Find Academic Supervisors

Hans-Peter KubisView profile

Senior Lecturer

• Obesity physiology and psychology
• Exercise adaptation mechanisms
• Autonomic nervous system regulation
• +7 more

Dr Hans-Peter Kubis is a Senior Lecturer in Sport & Exercise Science at Bangor University's School of Psychology and Sport Science. As director of the Physical Activity and Wellbeing (PAWB) group, he conducts multidisciplinary research integrating physiological, biochemical, and psychological approaches. His work spans obesity mechanisms, obstructive sleep apnoea (OSA) pathophysiology, and exercise behavior economics through collaborations with psychology and pulmonary medicine departments. Habilitation in Physiology (Medical School Hanover) DSc (Biochemistry, University of Hannover) Dipl. Biol. Sci. (University of Göttingen) Research focuses on obesity-related appetite regulation via autonomic nervous system (ANS) balance, OSA treatment mechanisms through weight-loss interventions and effort perception analysis, and chronic fatigue modulation using evaluative conditioning in rheumatoid arthritis patients. His team develops bespoke software linking heart rate with oral reward delivery to study exercise valuation. Recent publications examine muscle architecture in rugby players , respiratory muscle training effects , and stress-craving interactions during pandemic restrictions. Current projects investigate exercise perception paradigms, effort-reward conditioning protocols, and cross-disciplinary collaborations with Ysbyty Gwynedd's pulmonary department.

• Cognitive Neuroscience
• Neuroimaging
• Emotion Processing
• +4 more

Svetlana Shinkareva is a Professor in the Department of Psychology at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. She holds a Ph.D. in quantitative psychology from the University of Illinois at Urbana-Champaign and completed post-doctoral training at Carnegie Mellon University. Her work focuses on understanding the neural mechanisms underlying affective processing and conceptual representation across sensory modalities. Dr. Shinkareva's research investigates how hedonic valence (pleasantness/unpleasantness) is represented in the brain through audiovisual stimuli, as well as how abstract concepts are encoded neurally. Her studies use neuroimaging techniques like fMRI to decode brain activity patterns associated with emotion perception, cross-modal integration, and semantic knowledge. Key areas include evaluating valence processing in naturalistic settings, exploring autism-related neural connectivity differences, and advancing multivariate pattern analysis methods for cognitive neuroscience. Her recent publications (2021–2025) reveal a focus on audiovisual integration, affective valence dynamics, and translational applications of neuroimaging. Notable trends include cross-modal studies of emotional responses, computational modeling of brain representations, and methodological advancements in FMRI decoding. Her work bridges basic cognitive science with clinical applications, such as predictive modeling for stroke patients and understanding neurodevelopmental disorders. While no scientific awards are explicitly listed, her prolific publication record demonstrates sustained impact in cognitive neuroscience. She advises no listed graduate students but collaborates on interdisciplinary projects involving psychology, computer science, and clinical research. Her lab contributes to the SC Psycholinguistic Metabase and explores translational neuroimaging applications in health and behavior.

Niels van BerkelView profile

Professor

• Artificial Intelligence
• Human-Computer Interaction
• Large Language Models
• +4 more

Niels van Berkel is a Professor in the Department of Computer Science at Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on Human-Centered Computing and AI for societal benefit, with key projects including the HERD initiative on human-robot collaboration and initiatives to enhance mental health through digital tools. He leads multiple research projects involving AI ethics, healthcare applications, and swarm robotics. His educational background includes a Ph.D. in Computer Science. Research interests span AI explainability, human-AI interaction design, and the ethical implications of AI systems in healthcare and daily life. He has supervised 1 doctoral student and contributed to over 169 research outputs across journals, conferences, and datasets. Notable contributions include work on chatbot-driven data collection, coordination in AI development teams, and swarm robotics for search & rescue. His project 'AI for the People' emphasizes technology's role in improving societal wellbeing, while collaborations with medical professionals address chronic pain management and telehealth innovations. He received the DIS 2024 Honourable Mention and has actively engaged with media to communicate research impacts, including discussions on AI ethics and robot swarms. His work bridges technical innovation with human-centric design principles, addressing challenges in explainable AI, moral agency perceptions, and sustainable urban mobility solutions.

Selmer BringsjordView profile

Professor

• Artificial Intelligence
• Cognitive Science
• Computational Logic
• +9 more

Selmer Bringsjord is a Professor of Cognitive Science, Computer Science, Logic & Philosophy, and Management & Technology at Rensselaer Polytechnic Institute (RPI). He serves as Director of the Rensselaer AI & Reasoning (RAIR) Lab and Graduate Program Director in Cognitive Science. His research spans AI, cognitive science, formal logic, and ethics, with a focus on machine ethics, computational creativity, and conscious AI. Bringsjord has authored 6 books, over 260 refereed papers, and secured $28M+ in sponsored research. He has supervised 20 PhD and 19 MS students. Notable contributions include founding machine ethics, developing Psychometric AI, and the Λ measure for cognitive consciousness. His work bridges philosophy, computer science, and robotics, with media appearances across global platforms. Education: PhD in Philosophy from Brown University (1987), BA in Philosophy from the University of Pennsylvania (1981). Administrative roles include Department Chair of Cognitive Science (2002–2016) and Director of RAIR Lab since 2002. Awards include patents, grants, and a Best Paper Award at AGI 2024. Teaching spans 40 years in formal logic and AI. Ongoing projects include ethical AI frameworks, AGI, and cognitive consciousness models.

Tamara LorenzView profile

Associate Professor

• Human-Robot Interaction
• Human-Machine Interaction
• Human Movement Coordination
• +5 more

Tamara Lorenz is an Associate Professor at the University of Cincinnati, jointly appointed across three departments: Psychology, Mechanical and Materials Engineering, and Electrical Engineering and Computation Science. She holds a MSc (Dipl.-Ing.) in Mechanical Engineering from Technische Universität München (2008) and a Ph.D. in Systemic Neurosciences from Ludwig-Maximilians University (2015), with research focused on human interaction dynamics and their application to Human-Robot Interaction (HRI), rehabilitation engineering, and human factors. Research Interests: Her work explores human-robot collaboration, movement coordination, and embodied cognition. Key areas include HRI in manufacturing, rehabilitation exoskeletons, and immersive training modalities. She employs nonlinear dynamics and behavioral modeling to enhance human-machine interaction safety and efficiency. Recent Research Support: Notable grants include development of neurophysiologically integrated exoskeletons (2016–2018), Industry 4.0/5.0 AI projects (2023–2024), and studies comparing immersive vs. traditional training effectiveness (2023). She has led projects on trustworthy AI for autonomous systems and human-centered robotics. Courses Taught: Undergraduate and graduate courses on human factors, human-robot interactions, and perception-action cycles. Examples include 'Capstone Human Factors' and 'Humans, Machines, Robots and their Interactions.' Labs/Teams: Her research integrates interdisciplinary teams from engineering, psychology, and robotics, focusing on real-world applications such as industrial automation and healthcare robotics.

Thomas WilliamsView profile

Associate Professor

• Human-Robot Interaction
• Natural Language Processing
• Artificial Intelligence
• +3 more

Thomas Williams is an Associate Professor of Computer Science at the Colorado School of Mines, where he directs the MIRRORLab (Mines Interactive Robotics Research Lab). He earned his PhD in Computer Science and Cognitive Science from Tufts University in 2017. His educational background includes: BA in Computer Science, Hamilton College, 2011 MS in Computer Science, Tufts University, 2013 PhD in Computer Science: Cognitive Science, Tufts University, 2017 Williams' research focuses on artificial intelligence for human-robot interaction, especially natural language understanding and generation in uncertain environments. His work integrates cognitive science principles from linguistics and psychology to develop context-aware robotic systems. Key areas include Human-Robot Interaction, Natural Language Processing, Robot Ethics, and Augmented Reality, with emphasis on ethical implications and social dynamics in human-robot communication. Analysis of his publications (2015-2020) reveals a clear trajectory toward context-sensitive dialogue systems that handle ambiguity, social norms, and ethical considerations. His work increasingly explores multimodal interaction through AR/VR and examines how language-capable robots influence human moral frameworks, with growing attention to gender dynamics and noncompliance behaviors in social robotics. His notable awards include: New and Future AI Educator Award, EAAI (2017, 2018) Teaching Fellowship, Tufts Graduate Institute for Teaching (2015) Doctoral Consortia participation at YRRSDS (2014), HRI (2015), and AAAI (2016) Williams teaches Human-Robot Interaction, Robot Ethics, and Computer Vision courses while leading externally funded research. His work receives support from NSF, ONR, ARL, and Early Career awards from NSF, NASA, and AFOSR, focusing on grants that advance context-aware natural language interaction in robotics. As director of the MIRRORLab, he oversees research on human-robot interaction systems that dynamically adapt to environmental, cognitive, social, and moral contexts, with applications in assistive robotics and collaborative workspaces.

James LawView profile

Researcher

• Human-Robot Collaboration
• Trustworthy Autonomous Systems
• Responsible Innovation
• +3 more

Dr. James Law serves as a Senior Experimental Officer at Sheffield Robotics and leads the Collaborative Robotics Group within the School of Computer Science at the University of Sheffield. He holds a joint appointment between the Department of Computer Science and the Advanced Manufacturing Research Centre, and serves as Director of Innovation and Knowledge Exchange at Sheffield Robotics. His work integrates academic research with industrial applications in advanced manufacturing. His research focuses on human-robot collaboration, trustworthy autonomous systems, and responsible innovation in manufacturing robotics. Law develops safety assurance frameworks and digital twinning solutions for collaborative workspaces, with particular emphasis on trust engineering, user authentication, and graphical communication systems that reduce human anxiety in robot co-working environments. His interdisciplinary approach bridges computer science, engineering psychology, and industrial implementation requirements. Law's publication portfolio demonstrates consistent advancement in collaborative robotics safety, with recent work (2022-2024) focusing on verified safety controllers, digital twin frameworks, and trust modeling in human-robot triads. His research shows progression from foundational developmental robotics (2010-2015) toward applied industrial solutions, with increasing emphasis on security, safety assurance, and human factors in manufacturing contexts. As an active science communicator, Law serves on the UK Robotics and Autonomous Systems network (UK-RAS) management board and the EPSRC Early Career Forum in Manufacturing Research. EPSRC STAMAN: Robotic skill transfer for manufacturing (Co-PI, £1M+) Horizon Europe OpenSwarm: Energy-aware swarm orchestration (Co-PI, £463k) EPSRC Security of Digital Twins in Manufacturing (Co-PI, £775k) EPSRC UKRI Trustworthy Autonomous Systems Node (Co-PI, £3M) Law leads the Collaborative Robotics Group and contributes to the Complex Systems Modelling research group, focusing on translating theoretical safety frameworks into practical industrial implementations through close collaboration with manufacturing partners and policy stakeholders.

Scott NoklebyView profile

Professor

• Robotics Engineering
• Mechatronics Systems
• Automation Technologies
• +7 more

Dr. Scott Nokleby is an Associate Dean, Academic and Professor in the Department of Automotive and Mechatronics Engineering at the University of Ontario Institute of Technology. He holds a PhD in Mechanical Engineering from the University of Victoria (2003), and has over two decades of academic leadership and research experience. His primary roles include academic administration and advancing robotics and mechatronics research. Education: PhD (Mechanical Engineering, UVic, 2003), MASc (Mechanical Engineering, UVic, 1999), BEng (Mechanical Engineering with Co-op, UVic, 1997). Research interests focus on advanced robotics topics including parallel manipulators, mobile-manipulator systems, kinematic redundancy analysis, and autonomous systems. His work emphasizes practical applications such as radiation mapping robots, perching drones, and robotic hazard management. Notable contributions include optimal design methodologies for mechanisms and control systems for human-robot interaction. Publications reflect expertise in robotics systems, mechatronics, and nuclear engineering applications. Recent work explores multi-robot task allocation, autonomous navigation, and advanced control algorithms. Awards: Fellow of ASME (2022) Fellow of CSME (2016) CSME Best Paper Award (2014) UOIT Research Excellence Award (2008) CSME I.W. Smith Award (2007) Teaches graduate courses in advanced robotics, mobile robotic systems, and mechanism design. Active in academic administration, he bridges teaching innovation with engineering education through projects like tablet computing integration in design courses. Labs/Teams: Leads robotics research initiatives focused on autonomous systems development, with collaborations spanning nuclear safety, mining automation, and aerospace applications.

Martin v. MohrenschildtView profile

Associate Professor

• Control Systems
• Signal Processing
• Simulation
• +5 more

Dr. Martin v. Mohrenschildt is an Associate Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. He holds academic positions as Undergraduate Advisor for Mechatronics and Mechatronics Coordinator. His expertise spans control systems, signal processing, hybrid systems, and immersive simulation. He earned a Dr. sc. math. ETH and Dipl. math. ETH (Swiss Federal Institute of Technology), and is a Professional Engineer (P.Eng.). Research focuses include Model Predictive Control, vibration analysis for industrial machinery, and multisensory integration in motion simulation. His Motion Simulator Laboratory features a 6-DOF simulator with advanced visualization and physiological measurement capabilities. The Embedded Systems Laboratory explores mechatronics and robotics, including alternative fuel injection systems. Teaching includes courses like CAS-748 (Time Series Analysis) and SFWR ENG 2SO3. Recent work emphasizes sensor fusion (LiDAR/camera/thermal), biofeedback systems for muscle interventions, and cognitive effects of motion cues in virtual environments. Collaborations with Dr. Shedden investigate perceptual neuroscience in immersive simulators. His lab facilities support interdisciplinary projects combining engineering, computer science, and cognitive science. Publications span 30+ years, covering hybrid systems theory, control algorithms, and applied sensor technologies. Current research directions include autonomous vehicle perception systems and aging-related changes in multisensory integration.

Fabien MoutardeView profile

Professor

• Machine Learning
• Deep Learning
• Computer Vision
• +8 more

Fabien Moutarde is a Full Professor and Director of the Center for Robotics at MINES ParisTech (PSL University, Paris, France). He holds a PhD in Physics and an Habilitation to Direct Research in Engineering Sciences. Dr. Moutarde coordinates French engineering education at ParisTech_Shanghai (SPEIT) in China. Research Areas Deep Learning & Reinforcement Learning Computer Vision for Intelligent Vehicles Collaborative Robotics Traffic Analysis & Forecasting Human Gesture Recognition Recent Article Trends His work focuses on autonomous driving using Deep Reinforcement Learning, pedestrian trajectory prediction with spatio-temporal attention, and multi-modal localization techniques combining vision with Wi-Fi. Key applications include urban traffic analysis, collaborative robotics, and end-to-end driving systems. Leadership & Teaching Co-created specialized Machine Learning courses Pioneered Deep Reinforcement Learning lectures Led French-Chinese academic coordination Former UML/Java curriculum developer Publications With 28 h-index, his research includes 30+ IEEE/ACM publications on autonomous vehicles, gesture recognition, and traffic mining. Representative conferences: CVPR, NeurIPS, IROS, ITSC.

Ben WatersonView profile

Professor

• Road Transport
• Artificial Intelligence
• Traveller Behaviour
• +3 more

Ben Waterson is a Professor in Transportation at the University of Southampton within the Faculty of Engineering and Physical Sciences. He has been a member of the Transportation Research Group since 1997, specializing in traffic control, traveler behavior, and AI applications. His research focuses on improving urban traffic systems, air quality, and safety using VR and AI. Education: BSc (1st Class Honours) in Statistics, University of Reading (1996) MSc in Operational Research (Distinction), University of Southampton (1997) PhD in Data Fusion for Real-Time Traffic Monitoring (2005) Research Interests: His work spans road transport, AI-driven traffic control, traveler behavior analysis, and VR applications. Recent projects include narrow passage driving simulations and coordinated signal control algorithms. Teaching: He teaches modules such as Transport Modelling (CENV6153), Highway and Traffic Engineering (CENV3060/CENV6171), and Transport Management and Safety (CENV6168). He supervises projects on topics like self-calibrating traffic lights and virtual reality train stations. Awards: William W. Millar Prize (2017) Grants/Projects: EPSRC-funded IVP Signals project Siemens-funded Future SCOOT Algorithm AI Development Project Labs/Groups: Member of the Transportation Research Group and Southampton Marine and Maritime Institute.

Miles HansardView profile

Senior Lecturer

• Computer Vision
• 3D Scene Reconstruction
• Robotics
• +6 more

Dr. Miles Hansard is a Senior Lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on 3D scene reconstruction, multi-view imaging, depth cameras, radar localization, and human vision. He specializes in geometric aspects of vision, image processing, and robotics applications. His teaching includes modules on Computer Graphics (for both postgraduate and undergraduate students) and Introduction to Multimedia Systems. These courses cover topics like 3D graphics algorithms, human perception of color, and multimedia system fundamentals. Dr. Hansard's work integrates sensor technologies such as time-of-flight cameras and millimeter-wave radar to improve 3D reconstruction and robotic systems. He contributes to projects like the GRIP software framework for robotic grasping and has published extensively in journals like IEEE Transactions on Image Processing and the International Journal of Neural Systems. He is affiliated with the Centre for Multimodal AI, where he collaborates on research involving sensor fusion, autonomous systems, and visual perception. His expertise bridges computer vision, robotics, and multimedia engineering.

Alberto Quattrini LiView profile

Associate Professor

• Robotic Exploration
• Multi-Agent Systems
• Underwater Robotics
• +1 more

Alberto Quattrini Li is Associate Professor of Computer Science at Dartmouth College, directing the Reality and Robotics Laboratory. His research develops autonomous systems for aquatic environments with applications in environmental monitoring and underwater archaeology. Current NSF-supported projects focus on multi-robot aquatic exploration and archaeological inspection systems. Research thrusts include surface vehicle obstacle avoidance in waterways, low-cost underwater sensing solutions, autonomous underwater construction, and laser-based air-underwater communication. Recent innovations include buoyancy-enabled manipulation systems and multi-sensor SLAM techniques for underwater operations. Quattrini Li advises six PhD students and collaborates with archaeologists through NSF grants. His laboratory prototypes systems using custom surface and underwater robotic platforms. No scientific awards are documented in the source materials.

Mohammad Naser Sabet JahromiView profile

Assistant Professor

• Explainable Artificial Intelligence
• Biometrics
• Machine Learning
• +4 more

Mohammad Naser Sabet Jahromi is an Assistant Professor at the Department of Architecture, Design and Media Technology, Aalborg University, Denmark. He is affiliated with the Visual Analysis and Perception Centre for AI Ethics, Law and Policy. His research focuses on explainable AI (XAI), biometrics, machine learning, and ethical AI applications in legal and medical domains. He actively participates in interdisciplinary projects like REPAI: Responsible AI for Value Creation (2023-2027), which explores AI ethics, computational discourse analysis, and value-driven AI systems. His educational background is not explicitly detailed in the provided text, but his research trajectory indicates strong expertise in computer science and AI systems. Key research interests include interpretable machine learning models, privacy-preserving biometric systems, and AI applications in asylum adjudication and educational assessment. Recent work emphasizes developing XAI frameworks like SIDU-TXT for NLP, verifying machine unlearning mechanisms, and automating large-classroom assessments. His projects bridge technical AI advancements with societal implications through collaborations with legal and ethical scholars. Notable contributions include datasets evaluating XAI methods in medicine and methodologies for transparent AI decision-making. He has participated in conferences such as ICPR 2024 and JURISIN 2023, showcasing interdisciplinary research impact.

Robin BloomfieldView profile

Professor

• Software and System Dependability
• Safety and Assurance Cases
• Security-Informed Safety
• +7 more

Robin Bloomfield is Professor of Software and System Dependability at City, University of London, affiliated with the College of Engineering, Design and Physical Sciences. He is also a founding member of Adelard LLP, a leading consultancy in software safety and dependability, where he continues to contribute significantly. MA in Natural Sciences, University of Cambridge, 1978 His research focuses on the dependability of computer-based systems, particularly in safety, security, and reliability . Key areas include assurance cases , security-informed safety , trustworthiness evaluation , critical infrastructure interdependencies , and policy development . He has pioneered frameworks for evaluating complex systems, especially in nuclear and transportation domains. His recent publications highlight a growing emphasis on assuring machine learning and autonomous systems , integrating security into safety arguments , and developing methodologies for interdependency analysis in critical infrastructures. These works reflect a trend toward holistic, evidence-based assurance models for emerging technologies. Fellow of the Royal Academy of Engineering (FREng), 2014 Bloomfield has held influential roles in national and international advisory bodies, including the UK Nuclear Safety Advisory Group (NUSAC), the Cabinet Office Information Assurance group, and NATO research initiatives. He has chaired major conferences such as EWICS and served on the Safecomp programme committee for many years. He is also Associate Editor-in-Chief of IEEE Security & Privacy magazine and has advised the UK Parliament on systemic risks in computer-based trading. He leads research efforts that bridge academic rigor and real-world application, particularly through his work with Adelard. His projects often involve collaboration with government agencies, industry stakeholders, and international research bodies to address challenges in critical infrastructure resilience and safety assurance.