جستجوی استاد راهنمای دانشگاهی

• Robot-assisted Manufacturing
• Human-Robot Collaboration
• Additive Manufacturing Quality Control
• +۳ مورد دیگر

Kun Qian is a Lecturer in Robot-assisted Manufacturing at the University of Manchester. He holds a B.Eng. and M.S. from the University of Leeds (2016-2017) and a Ph.D. in Rehabilitation Robotic Control (2022). Prior to joining UoM in 2024, he worked at Heriot-Watt University (2023-2024) and the University of Leeds (2022). His research focuses on control and machine vision technologies for advanced manufacturing systems. Key areas include additive manufacturing quality control, vision-based environment sensing, and AI-driven human-robot collaboration. His work addresses scalability, productivity, and adaptability in manufacturing through interdisciplinary approaches. Dr. Qian has published over 20 peer-reviewed papers and collaborates with industries via funding from EPSRC, STFC, and ISCF. He is actively mentoring PhD students in topics like human-in-the-loop collaboration and 3D reconstruction for quality control. Notable research contributions include iterative learning control, robotic assembly optimization, and medical robotics innovations. His work aligns with UN Sustainable Development Goals related to industry, innovation, and infrastructure.

• Control Systems
• Convex Optimization
• Aerospace Systems
• +۷ مورد دیگر

Dr. Emmanuel Prempain is an Associate Professor at the School of Engineering, University of Leicester. His research focuses on control systems, convex optimization, and their applications in aerospace systems such as helicopters, re-entry vehicles, and UAVs. He specializes in robust control methodologies like gain scheduling, fixed-order synthesis, and Linear Matrix Inequality (LMI) frameworks. Recent work emphasizes energy-efficient control strategies for robotic arms and quadrotor UAVs, leveraging iterative learning control (ILC) and hybrid optimization algorithms. His contributions include model predictive control (MPC) designs for nonlinear systems and fault-tolerant switched control for multivariable systems. Key Technologies: Robust control, MPC, ILC, UAV control, aerospace systems Applications: Autopilot design, robotic trajectory tracking, energy optimization Dr. Prempain has developed a 2DoF Twin Rotor MIMO system for educational and research purposes, demonstrating practical applications of advanced control theories. His publications span over two decades, reflecting a sustained commitment to advancing control system methodologies.

• Trustworthy AI
• Deep Learning Theory
• Decentralised Learning
• +۱۰ مورد دیگر

Fengxiang He is a Lecturer in AI Driven Business Informatics and Financial Computing at the School of Informatics, University of Edinburgh, where he is affiliated with the Artificial Intelligence and its Applications Institute. He also holds fellowships and affiliations with the Generative AI Laboratory, Institute for Adaptive and Neural Computation, Edinburgh Future Institute, and the Edinburgh Centre for Financial Innovations. Prior to Edinburgh, he was the Founding Lead of the Trustworthy AI project at JD.com, Inc. His research centers on trustworthy AI, with core interests in deep learning theory, decentralised learning, privacy and fairness in machine learning, symmetry in machine learning, and algorithmic game theory, with applications in economics and finance. He leads the Edinburgh Ventures for Intelligence and Economics and is actively involved in shaping ethical and robust AI systems. The recent publications highlight a strong trend in theoretical and applied machine learning, particularly in decentralised learning, trustworthy AI, game theory, and financial modeling. His work bridges deep learning theory with real-world applications, focusing on safety, privacy, fairness, and robustness. The articles span top venues including ICML, NeurIPS, ICLR, IEEE TPAMI, and ACM, reflecting high impact and interdisciplinary reach. Area Chair, ICML Area Chair, NeurIPS Area Chair, UAI Area Chair, AISTATS Area Chair, ECAI Area Chair, ACML Associate Editor, IEEE Transactions on Technology and Society Fengxiang He supervises multiple PhD and MScR students and is currently recruiting for postdoctoral researchers and fully funded PhD studentships through Centres for Doctoral Training in Machine Learning Systems, Designing Responsible NLP, and AI for Biomedical Innovation. He welcomes visitors and collaborators. His team includes PhD students Johnny MyungWon Lee, Kejiang Brian Qian, Saimun Habib, Qian Zuo, and MScR student Ye Wang, along with several interns and visiting researchers. He leads the Edinburgh Ventures for Intelligence and Economics and is a key member of the Generative AI Laboratory and other interdisciplinary research centers, fostering collaboration across AI, finance, and societal impact domains.

Keisuke Okumuraمشاهده پروفایل

استاد مهمان

• Multi-Agent Systems
• Robotics
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Keisuke Okumura is a Visiting Professor at the University of Cambridge (as a JSPS overseas fellow) and a researcher at the National Institute of Advanced Industrial Science and Technology (AIST) in Japan. He holds a Ph.D. in Artificial Intelligence from Tokyo Institute of Technology (2023), supervised by Prof. Xavier Défago. His research focuses on multi-agent systems, particularly in path planning and coordination for autonomous agents, leveraging techniques from planning, search algorithms, machine learning, and distributed computing. Research Interests: His work addresses challenges in multi-agent pathfinding (MAPF), real-time trajectory optimization, and scalable solutions for large-scale robotic systems. He explores hybrid approaches combining sampling-based methods with search algorithms, as well as fault-tolerant offline planning strategies. Key areas include dynamics-aware trajectory deformation, time-independent execution frameworks, and machine learning integration for enhanced pathfinding efficiency. Publications: His recent work emphasizes advancements in algorithms like LaCAM and D4orm, targeting real-time performance and scalability. Themes include optimizing multi-robot motion, integrating diffusion models for trajectory refinement, and addressing challenges in large-scale automation. His articles reflect a blend of theoretical contributions and practical implementations in robotics and autonomous systems. Affiliations: Alongside his visiting role at Cambridge’s Department of Computer Science and Technology, Dr. Okumura contributes to AIST’s research initiatives. His collaborations span academic and industrial partners, focusing on applied robotics and intelligent systems.

Vuong Phanمشاهده پروفایل

دانشیار

• Continuous optimisation
• Dynamical systems
• Optimal control
• +۵ مورد دیگر

Dr. Vuong Phan is an Associate Professor in Operational Research at the School of Mathematical Sciences, University of Southampton. His research focuses on continuous optimization, dynamical systems, optimal control, variational inequalities, and equilibrium problems with applications in engineering, economics, and biochemistry. He has taught modules such as Operational Research II, Optimization, and Introduction to Python. He currently supervises three PhD students and has held academic positions at institutions including the University of Vienna, Vienna University of Technology, and the University of Luxembourg. His work bridges theoretical advancements in optimization with practical applications in interdisciplinary fields. Research interests include solution methods for continuous optimization, dynamical systems, optimal control, and equilibrium problems. His work emphasizes applications in engineering systems, economic models, and biochemistry, particularly in constraint-based modeling of human metabolism. Recent projects involve developing algorithms for variational inequalities and equilibrium control problems, with applications to traffic assignment and network optimization. Publications span journals like Computational Optimization and Applications, Journal of Optimization Theory and Applications, and Bioinformatics, highlighting contributions to algorithm design, convergence analysis, and interdisciplinary applications. He actively mentors students and contributes to teaching in mathematical sciences.

Mark Frenchمشاهده پروفایل

استاد

• Mathematical Systems Theory
• Adaptive and Robust Control
• Iterative Learning Control
• +۳ مورد دیگر

Professor Mark French serves as the Assistant Principal (Campus Futures) and Professor of Engineering at King’s College London. He leads strategic projects such as the King’s Interdisciplinary Science initiative and the Campus Futures program, focusing on infrastructure development and campus enhancement. Previously, he held roles as Executive Dean and Vice-Dean (Academic Planning) in the Faculty of Natural, Mathematical & Engineering Sciences. His research centers on Mathematical Systems Theory, Adaptive Control, and applications in fields like Power Electronics and Stroke Rehabilitation. French is affiliated with the Centre for Intelligent Information Processing Systems, exploring nanoscale systems and quantum information processing. His work emphasizes robust control and interdisciplinary collaboration, reflected in over 80 peer-reviewed publications. Education: Not explicitly stated in text. Research interests include designing adaptive intelligent systems through signal processing and control, with a focus on theoretical robustness and practical applications. His recent publications address state space constructions, Kalman filters, and cyber-physical systems modeling. Key contributions span adaptive control frameworks, iterative learning control, and robust stability analysis. His interdisciplinary approach bridges engineering theory with medical applications, such as functional electrical stimulation for rehabilitation.

James Moirمشاهده پروفایل

استاد

• Microbiology
• Bacterial Pathogenesis
• Environmental Microbiology
• +۳ مورد دیگر

Professor James W B Moir is a Professor of Microbiology at the Department of Biology, University of York. His research focuses on bacterial metabolism, particularly in pathogenic Neisseria species and environmental systems like agricultural soils. Key interests include denitrification, hydrogen metabolism, pesticide degradation, and microbial community dynamics. He emphasizes interdisciplinary approaches combining biochemistry, ecology, and genetics. Teaching philosophy: Advocates for experiential learning through experimental design and critical thinking, emphasizing the iterative nature of scientific inquiry. Engages students in hypothesis development and the philosophy of science. Collaborations: Works with industry, agriculture, and international partners (e.g., Vanuatu) to address global challenges in water quality and microbial contamination. Active in projects like equitable technology development for faecal contamination detection. Grants & Activities: Co-investigator on funded projects including 'The Need for Speed: Understanding the importance of different ELF3 nuclear localisation mechanisms' and 'Molecular probes to diagnose pathoadaptations in bacterial infections'. Member of university committees and editorial boards. Lab & Research Group: Leads a team exploring microbial metabolism’s role in health and environmental contexts, with a focus on translational research linking fundamental biology to global applications.

Xueqin Amy Liuمشاهده پروفایل

مدرس ارشد

• Smart Grid Data Analytics
• Renewable Energy Integration
• Energy Storage Systems
• +۳ مورد دیگر

Dr. Xueqin Amy Liu is a Senior Lecturer at Queen’s University Belfast (QUB) in the School of Electronics, Electrical Engineering and Computer Science. She holds a Ph.D. from QUB (2009) through a joint program with Zhejiang University. Her research bridges power systems and data analytics, focusing on smart grid challenges, energy storage, and renewable integration. She leads industry-driven projects like the €6.7M INTERREG SPIRE2 initiative, advancing energy storage solutions and grid stability. Dr. Liu is a Senior Member of IEEE and serves as an Associate Editor for IET Energy Conversion & Economics. She has supervised 13 PhD students and 2 postdocs, many employed in energy sectors. Notable achievements include the Best Graduate Student Poster Award (2021) and the Outstanding Associate Editor Award (2024). Education: Ph.D., Electrical and Electronic Engineering, Queen’s University Belfast (2009), joint with Zhejiang University Research Interests: Smart Grid data analytics, machine learning for grid stability, energy storage optimization, and renewable integration. Her work addresses real-world challenges with industry partners like EirGrid and SONI, developing tools for grid oscillation detection and energy market strategies. Recent Articles: Focus on oscillation mode identification, dynamic mode decomposition for grid analysis, and machine learning for electricity price forecasting. These contributions enhance grid stability and renewable integration. Awards: EPSRC Impact Acceleration Award (2022) Outstanding Associate Editor Award (IET, 2024) Best Graduate Student Poster Award (IEEE PES, 2021) Advising & Grants: Supervised 13 PhD students (e.g., Javier Lopez Lorente, Mark Rafferty) and led projects totaling millions in funding. Active in the SPIRE2 project and EU INTERREG programs. Labs/Teams: Her team develops diagnostic tools for grid oscillations and behind-the-meter analytics, collaborating with transmission system operators to enhance grid visibility and flexibility.

• Inclusive Design
• Ergonomics
• Human Factors
• +۷ مورد دیگر

Professor Patrick Langdon serves as Director of the Transport Research Institute at Edinburgh Napier University's School of Computing Engineering and the Built Environment (SEBE). He holds the position of Professor of Engineering Design, Transportation, and Inclusion and leads Ethics and Integrity initiatives for both SEBE and the School of Computing. With over 20 years of experience as an Experimental Psychologist, Langdon has established himself as a leading researcher at the intersection of cognitive science, artificial intelligence, and transportation engineering. Langdon's research spans multiple interconnected domains focused on human-centered design in transportation systems. His work centers on inclusive design principles for autonomous vehicle interfaces, human-machine interaction in complex environments, and the application of cognitive science to transportation challenges. Key research themes include designing for diverse user populations, predicting driver intent using Bayesian methods, enhancing situation awareness in multitasking scenarios, and developing ethical frameworks for human participation in technology research. His multidisciplinary approach bridges engineering, psychology, and computer science to address real-world transportation challenges. Analysis of Langdon's publication record reveals a consistent focus on human factors in automated transportation systems, with increasing emphasis on sustainable and inclusive mobility solutions. His work shows progression from foundational research in inclusive design to cutting-edge applications in autonomous vehicles and hydrogen-electric aviation. The publications demonstrate strong methodological diversity, incorporating experimental studies, theoretical frameworks, and real-world implementation projects that address both technical and social dimensions of transportation technology adoption. Langdon's research activities are supported by significant funding from diverse sources including Innovate UK, Engineering and Physical Sciences Research Council, European Commission, and industry partners. His leadership extends to major collaborative projects such as the HEART (Hydrogen Electric and Automated Regional Transportation) initiative, CAVForth automated bus service expansion, and research on inclusive kerb design for urban environments. These projects demonstrate his ability to translate theoretical research into practical transportation solutions with real-world impact. As Director of the Transport Research Institute, Langdon oversees a multidisciplinary team working at the forefront of transportation innovation. His research group focuses on the integration of cognitive science, artificial intelligence, and engineering design to create transportation systems that accommodate diverse user needs while advancing sustainable mobility solutions. The institute serves as a hub for collaboration between academia, industry, and government agencies working to shape the future of transportation.

Liucheng Guoمشاهده پروفایل

پژوهشگر ارشد

• AI software-hardware co-design
• embedded AI
• high-performance computing (HPC)
• +۷ مورد دیگر

Dr. Liucheng Guo is a Visiting Senior Research Fellow and RAEng Industrial Fellow at King’s College London, affiliated with the Department of Mathematics within the Faculty of Natural, Mathematical & Engineering Sciences. His expertise spans AI software-hardware co-design, embedded AI, and high-performance computing (HPC), with a focus on sustainable human-machine interfaces (HMI) for sectors like IoT, Automotive, Healthcare, and XR. He holds a Ph.D. from Imperial College London and an MA from Peking University. Dr. Guo has co-founded the award-winning AI startup TG0, securing seven patent groups and over £20 million in grants from InnovateUK, RAEng, and the European Innovation Council. His research emphasizes smart home, IoT, and healthcare applications, including VR mirror therapy for post-stroke rehabilitation and gesture-based control systems. He is a Senior Member of IEEE and Fellow of IET/BCS/RSA, reflecting his leadership in bridging research and industry. His grants include Smart Grants, KTP awards, and SME Leader initiatives. He actively promotes STEM through roles as RAEng Visiting Professor and public engagement ambassador.

Charlie C.L. Wangمشاهده پروفایل

استاد

• Digital Manufacturing
• Computational Design
• Additive Manufacturing
• +۳ مورد دیگر

Prof. Charlie C.L. Wang is a Professor of Smart Manufacturing at the University of Manchester, holding an EPSRC Fellowship in Field Computation Based Kernel for Vector 3D Printing. Previously, he served as Professor and Chair of Advanced Manufacturing at Delft University of Technology (2016) and held academic roles at the Chinese University of Hong Kong from 2003. He also held a visiting position at the University of Southern California (2011). His expertise spans Digital Manufacturing, Additive Manufacturing, Soft Robotics, and Geometric Computing. He holds a B.Eng. from Huazhong University of Science and Technology (1998) and a Ph.D. from HKUST (2002). Research Interests: His work focuses on computational design, additive manufacturing techniques, soft robotics kinematics, and mass personalization. He contributes to UN Sustainable Development Goals through innovations in smart manufacturing and sustainable technologies. Key areas include 3D printing optimization, bioprinting systems, and geometric computing for robotics. Key Projects: Current projects include SORO-MADE (soft robotic mannequin design), decomposition-based process planning for CNC machining, and automated knitting code generation. He leads initiatives in multi-axis additive manufacturing and field computation for 3D printing. Awards: Notable honors include the ASME CIE Excellence in Research Award (2016), Fellow of ASME (2013), and ISSMO/Springer Prize (2019). He has received nine Best Paper Awards and multiple teaching and project-oriented accolades. Grants & Activities: Active in research grants totaling over £multi-million. Engages in invited talks on topics like soft robotics and computational design, and chairs the Solid Modeling Association’s Executive Committee (2021–2024).

Yongjia Xuمشاهده پروفایل

پژوهشگر ارشد

• Optical Surface Metrology
• Structured Light Techniques
• Phase Measuring Deflectometry
• +۴ مورد دیگر

Dr. Yongjia Xu is a Research Fellow at the Department of Engineering, School of Computing and Engineering, University of Huddersfield. He actively contributes to precision metrology through structured light technologies and serves as a core researcher for two EPSRC projects: The EPSRC Future Advanced Metrology Hub (EP/P006930/1) and A Multiscale Digital Twin-Driven Smart Manufacturing System (EP/T024844/1). PhD in Mechanical Engineering (University of Huddersfield, 2019) MSc in Instrument Science and Technology (Hebei University of Technology, 2014) BEng in Control Technology and Instrument (Hebei University of Technology, 2011) His research spans optical surface metrology , fringe projection , phase measuring deflectometry , and 3D optical measurement systems . Key expertise includes calibration algorithms , error compensation , and portable metrology for industrial applications. Recent publications highlight advancements in deep-learning-enabled 3D measurement , refraction error modeling , and digital twin frameworks for smart manufacturing. His work has been cited over 409 times (Scopus h-index: 12) and 375 times (Google Scholar h-index: 11). Awarded the 2019 Chinese Government Award for Outstanding Self-financed Students Abroad, he also serves as a peer reviewer for journals like Optics Express and PhotoniX . He is a member of Optica and SPIE.

Zhengtao Dingمشاهده پروفایل

استاد

• Nonlinear Control Systems
• Adaptive Control Systems
• Distributed Optimization
• +۵ مورد دیگر

Prof. Zhengtao Ding is a Chair of Control Systems and Head of the Control, Robotics and Communication Division at the University of Manchester. He holds a BEng from Tsinghua University and MSc/PhD from the University of Manchester. His research focuses on nonlinear and adaptive control, distributed optimization, machine learning applications in robotics and power systems, and disturbance rejection in dynamic systems. He has authored/co-authored three books and over 300 papers, and serves as Editor-in-Chief for Drones and Autonomous Vehicles and Specialty Chief Editor for Frontiers in Control Engineering . Education : BEng, Tsinghua University (China) MSc in Systems and Control, University of Manchester PhD in Control Systems, University of Manchester His research interests span networked control systems, distributed optimization, and AI-driven robotics. Recent work emphasizes multi-agent systems, consensus algorithms, and fault-tolerant control. Key contributions include fixed-time control strategies and Nash equilibrium-seeking mechanisms for heterogeneous systems. Scientific awards include Fellowship at the Alan Turing Institute. He has supervised 32 students and led projects funded by EPSRC and industry collaborations. Active in editorial roles for journals like IEEE Transactions on Automatic Control and Neurocomputing . Labs/Teams: Control Systems Centre, University of Manchester.

David Mguniمشاهده پروفایل

مدرس

• Multi-Agent Systems
• Reinforcement Learning
• Game Theory
• +۳ مورد دیگر

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.

• Category Theory
• Compositional Game Theory
• Categorical Cybernetics
• +۷ مورد دیگر

Jules Hedges is a mathematics and computer science researcher affiliated with the Institute for Categorical Cybernetics and the Mathematically Structured Programming group at the University of Strathclyde. As of September 2024, he is working 90% for the CyberCat Institute and 10% for the University of Strathclyde, focusing on applying category theory across various interdisciplinary domains. Hedges completed his PhD at Queen Mary University of London in 2016 with a thesis titled "Towards compositional game theory," which established foundational work for his ongoing research program. His academic journey spans from early work on game theory to current developments in categorical cybernetics. His primary research involves applying category theory in economics, machine learning, and control theory, with a view towards what was historically called cybernetics. Hedges is particularly known for developing compositional game theory, which provides a categorical framework for understanding strategic interactions through concepts like "open games" and "selection functions." His other research interests include graphical calculi for visual representation of categorical structures, functional programming paradigms, proof theory, and the semantics of natural language. He has made significant contributions to understanding how complex systems can be built from simpler components while preserving mathematical properties. Hedges' publication record reveals a consistent trajectory toward developing compositional frameworks for complex systems, with recent work increasingly focused on categorical cybernetics—the application of category theory to systems with feedback loops and goal-directed behavior. His research often bridges abstract mathematical concepts with practical applications in economics and artificial intelligence, particularly in reinforcement learning where he has demonstrated connections between value iteration algorithms and categorical optic composition. Hedges is a signatory of the "No Free View? No Review!" pledge, indicating his strong commitment to open access publishing. He has publicly stated he will not peer review for closed access or hybrid venues, reflecting his principled stance on making academic research freely available. While specific details about his advising activities are not documented in the provided text, his extensive publication record with numerous co-authors suggests active research collaboration and likely mentorship of junior researchers. His work spans multiple institutions and involves collaborators across mathematics, computer science, and economics. Hedges is a founding member of the Institute for Categorical Cybernetics, formally incorporated in October 2023, which represents the primary focus of his current work (90% of his professional time as of September 2024). This institute serves as a hub for researchers working at the intersection of category theory, cybernetics, and their applications to complex adaptive systems, potentially supporting collaborative projects, workshops, and publications in this emerging field.