جستجوی استاد راهنمای دانشگاهی

• 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.

Kanishka Bhattacharyaمشاهده پروفایل

دانشیار مدعو

• Machine Learning
• Artificial Intelligence
• Generative AI
• +۷ مورد دیگر

Kanishka Bhattacharya is an Adjunct Associate Professor of Management Science and Operations at London Business School since 2019. He is also a Partner at Bain & Company, specializing in advising corporations on digital transformation, AI integration, and strategic innovation, particularly in Private Equity and Financial Services. His career spans academia, quantitative finance, and technology consulting. Education: BSc (India), MSc and DPhil (Oxford). Research interests focus on machine learning applications in business strategy, generative AI tools, and leveraging data science for organizational decision-making. He teaches core courses in statistics, machine learning, and leadership in digital innovation at London Business School. Professional experience includes developing high-frequency trading algorithms as a quant and creating AI-driven products for large enterprises. His cross-sector advisory work combines technical expertise with strategic business acumen.

Sam Amiriمشاهده پروفایل

مدرس

• Embedded Systems
• Hardware Design
• Signal Processing
• +۳ مورد دیگر

Sam Amiri is a Lecturer in Microelectronics / Embedded Electronics at Loughborough University. He holds a BSc in Software Engineering (2007, Iran), M.Sc. in Embedded Systems (2010, Masaryk University), and Ph.D. in Electrical/Electronic Engineering (2014, Masaryk University). Prior to his current role, he worked as a Researcher at Queen’s University Belfast (2015-2017) and the University of Bristol (2017-2018). His research focuses on hardware design, embedded systems, signal/image processing, and FPGA-based solutions for real-time systems. Key research areas include intrusion detection systems for vehicular networks using binarized neural networks, RISC-V processor optimization for neural networks, and FPGA-based acceleration of CNN models. His work also explores reliability engineering in wind turbine drivetrains and heterogeneous computing architectures combining FPGAs with CPUs. Publications span topics like embedded cybersecurity, low-power edge computing, and hardware-software co-design for real-time applications. His recent work emphasizes lightweight neural networks on constrained hardware and fault tolerance in aerospace systems. No specific awards or grants are listed in the provided information.

• 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.

Dev Gangjeeمشاهده پروفایل

استاد

• Intellectual Property Law
• Branding and Trade Marks
• Geographical Indications
• +۱۱ مورد دیگر

Dev Gangjee is Professor of Intellectual Property Law at the University of Oxford's Faculty of Law and a Fellow at St Hilda's College. He previously held roles at the London School of Economics and has been a Rhodes Scholar. His research emphasizes Intellectual Property (IP), with focus on Trade Marks, Geographical Indications (GIs), Copyright, and the historical/political economy of IP systems. He advises governments, law firms, and international organizations on IP issues. Education: National Law School of India University, Oxford University (Rhodes Scholar) Teaching: Private law areas including Contract, Land, and Tort Law Awards: Teaching Prize (LSE), Student Welfare Award (Oxford) Research interests include data rights, AI impacts on IP, and collective innovation. He is Director of the Oxford Intellectual Property Research Centre (OIPRC) and serves on editorial boards of the Modern Law Review and Queen Mary Journal of Intellectual Property . He holds visiting professorships at institutions including the Munich Intellectual Property Law Centre and has held fellowships globally.

• 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.

Jochen Leidnerمشاهده پروفایل

استاد مهمان

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Professor Jochen Leidner is a Visiting Professor in the Department of Computer Science at the University of Sheffield and a Research Professor for Explainable and Responsible AI at Coburg University of Applied Sciences, Germany. He has held leadership roles including Director of Research at Thomson Reuters and Refinitiv, and has founded companies like Polygon Analytics and KnowledgeSpaces. His academic background includes degrees from the University of Erlangen-Nuremberg, University of Cambridge, and a PhD in Informatics from the University of Edinburgh. Research focuses on AI ethics, natural language processing, information extraction, and geoinformatics. Notable contributions include work on question-answering systems (QED/ALYSSA), spatial toponym resolution algorithms, and risk-mining frameworks. Awards include the ACM SIGIR Doctoral Consortium Award and twice winning Thomson Reuters Inventor of the Year for patents. He has taught at institutions across Europe and advises EU funding bodies (FP7/Horizon). Holds multiple patents in information retrieval and mobile computing. Active in industry collaborations, blending academic research with real-world applications in finance, legal tech, and supply chain analytics.

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.

Martin Herdegenمشاهده پروفایل

دانشیار

• Mathematical Finance
• Probability Theory
• Stochastic Processes
• +۵ مورد دیگر

Martin Herdegen is a Reader in Financial Mathematics at the Department of Statistics, University of Warwick. He previously served as a postdoc at ETH Zürich under Johannes Muhle-Karbe and holds a PhD in Mathematics from ETH Zürich (2014) under Martin Schweizer. His research focuses on equilibrium theory, utility maximization, stochastic processes, and risk measures, with applications to financial bubbles and market microstructure. Herdegen’s academic career includes supervising multiple PhD students (e.g., Florian Gutekunst, Andreea Popescu) and postdocs (e.g., Nazem Khan). His research group explores topics like ρ-arbitrage, recursive utility, and liquidity provision under adverse conditions. He has contributed to foundational work on strict local martingales and their implications for financial markets. Publications span leading journals such as Finance and Stochastics , Mathematical Finance , and Annals of Applied Probability , addressing equilibrium models with transaction costs, optimal investment strategies, and risk measurement techniques. His work frequently integrates stochastic analysis and control theory to solve practical finance problems. Herdegen’s research also intersects with reinforcement learning applications in trading, as seen in the Mbt-gym framework for limit order book simulations. His contributions emphasize rigorous mathematical foundations while addressing real-world market frictions and liquidity dynamics.

Matthew Thorpeمشاهده پروفایل

دانشیار

• Machine Learning
• Data Science
• Applied Mathematics
• +۴ مورد دیگر

Matthew Thorpe is an Associate Professor in the Department of Statistics at the University of Warwick and a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). His research focuses on applying methods from applied analysis—including partial differential equations (PDEs), calculus of variations, and optimal transport—to machine learning and data science challenges. He has organized workshops such as the 'Machine Learning in Infinite Dimensions' at ETH Zurich and the 'LMS-Bath Symposium on Inverse Problems and Artificial Intelligence in Medicine.' Thorpe currently seeks PhD students for his research projects. His work bridges theoretical mathematics with practical applications in data-driven fields. Thorpe's research interests include manifold learning in Wasserstein space, PDE-based approaches to data science, and convergence analysis of graph-based learning algorithms. He has contributed to understanding the impact of imputation quality on machine learning models and developed novel transportation distances for pattern recognition. His interdisciplinary approach integrates mathematical rigor with advancements in artificial intelligence. He has co-organized the One World Seminar Series on the Mathematics of Machine Learning and remains active in promoting collaborative research initiatives. Despite no explicitly listed awards, his prolific publication record reflects sustained academic impact. Thorpe’s advising focuses on training students in the intersection of applied mathematics and modern data science techniques.