جستجوی استاد راهنمای دانشگاهی

Oya Celiktutan is a Reader (Associate Professor) at King’s College London in the Department of Engineering, leading the Social AI & Robotics (SAIR) Lab within the Centre for Robotics Research. She holds a BSc in Electronics Engineering from Uludag University, and an MSc and PhD in Electrical and Electronics Engineering from Bogazici University, Turkey. Her doctoral work included a visiting research period at the National Institute of Applied Sciences in Lyon, France. She has held postdoctoral positions at Queen Mary University London, the University of Cambridge, and Imperial College London before joining King’s College in 2018. Her research focuses on multimodal machine learning for autonomous agents, addressing challenges in human-robot interaction, social awareness, and navigation. Key areas include human behavior analysis, generative models, and continual learning. Her work is supported by EPSRC, The Royal Society, EU Horizon, and industry partners like Toyota and NVIDIA. Notable awards include the EPSRC New Investigator Award (2021) and a Best Paper Award at IEEE Ro-Man 2022. Dr. Celiktutan’s lab explores socially assistive robotics, extended reality systems, and ethical AI. Recent projects include the LISI initiative to enable robots to learn social interactions from human-human data and the CL-HRI project advancing continual learning in human-centric robotics. Her research has led to datasets like MHHRI and RICA, and collaborations with companies like SoftBank Robotics. She teaches modules on Sensing and Perception, and Sensors and Actuators, emphasizing practical applications in robotics and mechatronics. Current research interests span socially-aware navigation, algorithmic fairness, and multimodal data fusion for robotics in healthcare and urban environments.

• Multimodal Machine Learning
• Human-Robot Interaction
• Human Behavior Analysis
• +۵ مورد دیگر

Fethiye Irmak Dogan is a Postdoctoral Research Associate at the University of Cambridge's Department of Computer Science and Technology, working in the Affective Intelligence and Robotics Laboratory. She holds a Ph.D. in Computer Science from KTH Royal Institute of Technology (2023), an M.Sc. and B.Sc. in Computer Engineering from Middle East Technical University (METU). Her research focuses on human-robot interaction, continual learning, and socially appropriate robot behaviors leveraging explainability. She has conducted robotics research at KTH's Division of Robotics, Perception and Learning and collaborated internationally, including a visiting scholar stint at Georgia Institute of Technology. Education highlights include: B.Sc., Computer Engineering, METU (2015) M.Sc., Computer Engineering, METU (2018), with research at Kovan Robotics Lab Ph.D., Computer Science, KTH (2023), with visiting research at Georgia Tech Research interests emphasize deploying autonomous robots in human environments, resolving ambiguous user instructions through explainability, and enabling socially intelligent robot behaviors. Recent work explores continual learning for context adaptation, multimodal frameworks for human-robot collaboration, and vision-language models for wellbeing assessment in children. Key projects include BT-ACTION (modular instruction understanding), GRACE (LLM-driven socially appropriate actions), and STREAK (continual learning for household tasks). Her contributions span robotics, AI ethics, and human-centered design, with a focus on real-world applications in healthcare and education.

• Human-Robot Interaction
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Professor Bing Chu is an academic at the University of Southampton, actively contributing to research in control systems, robotics, and machine learning. They are a member of the Vision, Learning and Control Centre for Internet of Things and Pervasive Systems and the Centre for Robotics, focusing on interdisciplinary approaches that combine control theory with data-driven methodologies. Current research interests include: Iterative learning control Human-robot interaction Wind farm power optimization Robot behavior modeling Control system architectures Collaborative learning systems Recent publications highlight trends in data-driven control systems, human-robot interaction datasets, and optimization techniques for both continuous-time systems and wind energy applications. Professor Chu supervises multiple PhD students across robotics and electronic engineering, including Balint Gucsi, Haonan Shen, and Aleksander Wolski, while leading projects funded by Zhengzhou University and the Royal Society.

• Control Systems
• Robotics
• Machine Learning
• +۳ مورد دیگر

Petar Kormushev is a Senior Lecturer (Associate Professor) in Robotics at the Dyson School of Design Engineering, Imperial College London, and the founder/director of the Robot Intelligence Lab. He holds a PhD in Computational Intelligence from Tokyo Institute of Technology. His research focuses on robotics and machine learning, particularly reinforcement learning for autonomous robots. Key projects include the WALK-MAN humanoid robot for disaster response and contributions to the PANDORA and STIFF-FLOP EU projects. He has received the 2013 John Atanasoff Award for scientific excellence in ICT. His lab develops machine learning algorithms applied to humanoid robots like COMAN and iCub, with interests in robot learning, compliant control, and autonomous systems. He has supervised numerous PhD students and led research teams at IIT and Imperial College.

• Robotics
• Machine Learning
• Reinforcement Learning
• +۴ مورد دیگر

Professor Kais Atallah is a Professor in Electrical Engineering at the University of Sheffield's School of Electrical and Electronic Engineering, with a secondary role as a Lecturer in Aerospace Engineering. He joined the University in 1989 as a PhD student and became academic staff in 2000. His research focuses on electromechanical energy conversion, magnetic torque systems, and applications in renewable energy, electric vehicles, and aerospace actuation. He is a member of the Centre for Research Into Electrical Energy Storage and Applications (CREESA) and leads the Electrical Machines and Drives Research Group. His research interests include novel drivetrain designs for wind turbines, flywheel systems, and fault-tolerant machine systems for safety-critical aerospace applications. He has advised multiple PhD students and contributed to over 150 publications, with recent work emphasizing magnetic gear design, high-performance ferrite machines, and modular multilevel converters. His articles explore themes like high-efficiency power transmission, magnetic gear optimization for wind turbines, and traction machine control systems. Collaborations include work on subsea ROV propulsion and flywheel energy storage. He oversees research labs focused on advanced electrical machine design and energy storage solutions.

• Electromechanical Energy Conversion
• Magnetic Torque/Force Transmission Systems
• Renewable Energy Systems
• +۴ مورد دیگر

Dr. Edward Johns is an Associate Professor in the Department of Computing at Imperial College London and Director of the Robot Learning Lab. He specializes in robot learning, focusing on enabling robots to learn tasks through imitation and language-based reasoning. His expertise spans robotics, machine learning, and computer vision, with a particular emphasis on manipulation tasks requiring physical interaction with objects. He holds a BA and MEng from the University of Cambridge and a PhD from Imperial College London. Prior to his current role, he was a postdoc at UCL, a founding member of the Dyson Robotics Lab, and led the robot manipulation team there. He also served as Head of Robot Learning at Dyson (part-time, 2021–2022). His research has produced state-of-the-art capabilities such as one-shot imitation learning and language-driven task execution. Key areas of interest include sim-to-real transfer, self-supervised learning, and adaptive robotic systems. His work bridges foundational AI research with practical robotics applications, emphasizing real-world deployment and human-robot collaboration. Dr. Johns has published over 60 peer-reviewed papers, with over 4,000 citations, and has received prestigious awards including the UK-RAS Early Career Award (2023) and the Best Conference Paper Award at ICRA (2024). He is also actively involved in industry through advisory roles for robotics and AI startups. His teaching includes graduate courses on reinforcement learning and robot learning, and he collaborates extensively with labs such as the Robotics Forum and the Artificial Intelligence Network at Imperial College.

• Robot Learning
• Robot Manipulation
• Artificial Intelligence
• +۴ مورد دیگر

Professor Sara Bernardini is a leading academic in Artificial Intelligence at the University of Oxford's Department of Computer Science, where she holds a joint appointment as a Tutorial Fellow at Mansfield College. Her research specializes in decision-making for autonomous systems, automated planning, and robotics, with applications in extreme environments like space missions, nuclear decommissioning, and offshore energy. She bridges theoretical AI with real-world challenges through projects funded by Innovate UK, EPSRC, NERC, and the Alan Turing Institute. Her research interests span: Autonomous Systems : Developing agents that support humans in complex cognitive tasks. Automated Planning : Algorithms for goal recognition, pathfinding, and multi-agent coordination. Robotics : Solutions for subterranean exploration, offshore wind farms, and UAV operations. AI Safety : Risk-aware autonomous systems and interpretable decision-making. Bernardini's publications emphasize algorithmic robustness in path planning, multi-agent coordination , and real-world AI deployments . Recent work explores goal legibility in uncertain environments, energy-efficient robotics, and AI education tools. Her 65+ papers in top venues (e.g., AIJ, JAIR, ICAPS) show a trend toward safety-critical applications and human-AI collaboration. Awards & Leadership: ICAPS-2020 Best Paper Honorable Mention Executive Council Member, Association for the Advancement of Artificial Intelligence (AAAI) Program Chair, International Conference on Automated Planning and Scheduling (ICAPS 2024) Associate Editor, Artificial Intelligence Journal She leads interdisciplinary teams for projects like autonomous offshore wind farm maintenance and modular robots for extreme environments. As Principal Scientist at the UK National Oceanography Centre, she advanced marine robotics. She mentors PhD candidates and collaborates globally (e.g., NASA Ames, MIT).

• Artificial Intelligence
• Automated Planning
• Robotics
• +۴ مورد دیگر

Victor Becerra is a Professor of Power Systems Engineering at the University of Portsmouth, affiliated with the School of Electrical and Mechanical Engineering within the Faculty of Technology. He is also associated with the Centre for Environmental and Renewable Energy Solutions and the Agile Centre For Equitable Sustainability. His research focuses on advanced control systems, renewable energy integration, smart grids, battery management, and nuclear power plant control. He actively supervises PhD students in topics like optimal control of battery systems and microgrid optimization. His academic work spans over 193 publications, with recent contributions emphasizing techno-economic analysis of green hydrogen, modular energy storage solutions, and optimal control strategies for batteries and nuclear reactors. His research often addresses real-world applications such as grid flexibility at ports, peer-to-peer energy trading platforms, and drone-based inspection of offshore wind turbines. Becerra's expertise includes developing control algorithms for marine structures, sodium-cooled reactors, and unmanned aerial vehicles. He has contributed to books on solar energy engineering and applications, highlighting interdisciplinary approaches to sustainability challenges. His work integrates theoretical models with practical implementations, such as energy management systems for compressed air and advanced BMS for drones. Key themes in his research include renewable energy systems optimization, fault-tolerant control mechanisms, and adaptive control strategies for dynamic environments. He collaborates internationally, addressing global energy challenges through innovative control methodologies and sustainable technologies.

• Power Systems Engineering
• Renewable Energy
• Control Systems
• +۴ مورد دیگر

Sonia Antoranz Contera is a Full Professor of Biological Physics at the University of Oxford and Associate Head of the Physics Department. Her research bridges physics, nanotechnology, and biology, focusing on the interplay of mechanics, chemistry, and electricity in biological systems. She leads projects on neural interfaces, sustainable architecture inspired by biological principles, and nanomedicine. Notable collaborations include work with Amanda Levete Architects on bioinspired materials and the EU-funded ZEBAI initiative for carbon-neutral buildings. Her book Nano Comes to Life (Princeton University Press) explores nanotechnology's impact on biology and medicine. She also engages in public discourse through columns in El Pais and academic lectures on the history of physics and ethics in science. Education: PhD in Physics (Osaka University), with postdoctoral work across global institutions. Research Interests: Biomechanics of growth, AFM of biological systems, neuromorphic computing, and sustainable materials. Funding: John Fell Fund, Moore Foundation, EU Horizon Programme (ZEBAI), UKRI (BuildAir). Her lab's recent work includes AI-driven cement microstructure synthesis, PDMS mechanical aging studies, and anaesthesia mechanisms linked to neuronal viscoelasticity. She advises on policy and innovation, advocating for ethical, equitable scientific progress.

• Biological Physics
• Nanotechnology
• Biomechanics
• +۳ مورد دیگر

Professor Michael Ramage is a Senior Lecturer in the Department of Architecture at Cambridge University , where he directs the Centre for Natural Material Innovation . He is also a fellow of Sidney Sussex College and co-founder of Light Earth Designs . His academic background includes architecture studies at MIT and professional experience at Conzett Bronzini Gartmann in Switzerland. His research focuses on low-energy structural materials , natural material innovation , and sustainable housing in developing regions, with particular emphasis on engineered timber and bamboo . The 15 most recent publications highlight trends in modular timber construction , 3D printed earthen materials , and climate action in the building sector . Key subfields include circular economy , resource optimization , structural testing , and behavioral impacts on decarbonization . He has secured research funding from the Leverhulme Trust , Engineering and Physical Sciences Research Council (EPSRC) , Royal Society , and British Academy .

• Sustainable Architecture
• Structural Engineering
• Natural Material Innovation
• +۳ مورد دیگر

Dr. Anwar Ali is a Lecturer in the Department of Electronic and Electrical Engineering at Swansea University's Bay Campus, affiliated with the School of Aerospace, Civil, Electrical and Mechanical Engineering. He holds an M.S. in Electronic Engineering (2010) and a Ph.D. in Electronic and Communication Engineering (2014) from Politecnico di Torino, Italy. His research focuses on: Power electronic converters and conditioning systems Embedded systems for aerospace applications Analog/mixed-signal circuit design Satellite technologies including power management Attitude determination and control systems Thermal modeling of aerospace systems Dr. Ali has authored over 50 publications with recent works concentrated in satellite power systems, thermal analysis of spacecraft, machine learning applications in healthcare/robotics, and energy harvesting techniques. His research demonstrates consistent innovation in small satellite technologies and cross-disciplinary applications of electrical engineering principles. He currently supervises PhD projects on: Wireless power transfer for implantable medical devices Integrated power and attitude control optimization for small spacecraft and teaches modules including Analogue Design, Software Engineering, Embedded System Design, and Integrated Circuit Design.

• Power Electronic Converters
• Embedded Systems
• Analog and Mixed-Signal Circuit Design
• +۵ مورد دیگر

Dr Michael Boemo is an Assistant Professor at the University of Cambridge, holding dual appointments in the Department of Pathology and Department of Genetics. He leads research at the intersection of computational biology, DNA replication, and cancer genomics, developing machine learning tools to analyze replication stress and genomic instability. Academic Background: BA in Mathematics (Rutgers University), PhD in Physics (University of Oxford) Research Focus: Genomic instability in cancer, DNA replication/repair defects, computational modeling using machine learning and high-performance simulations Teaching: Lectures in Natural Sciences Tripos (mathematical biology, genetics, systems biology), module organizer for cancer biology and biological modeling His research group leverages nanopore sequencing and AI to map replication fork dynamics, revealing how stalled forks generate mutations in cancer cells and pathogens. Recent work examines extrachromosomal DNA replication vulnerabilities and transcription-replication conflicts. Dr Boemo collaborates across computational biology and cancer research domains, with publications spanning journals like Nature Methods, Cell, and PLoS Computational Biology. His lab develops tools such as DNAscent for replication fork analysis and explores therapeutic targeting of replication stress.

• Genomic Instability
• DNA Replication and Repair
• Computational Biology
• +۵ مورد دیگر

Roderich Gross is a Senior Lecturer in the Department of Automatic Control and Systems Engineering at the University of Sheffield. He is also a Visiting Scientist at CSAIL, MIT, and leads the Enabling Technologies theme at Sheffield Robotics. His academic journey includes a Ph.D. in engineering science from Université libre de Bruxelles (2007), followed by postdoctoral fellowships as a JSPS Fellow (Tokyo Institute of Technology), Research Associate (University of Bristol), and Marie Curie Fellow (EPFL & Unilever). Research Interests : Swarm robotics, self-reconfigurable robots, multi-robot coordination, robotics software/tools (human-robot interaction interfaces, formal design tools), machine learning for behavior inference (Turing Learning, GANs), autonomous systems, natural computing (swarm intelligence, evolutionary algorithms). Scientific Contributions : Inventor of Turing Learning, a machine learning method for behavior inference. Key work includes swarm coordination, self-assembly, fault-tolerant quadcopters, energy-efficient drone delivery, and infrared-based swarm communication. Scientific Recognition : Held prestigious fellowships including JSPS and Marie Curie, and served as Associate Editor for leading robotics journals (IEEE Robotics and Automation Letters, Swarm Intelligence) and conference roles (General Chair DARS 2016, Program Co-Chair GECCO 2018). Grants : Principal Investigator on Horizon Europe OpenSwarm (£463,699), EPSRC Core Capital (£104,196), DSTL Multi Robot Systems (£98,781), and industry-funded modular robotics projects. Labs : Affiliated with the Natural Robotics Lab at the University of Sheffield.

• Swarm Robotics
• Modular Robotics
• Computational Intelligence
• +۵ مورد دیگر

Fahad Khan is a Researcher at Cranfield University's Centre for Robotics and Assembly, part of the Aerospace department. He holds an MSc in Robotics Engineering (Cranfield University) and a B.Tech in Mechatronics (NMIMS University). Currently employed full-time as a Research Assistant and part-time PhD candidate, his work focuses on intelligent robotics systems, human-robot collaboration (HRC), and adaptive automation technologies. Education: MSc Robotics Engineering, Cranfield University (UK) B.Tech Mechatronics Engineering, NMIMS University (India) Current Projects: Smart Cobotics (ISCF) project: Developing adaptive HRC systems using physiological and contextual data. EPSRC-funded research on facial emotion recognition for manufacturing robots. Research interests include IoT integration, motion control, and machine learning applications in robotics. He has published on HRC gesture design, emotion recognition systems, and ROS 2-based industrial frameworks. Awards: None explicitly listed. Grants: Supported by Engineering and Physical Sciences Research Council (EPSRC) through the Made Smarter Innovation project. Technical expertise spans ROS, OpenCV, MATLAB, and PLC programming. His LinkedIn and GitHub profiles showcase contributions to robotic systems and machine learning projects.

• Intelligent Automation
• Human-Robot Collaboration
• Machine Learning
• +۳ مورد دیگر

Professor Amanda Prorok leads the Prorok Lab at the University of Cambridge's Department of Computer Science and Technology, focusing on multi-agent and multi-robot systems. Her work integrates machine learning, planning, and control to coordinate intelligent agents in shared environments, with applications in transport, environmental monitoring, and search-and-rescue. She is a Fellow of Pembroke College and holds editorial roles at IEEE Robotics and Automation Letters and Autonomous Robots. Education: Ph.D., EPFL (Switzerland); Postdoctoral Research, University of Pennsylvania (USA). Research Interests: The lab pioneers methods like differentiable communication between learning agents and develops decentralized algorithms for navigation, coverage, and coordination. Key themes include neural diversity in collective learning, resilient swarm systems, and environment-aware control. Notable Achievements: ERC Starting Grant, Amazon Research Award, EPSRC New Investigator Award ABB Prize for Best Thesis in Computer Science (EPFL) Teaching: Leads Computing for Collective Intelligence (MPhil/Part III) modules. Lab & Infrastructure: The Prorok Lab operates the Cambridge RoboMaster platform and develops testbeds for connected vehicles and robot swarms. Recent work emphasizes scalable reinforcement learning and graph neural networks for decentralized decision-making.

• Multi-Agent Systems
• Robotics
• Collective Intelligence
• +۶ مورد دیگر