جست‌وجوی استاد و پژوهشگر

Paul Goulartمشاهده پروفایل

استاد

• Robust and high-speed optimisation
• Control engineering
• Convex optimization
• +۴ مورد دیگر

Professor Paul Goulart is a full Professor of Engineering Science at the University of Oxford and Tutorial Fellow at St Edmund Hall, positions he has held since 2014. He leads research and teaching in robust optimization, control systems, and high-speed numerical methods, with applications spanning fluid flows, traffic networks, and economics. Education SB & MSc, Aeronautics and Astronautics – Massachusetts Institute of Technology (MIT) PhD, Control Engineering – University of Cambridge (Gates Scholar, 2007) Research Interests Professor Goulart’s work lies at the intersection of control engineering and optimization . His core expertise includes: Robust and high-speed convex optimization Model predictive control (MPC) and control barrier functions Neural-network-based control and system identification Optimization over traffic and economic networks Real-time and embedded optimization solvers These interests are reflected in prolific publication output and active supervision of doctoral researchers. Publications & Trends From 2020 to 2025 Professor Goulart has co-authored more than thirty papers. A dominant theme is the development of fast, reliable algorithms for conic optimization and robust control , often leveraging machine-learning techniques to enhance scalability and real-time performance. Recent works emphasize safety certificates, GPU-accelerated solvers, and neural-network controllers for uncertain systems. Awards & Honors Gates Cambridge Scholar (2003) Advising & Grants Professor Goulart actively seeks DPhil students in control engineering and optimization . He leads the Control Group within the Department of Engineering Science and has been involved in multiple industrially funded projects, although specific grant identifiers are not provided in the supplied text. Laboratory & Teams He is a member of the Control Group , Department of Engineering Science, University of Oxford, and serves as Secretary to the Governing Body of St Edmund Hall (Michaelmas Term 2024).

Mahroo Eftekhariمشاهده پروفایل

استاد

• Building Energy Management Systems
• HVAC Systems Optimization
• Thermal Comfort Modeling
• +۵ مورد دیگر

Professor Mahroo Eftekhari is a Professor in Building Services Engineering at Loughborough University, leading the Low Energy Building Services Engineering MSc programme. Her research focuses on energy-efficient building systems, thermal comfort, HVAC optimization, and renewable integration. She has pioneered control systems for airports and buildings, including MPC-based strategies to reduce energy use while enhancing occupant well-being. Notable contributions include the development of BISPA (Building & Industrial Services Pipework Academy), a national center for BIM and pipework training. Education: Holds qualifications including CEng (Chartered Engineer), DPhil (Doctor of Philosophy), FCIBSE (Fellow of Chartered Institution of Building Services Engineers), and SFHEA (Senior Fellow of the Higher Education Academy). Her academic career is marked by collaborations with Tata Steel, Mitsubishi R&D, and Vexo, yielding applied research in sustainable building technologies. Research Interests: Indoor Air Quality, Thermal Comfort Modeling, Zero Energy Buildings, Digital Twins, and Advanced Control Systems. She has developed innovative solutions like AI-driven thermal management for Building Energy Management Systems (BEMS) and interfaces to synchronize airport operations with energy systems. Awards & Grants: Secured funding from diverse bodies for projects such as adaptive thermal comfort models, BISPA infrastructure, and energy-efficient HVAC strategies. Her work emphasizes practical applications, including reducing CO₂ emissions via airport terminal optimization and improving renewable energy use in buildings. Lab & Teams: Leads the Building Energy Research Group, managing projects in closed-loop heating systems, IEQ monitoring, and hydronic system efficiency. The Civil Engineering labs house interactive BIM rigs launched with institutional and industry support.

• Sustainable Development
• Low-Carbon Technologies
• Energy and Building Technology
• +۱۳ مورد دیگر

Dr. Behrang Vand Alimohammadisagvand is a Lecturer at the School of Computing, Engineering and the Built Environment, Edinburgh Napier University. His research focuses on sustainable energy systems, building performance, and smart energy management, with strong international collaboration across Iran, China, Finland, and the UK. B.Sc. and M.Sc. in Mechanical and Energy Engineering Ph.D. in Energy and Building Technology His research interests include sustainable development, low-carbon technologies, energy policy, thermal comfort in buildings, and energy management systems at multiple scales. He actively investigates model predictive control (MPC), demand response, energy sharing in communities, and low-temperature heating networks. His work bridges theoretical modeling and practical implementation in real-world buildings. Dr. Vand's recent publications highlight trends in integrated energy management , smart grid optimization , and decarbonization of building heating systems . His articles emphasize mathematical modeling, simulation, and control strategies to improve energy efficiency and reduce carbon emissions in the built environment. He supervises doctoral students working on smart IoT systems and low-temperature heat networks. His research is supported by funders such as the Academy of Finland and Energy Technology Partnership. Active supervisor for PhD project on smart IoT systems with real-time deep learning (since 2022) Director of Studies for PhD on heating Scottish public buildings with low-temperature networks (2018–2024) He is affiliated with the Institute for Sustainable Construction and contributes to the research theme Culture and Communities . His work aligns with global efforts toward net-zero emissions and sustainable urban development.

• Formal Verification and Control
• Safety-Critical Systems
• Machine Learning
• +۵ مورد دیگر

Dr. Yulong Gao is an Assistant Professor in the Department of Electrical and Electronic Engineering at Imperial College London, affiliated with the Control and Power Research Group. He holds a B.E. in Automation from Beijing Institute of Technology (2013), M.E. in Control Science & Engineering (2016), and a joint Ph.D. in Electrical Engineering from KTH Royal Institute of Technology and Nanyang Technological University (2021). He has held postdoctoral positions at Oxford University and KTH. His research focuses on formal verification and control, machine learning, and their applications to safety-critical systems, including autonomous systems and control synthesis under uncertainty. His work emphasizes robust control strategies, data-driven optimization, and formal methods to ensure safety in dynamic systems. Recent publications address challenges in autonomous vehicle motion planning, risk-aware Bayesian neural networks, and adaptive task planning using temporal logic. He has contributed to stochastic modeling, distributed MPC, and resilient control under cyber-physical threats. Research affiliations include the Control and Power Research Group at Imperial College London, leveraging interdisciplinary collaboration to advance theoretical and applied control systems research.

Abigail Hathwayمشاهده پروفایل

مدرس ارشد

• Building Physics
• Indoor Airflow Dynamics
• Sustainable Building Systems
• +۷ مورد دیگر

Dr Abigail Hathway is a Senior Lecturer at the School of Mechanical, Aerospace and Civil Engineering , University of Sheffield, specializing in Building Physics and Indoor Airflow Dynamics . Her work bridges energy efficiency with occupant health , focusing on human-building interactions. Education: PhD in CFD modeling of bioaerosols (University of Leeds) Research interests include: Computational Fluid Dynamics (CFD) for indoor environments Human activity impacts on airflow and infection risk Sustainable drainage systems (SuDS) for urban climate mitigation Smart building controls integrating machine learning Her recent publications address ventilation strategies and airborne transmission mitigation across healthcare, hospitality, and urban settings. She leads research on SuDS microclimate impacts and battery storage optimization in buildings. Current PhD opportunities in her group focus on: Urban stormwater-climate interactions Low-energy ventilation systems Occupant-driven building performance

William Heathمشاهده پروفایل

استاد

• Feedback Control Theory
• Nonlinear Control Systems
• Actuator Nonlinearities
• +۴ مورد دیگر

William Heath is a Professor and Head of the School of Computer Science and Engineering at Bangor University. He holds the position of Chair of the UKACC from 2024 to 2027. His research focuses on feedback control theory, particularly addressing actuator nonlinearities such as saturation, rate constraints, backlash, and hysteresis. He employs multiplier theory within absolute stability frameworks to analyze model predictive control and antiwindup strategies. His research interests include nonlinear control systems design, stability criteria for Lur’e systems, and discrete-time extensions of classical control methodologies. He has contributed to foundational work on O’Shea-Zames-Falb multipliers and their applications in robust control. Key collaborations involve international researchers in control systems and applications, though specific partnerships are not detailed. His work bridges theoretical advancements with practical implementations, such as in biomedical BCIs and industrial systems like wind turbines and diesel engines. No awards or grants are explicitly listed, but his extensive publication record (105+ outputs) reflects sustained academic engagement. As Head of School, he leads a team advancing interdisciplinary research in computer science and engineering.

• Autonomous Systems
• Artificial Intelligence
• Computer Vision
• +۷ مورد دیگر

Dr. Yang Xing is a Senior Lecturer in Applied Artificial Intelligence for Engineering at Cranfield University's Centre for Autonomous and Cyberphysical Systems, where he also directs the HUMAX Lab focused on human-centered autonomous vehicle validation. He holds a PhD from Cranfield University (2018) and an MSc with Distinction in Control Systems from the University of Sheffield (2014). Previously, he was a Research Associate at the University of Oxford (2020-2021) and Research Fellow at Nanyang Technological University (2019-2020). His research centers on human-autonomy collaboration frameworks with four key pillars: Cognitive autonomous systems using trustworthy AI Computer vision for human behavior/intention modeling Multimodal foundation models for autonomous driving Deep learning for sustainable transportation systems His recent publications (2022-2025) demonstrate strong trends in AI-driven transportation research : 40% focus on trajectory prediction and behavior modeling, 30% on computer vision applications, 20% on human-AI collaboration frameworks, and 10% on energy optimization. Key thematic evolutions include increased use of transformer architectures, graph neural networks for interaction modeling, and simulation-to-real transfer learning. Awards and Honors: IEEE Outstanding Associate Editor Award (TNNLS 2023-2024) Best Paper Award, China National Intelligence Technology Conference 2019 IEEE Outstanding Service Award, Smart World Congress 2023 Best Workshop Paper, IEEE IV 2018 He currently advises PhD student Isa Ismail and has secured funding from the Royal Society, EPSRC, DSTL, SAAB, QinetiQ, and Thales. As lab director of HUMAX, he leads projects on human-AI teaming for autonomous systems.

Neil Simsمشاهده پروفایل

استاد

• Smart Fluids
• Modeling and Design of Smart Fluid Dampers
• Control and Stability of Smart Fluid Dampers
• +۶ مورد دیگر

Professor Neil Sims holds a faculty position in the Department of Mechanical Engineering at the University of Sheffield . His research spans smart fluid dampers , machining vibration control , and uncertainty propagation in structural systems. He has pioneered methods for chatter suppression in high-speed machining and energy harvesting system design. Educational Background: First Degree in Mechanical Engineering, University of Sheffield PhD in Mechanical Engineering, University of Sheffield Research Focus includes smart materials (magnetorheological/electrorheological fluids), nonlinear dynamics , and adaptive control systems . His work integrates computational modeling , experimental validation , and machine learning for industrial applications. Recent Publications highlight advancements in inerter-based vibration absorbers , machine learning for machining optimization , and actuator saturation challenges . These studies reflect his interdisciplinary approach to structural stability and manufacturing efficiency. Scientific Recognition: EPSRC Advanced Research Fellowship (2003-2008) on machining chatter Collaborative Impact extends to seismic isolation , robotic machining , and energy harvesting from human motion and machinery. His editorial work in the Proceedings of the Institution of Mechanical Engineers, Part B (2021) underscores leadership in machining science.

• 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.

Pouria Sarhadiمشاهده پروفایل

مدرس ارشد

• Control Theory
• Autonomous Vehicles
• Adaptive Control
• +۳ مورد دیگر

Pouria Sarhadi is a Senior Lecturer at the University of Hertfordshire's School of Physics, Engineering & Computer Science. He has held roles as a Research Fellow at Queen's University Belfast (2021-2022) and the University of Surrey (2019-2021), and served as an Adjunct Professor at Babol Noshirvani University of Technology (2016-2018). He is a Chartered Engineer (CEng), Member of the IET, and recognized as UK 'Global Talent' by UKRI. His research focuses on control theory applications, adaptive control, autonomous vehicles, and systems engineering. He has contributed to over 50 peer-reviewed publications and led projects such as HertsLynx CAM On-Demand (UKRI-funded). He is actively involved in editorial roles for Applied Ocean Research and advises on IFAC Technical Committees. Research interests include: Autonomous systems (ground, marine, aerial) Machine learning in control systems Adaptive control and system identification Autonomous vehicle safety and navigation Key awards include CEng, MIET, and UK Global Talent recognition. He has advised on UKRI and Horizon Europe projects and consults for industries. Current projects include hybrid navigation safety simulators and cooperative control of multi-AUV systems.

Yuchao Liuمشاهده پروفایل

مدرس

• Power Converters
• Model Predictive Control
• Motor Drives
• +۵ مورد دیگر

Dr. Yuchao Liu is a Lecturer in the Department of Mathematics, Physics & Electrical Engineering at Northumbria University since January 2023. Previously, she held academic roles at Harbin Engineering University (2019–2021) and the University of Nottingham (2021–2022 as a Research Fellow in the PEMC Group). She earned her Ph.D. in Electrical Engineering from Harbin Institute of Technology (2019) and was a visiting scholar at Imperial College London (2017–2018). Her research focuses on power electronics, renewable energy integration, and control systems. Key areas include power converter design, model predictive control (MPC), multi-terminal DC transmission (MTDC), and motor drives. She has contributed to EU Horizon 2020 projects like the IMOTHEP initiative. Her publications span topics such as battery state-of-health estimation, DC-DC converter optimization, and grid stability analysis. She actively seeks PhD students and has collaborated internationally on projects addressing sustainable energy challenges. Her work aligns with UN Sustainable Development Goals, particularly in enabling clean energy transitions and advancing sustainable infrastructure.

• Economic Model Predictive Control
• Stability of Nonlinear Systems
• Chemical Reaction Networks Theory
• +۵ مورد دیگر

David Angeli is a Professor of Nonlinear Network Dynamics in the Department of Electrical and Electronic Engineering at Imperial College London's Faculty of Engineering. Affiliations: Control and Power Research Group Energy Futures Lab His research focuses on Economic Model Predictive Control, Stability of Nonlinear Systems, Chemical Reaction Networks Theory, Systems Biology, and Control of Smart Grids. He develops theoretical frameworks for networked systems with applications in energy infrastructure and biological processes, emphasizing robustness and scalability in distributed settings. Recent publications (2023-2025) highlight work on distributed control algorithms, Lyapunov-based stability certification, and small-gain theorems for interconnected systems. His research bridges control theory, game theory, and machine learning to solve problems in cyber-physical systems, particularly in resilient consensus protocols and economic optimization for heterogeneous networks. As a core member of the Control and Power Research Group and Energy Futures Lab, he contributes to sustainable energy solutions and advanced control methodologies for complex networked systems.

• Model Predictive Control (MPC)
• Optimization Techniques
• Aerospace Engineering
• +۳ مورد دیگر

Eric Kerrigan is a Professor of Control and Optimization at Imperial College London's Department of Electrical and Electronic Engineering, part of the Faculty of Engineering. He holds a joint appointment in the Department of Aeronautics. His research focuses on Model Predictive Control (MPC), numerical optimization techniques, and their applications in aerospace, renewable energy, and information systems. Key projects include developing real-time optimization algorithms for embedded systems, co-design frameworks for closed-loop systems, and drag reduction in aerodynamics. He has supervised over 30 PhD students and post-doctoral researchers, many of whom have secured academic positions. Education: PhD in Control Engineering from the University of Cambridge and BSc in Electrical Engineering from the University of Cape Town. Research interests emphasize robust control methods, dynamic optimization, and interdisciplinary applications. Notable contributions include the ICLOCS (Imperial College Optimal Control Software) toolbox and frameworks for energy-efficient UAV communication networks. His work is funded by EPSRC, the European Commission, and industry partners like Siemens and ESA. Funding and collaborations include grants from the Royal Academy of Engineering and Royal Society, with consulting roles in industrial control systems. Editorial roles include Associate Editor for IEEE Transactions on Automatic Control and former Senior Editor for IEEE Transactions on Control Systems Technology . Labs and affiliations include the Control and Power Research Group, Energy Futures Lab, and Space Lab at Imperial College.

Pantelis Sopasakisمشاهده پروفایل

مدرس

• Model Predictive Control
• Embedded Numerical Optimization
• Stochastic Optimal Control
• +۵ مورد دیگر

Dr. Pantelis Sopasakis is a Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast, Northern Ireland. His research focuses on developing efficient numerical optimization algorithms and model predictive control (MPC) methodologies for uncertain systems, with applications in autonomous vehicles, smart infrastructure networks, and advanced manufacturing. He leads projects on embedded optimization solvers, GPU-accelerated MPC, and stochastic control for systems like water networks and microgrids. His work emphasizes real-time implementation and safety-critical applications in robotics and energy systems. He teaches postgraduate and undergraduate courses in control theory and signals, and is actively involved in supervising PhD students in areas like parallel algorithms and MPC for uncertain systems. Key achievements include the development of the Open-Source Optimization Engine , widely used for embedded MPC. Research interests span distributed embedded intelligence, intelligent uncertain-aware MPC, and biomedical applications of control systems. He collaborates internationally on projects involving risk-averse control, multi-agent systems, and circular economy applications. His interdisciplinary work bridges optimization theory, robotics, and energy systems, with a focus on scalable and real-time solutions. Teaching includes modules on control systems fundamentals and advanced MPC concepts, supported by his textbook Control Systems: An Introduction . Dr. Sopasakis has contributed to over 50 publications, with recent work on conformal prediction for stochastic control, distributed collision avoidance, and thermodynamical material networks. He participates in conferences and editorial activities, and has organized events like the 2025 IEEE UK and Ireland Robotics Conference. His research group is part of the Energy, Power, and Intelligent Systems and Control clusters at Queen's.

• 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.