipe-based data center cooling system funded by the EU FP7 and a solar façade hot water heating system supported by the EU FP7 Programme. Dr. Ma has published over 90 journal articles, two books, and three book chapters, with recent works focusing on machine learning-driven energy optimization, solar cooking systems with energy storage, and advanced heat pump technologies. She holds two patents and actively supervises PhD students in renewable energy, energy efficiency, and built environment applications. Her lab and team are part of the Energy and Environment Institute, collaborating on projects like the pioneering near-zero-carbon air conditioning system using atmospheric latent heat and natural light energy (EPSRC-funded). Key grants include a £814k EPSRC project and a £692k IEEA initiative for data center cooling advancements.

• Control Systems Engineering
• Mechatronics
• Condition Monitoring
• +۵ مورد دیگر

Roger Dixon is a Professor of Control Systems Engineering at the University of Birmingham's School of Engineering and a Visiting Professor at Loughborough University. His expertise spans applied control systems, mechatronics, condition monitoring, fault detection, and systems engineering. He holds a PhD from Lancaster University (1996) and has led major research initiatives in academia and industry, publishing over 150 papers. His work focuses on rail, road, aerospace, and energy sectors, emphasizing innovative solutions for industrial challenges. Education: BEng (Hons) Mechanical Engineering, Lancaster University (1992) MSc Mechatronics, Lancaster University (1993) PhD Control Systems Engineering, Lancaster University (1996) Research Interests: His research integrates advanced control strategies, fault detection, and condition monitoring. Key areas include: Rail infrastructure (e.g., fault-tolerant track switches) Aerospace actuator systems and fault-tolerant designs Energy systems (e.g., LNG platform monitoring) Model-based control and simulation Professional Highlights: Notable contributions include the 2013 REPOINT railway track switch concept and 2003 gas turbine fault-tolerant control patents. He chairs the UK Automatic Control Council (2017–2020) and has editorial roles in international journals. Awards: Fellow of the IMechE (2011) Fellow of the Higher Education Academy (2006) Chartered Engineer (2000) Lab & Collaborations: Leads the Control Systems theme at the Birmingham Centre for Railway Research & Education (BCRRE), focusing on rail innovation and multidisciplinary projects.

• Cybersecurity
• Machine Learning
• Network Security
• +۶ مورد دیگر

Dr. Zhiyuan Tan is an Associate Professor in the School of Computing at Edinburgh Napier University (ENU), specializing in cybersecurity research. He holds a PhD in Computer Systems from the University of Technology Sydney (UTS), Australia (2014), an MEng from Beijing University of Technology, China (2008), and a BEng with high distinction from North-eastern University, China (2005). Before joining ENU in 2016, Dr. Tan held research positions at the University of Twente (Netherlands), University of Technology Sydney (Australia), and La Trobe University (Australia). Dr. Tan's research focuses on cybersecurity, machine learning, data analytics, virtualisation, and cyber-physical systems. His work has resulted in over 44 scholarly publications with an H-Index of 13 and more than 830 citations according to Google Scholar. His recent publications demonstrate a continued focus on network security, intrusion detection systems, and the application of machine learning techniques to cybersecurity challenges, with publications spanning from 2022-2025 in top venues including IEEE Transactions and international conferences. Dr. Tan has received significant research funding, including AUD 27,800 from CSIRO and UTS for autonomous network intrusion detection research and £6,987 from ENU for securing future 5G health care systems. His research has been recognized with awards including the National Research Award 2017 from the Research Council of the Sultanate of Oman, a Best Paper Award, and the Kaspersky Lab's Annual Student Cyber Security Conference Finalist Award. National Research Award 2017 from the Research Council of the Sultanate of Oman Best Paper Award Kaspersky Lab's Annual Student Cyber Security Conference Finalist Award Dr. Tan has mentored 9 PhD students over the past 5 years, with 6 successfully completing their studies. His students have produced 12 journal and 10 conference publications. He has also served as an editorial board member for international journals, organized special issues, and participated as a technical program committee member for major international conferences. Dr. Tan is currently recruiting PhD students for research projects on network security, adversarial machine learning for anomaly/malware detection, virtualization security, and IoT security.

• Applied Mathematics
• Numerical and Computational Mathematics
• Partial Differential Equations
• +۴ مورد دیگر

Prof. Vanessa Styles is a Professor and Head of the School of Mathematical and Physical Sciences at the University of Sussex. Her research focuses on mathematical and computational analysis of nonlinear partial differential equations, with applications in physical sciences, including materials science, fluid dynamics, and biological modeling. She specializes in numerical methods for evolving surfaces, phase field models, and free boundary problems. Her work often involves interdisciplinary collaborations, addressing challenges in lithium batteries, tumor growth, and cell migration. Prof. Styles has led major grants, including ModCompShock (EU) and Leverhulme-funded projects on cell migration mechanics. She teaches calculus courses and actively contributes to academic leadership. Her research interests span computational fluid dynamics, numerical analysis of PDEs, and mathematical biology. Recent work emphasizes tumor growth modeling via phase field approaches, crystal growth in batteries, and cell migration force estimation. She has published extensively in journals like Interfaces and Free Boundaries and Journal of Computational Physics , with a focus on rigorous error analysis and algorithm development. Grants include funding for modeling grain boundary motion (EPSRC), 3D cell migration (Leverhulme), and shock interface modeling (EU). Her research bridges theoretical analysis and practical applications, with contributions to both fundamental mathematics and engineering/medical fields.

• Thermal Systems
• Battery and Vehicle Energy Management
• Model Predictive Control
• +۶ مورد دیگر

Professor Peter Fussey holds a dual role at the University of Sussex as a Professor in the School of Engineering and Informatics and Associate Dean for Global and Civic Engagement at the Faculty of Science, Engineering and Medicine. He leads the Energy and Materials Engineering Research Centre, focusing on Thermal Systems , Battery and Vehicle Energy Management , Model Predictive Control , and Data Analytics . Previously, he spent over 25 years at Ricardo UK, leading the Control Department and developing advanced control systems for hybrid/electric vehicles and thermal management. His career also includes rail acoustics research at British Rail Research and SNCF. Education: DPhil in Engineering from the University of Oxford MA in Engineering from the University of Cambridge Research Interests: Professor Fussey’s work spans automotive and mechanical engineering , with a focus on low-emission powertrains , energy management systems , and advanced control algorithms . His recent projects include optimizing EV cabin climate control for extended range, geofencing for smart mobility, and neuro-fuzzy modeling for thermal systems. He has also pioneered applications of model predictive control (MPC) in selective catalytic reduction (SCR) systems and engine combustion optimization. Teaching: Courses include Low Emission Vehicle Propulsion , Vehicle Dynamics , and supervising Formula Student projects. His teaching emphasizes practical applications of control theory and sustainability. Labs/Teams: Leads the Energy and Materials Engineering Research Centre , fostering interdisciplinary research in sustainable energy and advanced propulsion systems.

• Machine Learning
• Data Analytics
• Cybersecurity
• +۵ مورد دیگر

Dr. Priyanka Chaurasia is a Lecturer in Data Analytics at the School of Computing, Engineering and Intelligent Systems, Ulster University, based in the Derry~Londonderry campus. Her research focuses on machine learning applications in healthcare, cybersecurity, and assistive technologies. She contributes to interdisciplinary projects such as the EU-funded 'IT4Anxiety' mental health initiative and has expertise in AI-driven solutions for lead toxicity prediction, smart city security, and Metaverse privacy frameworks. Her academic background includes a strong emphasis on data science, with notable work in IoT security, medical image segmentation, and regulatory frameworks for large language models. She has collaborated on projects addressing technology adoption by older adults and individuals with dementia, emphasizing ethical AI and user-centric design. Dr. Chaurasia's recent publications span topics like deep learning for hate speech detection, privacy-preserving techniques in virtual environments, and computational modeling for environmental health risks. She maintains an active research profile, publishing in top-tier venues and co-authoring over 40 peer-reviewed articles since 2010. Her lab focuses on bridging theoretical advancements with practical healthcare and cybersecurity solutions.

• Multimodal Deep Learning
• Clinical Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Dr. Ruchit Agrawal is an Assistant Professor of Computer Science and Head of Computer Science Outreach at the University of Birmingham Dubai. Previously, he served as a Postdoctoral Researcher in AI for Healthcare at the University of Oxford’s Computational Health Informatics Lab, and as a Marie Curie AI Researcher in the transnational MIP-Frontiers project at Queen Mary University of London. His work focuses on optimizing healthcare systems using Machine Learning, alongside contributions to Natural Language Processing, Audio Signal Processing, and Multimodal Deep Learning. He holds a PhD in Computer Science from Queen Mary University of London and an MS by Research from IIIT Hyderabad. Education: PhD in Computer Science (Queen Mary University of London, 2021) MS by Research in Machine Translation (IIIT Hyderabad, 2017) Research Interests: Clinical Machine Learning for healthcare system optimization Natural Language Processing with a focus on Indian languages and context-aware models Audio Signal Processing for music performance analysis and stuttering detection Development of multimodal deep learning frameworks for diverse applications Adaptive AI systems leveraging contextual and positional encoding techniques Publications highlight trends in healthcare AI, multilingual NLP, and audio-visual alignment. Recent work includes Arabic sentiment analysis, stuttering detection via MMSD-Net, and stock price prediction using FB-GAN. Earlier contributions address structure-aware synchronization in music performance data and transformer-based post-editing for low-resource languages. His research bridges theoretical advancements with practical implementations in clinical, financial, and cross-modal domains. Scientific awards include the prestigious Marie Skłodowska-Curie scholarship (2017–2020) supporting his deep learning research in audio signal processing. Advising and grants: While no formal advisees are listed, his roles involve leading outreach initiatives and guiding collaborative projects at the Computational Health Informatics Lab during his postdoctoral tenure. Labs/Teams: Active member of the Computational Health Informatics Lab (Oxford) and Machine Translation group at FBK (Italy). His work also intersects with the MIP-Frontiers transnational research project.

• Socio-Technical Software Engineering
• Developer Attitudes
• Automation in Software Engineering
• +۳ مورد دیگر

Dr. Emily Winter is a Lecturer in Computing and Communications at Lancaster University, part of the School of Computing and Communications (SCC) since 2018. Her research focuses on socio-technical aspects of software engineering, including developer attitudes toward automation, human values in SE, and diversity in computing education. She holds a PhD in Sociology from Lancaster University, an MA in Sociological Research, and a BA in History. Emily has contributed to projects like the Fixie Project (investigating developer attitudes to automated tools) and the Values in Computing initiative. She is a Fellow of AdvanceHE and has received an ESRC 1+3 award for her postgraduate studies. Her external roles include serving as a Reviewer for Information and Software Technology , Workshop Chair for the International Workshop on Genetic Improvement (2022), and Program Committee member for several conferences. Emily teaches the SCC 130 module and actively engages in research addressing gender dynamics in CS education and ethical software design. Key research interests include qualitative methods in SE, developer-centric tool design, and fostering inclusive educational environments. Her work bridges social sciences and computing, emphasizing empirical studies of human-computer interaction in software development. Emily has published widely on topics like automatic program repair, SAST tool usage, and gender representation in CS. Her awards reflect her commitment to pedagogical innovation and social science contributions to computing. She is affiliated with the Security Lancaster research group and is based at InfoLab21.

• Information Systems
• Digital Innovation
• FinTech
• +۳ مورد دیگر

Dr. Ruilin Zhu is a Lecturer in Information Systems at Lancaster University Management School (LUMS), Lancaster University, within the Department of Management Science. His academic profile demonstrates active engagement in research supervision, with current PhD students under his guidance. Prior to his academic career, Dr. Zhu gained industry experience in financial institutions and completed visiting scholar positions internationally. Research Focus: Dr. Zhu's work integrates behavioral, economic, and legal perspectives to examine digital innovation phenomena. His primary domains include: FinTech and InsurTech systems development Health informatics and technology adoption Risk communication frameworks Digital transformation in business ecosystems Socio-technical aspects of information systems This multidisciplinary approach reflects his industry background and international research exposure. Publication Analysis: Recent scholarly output (2022-2024) reveals strong focus on crisis communication during COVID-19, health technology integration, and FinTech innovation. Methodologically, Dr. Zhu employs longitudinal studies, bibliometric analysis, and structural modeling to examine technology adoption patterns across healthcare, education, and SME sectors. Emerging themes include algorithmic fairness, digital banking evolution, and privacy concerns in technology deployment. Research Projects: Pennies Each Day (2024-2025): Micro-savings mechanisms in developed economies Digital health accessibility for elderly populations in Chinese communities (2019) Electronic food facilitation systems for export optimization in Bangladesh (2018-2019) Academic Leadership: Dr. Zhu currently supervises PhD candidate Itoro Abraham in Information Systems research. He contributes to LUMS research groups including the Centre for Technological Futures and Information Systems research cluster, facilitating cross-disciplinary technology innovation.

Manuchehr Soleimaniمشاهده پروفایل

استاد

• Electrical and Electromagnetic Tomography
• Medical Imaging
• Industrial Tomography
• +۵ مورد دیگر

Professor Manuchehr Soleimani is a Professor in the Department of Electronic & Electrical Engineering at the University of Bath, leading the Engineering Tomography Lab (ETL) since 2011. He specializes in AI-driven tomographic imaging techniques, including Electrical/Electromagnetic Tomography, X-ray CT, and Ultrasound Tomography. His research focuses on medical imaging, industrial process monitoring, and robotic touch sensing. He collaborates with CERN on projects like the TIGRE open-source software platform and leads Bath's educational initiatives, including the MSc in AI for Engineering and Design and the Robotics Engineering undergraduate program. His research interests span inverse problems, machine learning integration, and multi-modality tomography. Key applications include lung monitoring, industrial quality control, and medical diagnostics. Professor Soleimani has secured funding from Innovate UK, EPSRC, EU Horizon 2020, and industry partners. He serves on the Scientific Advisory Panel at the University of Cambridge and has supervised 31 doctoral/master’s students. His lab develops cutting-edge imaging systems, such as contactless EIT sensors and ultrasonic tomography for in-vivo imaging. Key Projects: AdvanCT (EU), TIGRE (CERN collaboration), and industrial tomography systems for steel casting and CO₂ capture. Publications: Over 378 peer-reviewed articles, with recent focus on AI-enhanced reconstruction algorithms and multi-frequency imaging. Scientific contributions include pioneering work in capacitively coupled tomography and tactile sensing systems. His work aligns with UN SDGs through innovations in health and sustainable industry practices.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Ioannis Maniadis Metaxas is a lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science. He teaches undergraduate and postgraduate courses including Artificial Intelligence, Machine Learning, and Machine Learning for Visual Data Analysis. His research focuses on: Unsupervised representation learning Computer vision systems Large vision-language models Deep clustering techniques Generative models for data augmentation E-commerce personalization systems Recent publications demonstrate consistent focus on efficient learning methods for visual data, particularly investigating unsupervised pretraining, cluster optimization, and model fine-tuning approaches. His work shows progression from foundational 3D data augmentation to cutting-edge vision-language model optimization.

• Robotics
• Control Systems
• Computer Vision
• +۴ مورد دیگر

Mr. Sami Siddiqui is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. He is affiliated with the Peter Landin building (Room CS 419) and can be reached at m.s.siddiqui@qmul.ac.uk . His research interests span Robotics, Control Systems, and Computer Vision, with a focus on applications such as haptic feedback systems, robotic grasping under uncertainty, and exoskeleton design for medical rehabilitation. He also explores Bayesian models for autonomous systems and advanced control techniques in mechatronics. Mr. Siddiqui’s publications reflect a trajectory from early work on crane control systems and Urdu sign language recognition to recent advancements in underwater robotics and dexterous manipulation. His contributions highlight interdisciplinary approaches at the intersection of robotics, AI, and biomedical engineering. No scientific awards or grants are explicitly mentioned in the provided texts. Advising roles or student supervision details are not listed here. His involvement in specific labs or teams is not detailed in the available information.

Dirk Sudholtمشاهده پروفایل

استاد

• Randomised Algorithms
• Algorithmic Analysis
• Combinatorial Optimisation
• +۵ مورد دیگر

Dr. Dirk Sudholt is a Full Professor at the University of Passau and a Visiting Professor at the University of Sheffield. He holds a Ph.D. from Technische Universität Dortmund and has held postdoctoral positions at the International Computer Science Institute (ICSI) in Berkeley and the University of Birmingham. His research focuses on randomized algorithms, algorithmic analysis, and combinatorial optimization, with expertise in the theoretical analysis of bio-inspired search heuristics like evolutionary algorithms and ant colony optimization. His work emphasizes rigorous runtime analysis to understand algorithmic performance and design principles. Education: PhD in Computer Science, Technische Universität Dortmund (2008) Diploma in Computer Science, Technische Universität Dortmund (2004) Research Interests: Runtime analysis of evolutionary algorithms Algorithmic design for multimodal optimization Noise robustness in metaheuristics Parallel and distributed evolutionary computation Grants: SAGE: Speed of Adaptation in Population Genetics and Evolutionary Computation (EU FP7, 2014–2016) Teaching: University of Passau: Courses on algorithms, evolutionary computation, and randomized algorithms

Rajan Jagpalمشاهده پروفایل

پژوهشگر

• Composite Materials
• Manufacturing Processes
• Hydrogen Storage Technologies
• +۳ مورد دیگر

Rajan Jagpal is a Researcher in the Department of Chemical Engineering at the University of Bath, affiliated with the Centre for Integrated Materials, Processes & Structures (IMPS), The Foundry Centre for Digital Manufacturing & Design, and the Centre for Sustainable Energy Systems (SES). He holds a PhD in Automated Composite Manufacturing (2022) supervised by Prof. Evangelou, Prof. Loukaides, and Dr. Butler. His research focuses on composite materials processing, hydrogen storage technologies, UAV photogrammetry applications, and sustainable manufacturing systems. Key research contributions include optimizing non-crimp fabric preforming via magnetic clamping and Bayesian algorithms, developing freeze-cast porous composites for hydrogen storage, and advancing UAV-based manufacturing quality control. He collaborates on projects like the High Speed Microtomography initiative (2022–present) investigating carbon fabric deformation mechanisms. His work addresses UN Sustainable Development Goals related to affordable clean energy and industry innovation. He actively engages in public outreach, contributing to school engagement activities from 2018–2019. Jagpal is currently accepting doctoral students for research aligned with his expertise in advanced manufacturing and materials science.

Vaggelis Giannikasمشاهده پروفایل

استاد

• Supply Chain Management
• Logistics
• Operations Management
• +۵ مورد دیگر

Vaggelis Giannikas is a Professor of Management Information, Decisions & Operations at the University of Bath, where he also serves as Director of the Centre for Smart Warehousing and Logistics Systems and Director of Studies for the MSc in Operations, Logistics, and Supply Chain Management. His research focuses on the adoption and evaluation of emerging technologies in supply chain and logistics operations, with particular emphasis on digitalization, robotics, and automation in manufacturing and warehouse environments. Giannikas holds a PhD in Operations Management and Technology from the University of Cambridge (2010–2014). His work aligns with UN Sustainable Development Goals, contributing to sustainable urban freight logistics and resilient supply chain practices. He leads multiple projects funded by Innovate UK and the Engineering and Physical Sciences Research Council (EPSRC), including Logidot AI Assistant for Enhanced AI Competency in Logistics and Made Smarter Innovation: People-Led Digitalisation. His research interests span technology implementation challenges, warehouse automation, human-robot collaboration, and dynamic inventory optimization. He actively supervises doctoral students exploring digital technologies in logistics and supply chain management. Key areas of exploration include last-mile delivery efficiency, robotic adoption in SMEs, and stochastic programming for production resilience under disruptions. Giannikas collaborates widely with industry partners, focusing on practical applications such as AMR-assisted picking systems, urban micro-fulfillment centers, and dark store operations. His work bridges academic research with real-world logistics challenges, emphasizing both technological innovation and human-centric design in digital transformation initiatives.