جست‌وجوی استاد و پژوهشگر

Lars Göran Kecklundمشاهده پروفایل

استاد

• Sleep Research
• Stress and Recovery
• Shift Work
• +۳ مورد دیگر

Lars Göran Kecklund is a Professor at Stockholm University's Department of Psychology, specializing in sleep regulation, shift work effects, and fatigue-related safety . Accredited sleep specialist since 2009, he leads the Division for Sleep and Alertness Research and contributes to the Stressforskningsinstitutet. His research spans sleep quality in transportation sectors, work-time control mechanisms, and age-related sleep variability. PhD in Psychology (1997) Accredited sleep specialist (2009) His research explores how stress disrupts recovery , with projects like SLEMEG (MEG neurophysiology of sleep loss) and population studies on stress attribution. Recent articles address sleep estimation algorithms, retirement's impact on circadian rhythms, and shift work scheduling optimizations. Publications demonstrate expertise in sleep technology validation (2023 polysomnography comparisons), gender-specific work patterns (2021 work-life interference studies), and occupational safety frameworks (2020 night shift recommendations). He serves as Associate Editor for the Scandinavian Journal of Work, Environment & Health and consults for the National Accident Committee and Swedish Trade Unions.

Giovanni Sparacinoمشاهده پروفایل

استاد

• Biomedical Signal Processing
• Physiological Systems Modeling
• Continuous Glucose Monitoring
• +۳ مورد دیگر

Giovanni Sparacino is a Full Professor at the Department of Information Engineering, University of Padova, Italy. He holds teaching responsibilities for courses in Medical Informatics and Biological Data Analysis, while actively contributing to PhD supervision and departmental leadership roles including Vice-Head (2016/17) and Vice-Director of the PhD School in Information Engineering. Education: MSc in Electronics Engineering (summa cum laude), University of Padova (1992) PhD in Bioengineering, Polytechnic of Milan (1996) His research focuses on Biomedical Signal Processing, Computational Neuroscience, and Physiological Systems Modeling. Key contributions include glucose dynamics modeling, nonlinear signal analysis, and advanced algorithms for continuous glucose monitoring sensors. He has extensive experience in multimodal neuroimaging (EEG, fNIRS) and parameter estimation techniques. Notable research trends include: Development of machine learning approaches for diabetes management Integration of EEG and fNIRS for cognitive workload assessment Creation of wearable biomedical devices for foot pressure monitoring Advancements in physiological modeling and deconvolution techniques He has led the Bioengineering track PhD program since 2012 and contributed to establishing a web-based platform for multimodal neuroimaging data analysis.

Lim Lam Ghaiمشاهده پروفایل

مدرس

• Neuroimaging
• Functional Near-Infrared Spectroscopy (fNIRS)
• Brain-Computer Interface
• +۳ مورد دیگر

Dr. Lim Lam Ghai is a Lecturer at the School of Engineering, Monash University Malaysia. He holds a PhD in Electrical and Electronics Engineering from Universiti Teknologi PETRONAS (2022), along with a Master of Science (2017) and Bachelor of Engineering (2014) from the same institution. His research focuses on non-invasive neuroimaging (fNIRS) for mental health applications and AI-driven precision agriculture. He teaches Engineering Numerical Analysis and Applied Artificial Intelligence. Research Interests: Dr. Lim’s work bridges neuroimaging and AI, with projects on fNIRS for neurological diagnostics and AI in crop management. His contributions include enhancing fNIRS clinical applicability and developing autonomous agricultural systems. Collaborations span neuroscience, engineering, and sustainable development. Awards: He has received multiple accolades, including the IEEE SPS Malaysia Best PhD Thesis Award (2022), IChemE Training & Development Award (2022), and the International Illnovasi Competition prize (2022). Grants & Projects: He leads a project (2023–2026) on cognitive load estimation using deep learning. His work aligns with UN SDGs, particularly in health and sustainable agriculture. Active in peer review for journals like Biological Psychology and Frontiers in Human Neuroscience . Labs/Teams: Engaged in interdisciplinary research teams focused on neuroimaging technology and smart agriculture solutions.

FRANCESCO FERRACUTIمشاهده پروفایل

پژوهشگر

• Control Systems
• Robotics
• Brain-Computer Interfaces
• +۶ مورد دیگر

Prof. Francesco Ferracuti is a Researcher at the Department of Information Engineering (Università Politecnica delle Marche, Ancona, Italy). His work focuses on advanced control systems, robotics, brain-computer interfaces, and energy management. Key research themes include fault-tolerant control, autonomous navigation, and human-machine interaction through wearable sensors. Research interests span across: Control Engineering (MIMO systems, PID control, Model Predictive Control) Robotics (mobile robotics, UAVs, smart wheelchairs) Neuroengineering (EEG-based BCI, cognitive workload assessment) Energy Systems (renewable integration, home energy management) Data-Driven Methods (machine learning, system identification) Publications emphasize innovative solutions like EEG-driven obstacle avoidance and thermal stress monitoring for smart wheelchairs, demonstrating interdisciplinary approaches combining robotics with biometrics. His work on fault detection in multirotors and energy optimization algorithms highlights practical applications in both industrial and healthcare domains. Current projects include the E-MOTIVE initiative for mobility solutions via affective computing and BCI. His lab develops cutting-edge algorithms for cyber-physical systems security and predictive maintenance in industrial equipment.

Ge Gaoمشاهده پروفایل

استادیار

• Computer-supported cooperative work (CSCW)
• Computer-mediated communication (CMC)
• Human-centered computing (HCC)
• +۶ مورد دیگر

Ge Gao is an Assistant Professor at the University of Maryland's College of Information (INFO College). Her research focuses on the intersection of technology use and diversity in teamwork, employing mixed methods to design tools that enhance team communication and collaboration across contexts. She is available to advise PhD students and can be reached at gegao@umd.edu or via phone at 301-405-9518 in Hornbake Building (South Wing), Room 2123-A. Education: PhD in Communication from Cornell University (2017) MS in Psychology from Peking University (2010) BS in Psychology from Peking University (2007) Research Interests: Ge Gao's work centers on computer-supported cooperative work (CSCW), computer-mediated communication (CMC), human-centered computing (HCC), and multilingual/intercultural communication. She investigates how technology bridges linguistic and cultural divides in collaborative environments. Article Trends: Her recent publications (2025-2011) reveal a consistent focus on human-AI interaction, virtual reality social dynamics, multilingual collaboration tools, and the ethical implications of technology in professional and healthcare settings. Projects like "NatSGLD" and "CheXplain" highlight her contributions to robotics and medical AI transparency.

Christoph Tremmelمشاهده پروفایل

پژوهشگر ارشد

• Brain-Computer-Interfaces
• Electroencephalography
• Biomedical Engineering
• +۱ مورد دیگر

Dr. Christoph Tremmel is a Research Fellow in Wellbeing Technology at the University of Southampton. His research focuses on Brain-Computer-Interfaces (BCIs), Electroencephalography (EEG), Biomedical Engineering, and Human-Computer Interaction. He investigates applications such as cognitive workload estimation using commercial in-ear EEG headsets and enhancing virtual communication through synthesized co-presence. His work spans interdisciplinary projects funded by EPSRC, including collaborations on wearable biosensors and low-cost non-contact detection systems. Key research projects include the EPSRC Core Equipment Award 2022/23 collaboration and the 'Enhancing the effectiveness of virtual communication' initiative. Tremmel has published extensively on EEG signal processing, decision confidence prediction, and neural correlates of perceptual tasks. His research integrates machine learning, neurophysiology, and engineering principles to advance human-centric technologies. While no awards are listed, his contributions to wellbeing technology and virtual reality applications highlight his innovative approach to bridging neuroscience and computational systems. Collaborations with colleagues like Prof. M.C. Schraefel and Prof. Mark Spearing reflect his commitment to multidisciplinary problem-solving.

• Digital Mental Health
• Machine Learning
• Anomaly Detection
• +۳ مورد دیگر

Dr. Patrick McAllister is a Lecturer in Computer Science at Ulster University's School of Computing, Belfast campus, with research spanning digital mental health, machine learning, and anomaly detection. His work bridges computer science and healthcare through interdisciplinary collaborations. Education: PhD in Computer Science (Ulster University, 2018) - Thesis: Deep learning-based food image classification and crowdsourcing-based calorie estimation approach to support dietary management Research Interests: Dr. McAllister pioneers AI applications for mental wellbeing, focusing on chatbot systems for bibliotherapy, multimodal sensor analytics for anomaly detection, and digital interventions for community mental health. His work integrates machine learning with clinical requirements to develop scalable health technologies, particularly targeting youth mental health and diagnostic support systems. Publication Trends: Recent work (2023-2025) shows increasing focus on real-world AI deployment in healthcare, with 12 conference contributions and 4 journal articles exploring emotion recognition from physiological signals, explainable anomaly detection, and policy implementation of digital mental health services. His ecological study on vulture decision-making demonstrates cross-disciplinary methodology transfer. Awards: Outstanding Interdisciplinary Research Team Award (2024) for digital mental health innovations Advising and Grants: Supervises PhD researchers Muhammad Imad (Anomaly Detection) and Jing Hua Ye (Machine Learning for Emotion Recognition). Leads $1.2M NIH-funded project AI-assisted echocardiography for congenital heart defects diagnosis in Sub-Saharan Africa (2023-2026) and Department for Economy project Transforming Neonatal Cardiac Diagnostics with AI (2024-2025). Labs and Teams: Core member of Ulster's Digital Mental Health and Wellbeing (DMHW) research group, co-organizing the International DMHW Conference (2024, 2025) and leading the MVSE Multimodal Video Search project with Health and Social Care R&D Division.

Eduardo Davalosمشاهده پروفایل

استادیار

• Large Language Models
• Human-Computer Interaction
• Artificial Intelligence for Education
• +۷ مورد دیگر

Eduardo Davalos is an Assistant Professor in the Computer Science department at Trinity University in San Antonio, Texas. His work focuses on the intersection of AI in Education (AIED), Human-Computer Interaction (HCI), and Large Language Models (LLMs), with the aim of developing technologies that enhance learning experiences while reducing teacher workload through privacy-preserving, browser-native approaches. Dr. Davalos received his academic training at: B.S. in Electrical Engineering from St. Mary's University M.S. in Electrical Engineering from St. Mary's University Ph.D. in Computer Science from Vanderbilt University His research centers on developing technologies that translate into scalable learning solutions, with particular emphasis on browser-based sensing and modeling. Dr. Davalos is currently developing RedForest, a next-generation e-learning platform that combines AI, gamification, online collaboration, and domain-specific activities to enhance learning while providing AI-assisted lesson planning. His work bridges computer science, cognitive science, and educational psychology, with applications across multiple domains including literacy, nursing education, computer science education, and physics. He has developed WebEyeTrack, a head-pose-aware, deep-learning gaze estimation framework for browsers that runs entirely on-device. Analysis of Dr. Davalos's publications reveals a clear trajectory from foundational technology development (like WebEyeTrack) to sophisticated educational applications using LLMs for data analysis. His work consistently emphasizes practical, teacher-friendly tools that can be deployed in real classrooms without compromising student privacy. The research spans multiple educational contexts and demonstrates strong interdisciplinary collaboration, particularly with experts in cognitive science, psychology, and domain-specific education fields. Dr. Davalos leads the RedForest Lab, which focuses on creating learning technologies that benefit both students and teachers. His research has been supported by grants from the Institute of Education Sciences, U.S. Department of Education (Grants R305A150199 and R305A210347 to Vanderbilt University), indicating recognition of the importance and potential impact of his work in educational technology.

Felix Klotzscheمشاهده پروفایل

پژوهشگر

• Neuroscience
• Cognitive Science
• Virtual Reality
• +۳ مورد دیگر

Felix Klotzsche is a Research Scientist at the Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, and an affiliated researcher at the Berlin School of Mind and Brain, Humboldt-Universität zu Berlin. His work focuses on cognitive neuroscience, with a particular emphasis on short-term memory, attention, and virtual reality (VR) applications in clinical and experimental settings. Education: B.Sc. Psychology & B.A. Philosophy (2009–2015), Universität Regensburg M.Sc. 'Mind and Brain' (Track 'Brain', 2015–2017), Berlin School of Mind and Brain | Humboldt-Universität zu Berlin Research Interests: Neural mechanisms of visual short-term memory EEG analysis in VR environments Clinical VR applications for neurological assessment Emotional arousal decoding via neurophysiological signals 3D face perception and deep encoding models Cardiac-phase modulated perception Publications reflect a focus on integrating VR with neuroscience methodologies, particularly in evaluating spatial memory, threat perception, and emotional processing. Key tools include Python, R, Unity3D, and advanced EEG signal processing. No scientific awards explicitly noted. No listed advisees or grants. Active in the Research Group Mind-Body-Emotion, contributing to labs and teams developing VR-based cognitive assessment tools.

Morten Rieger Hannemoseمشاهده پروفایل

استادیار

• Computer Vision
• Medical Imaging
• 3D Reconstruction
• +۳ مورد دیگر

Morten Rieger Hannemose is an Assistant Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His research focuses on computer vision, medical imaging, 3D reconstruction, and AI-driven healthcare solutions. He is actively involved in projects like 'Fighting Cancer with Generative AI' and 'Decision support AI for Skin Lesions,' supervising multiple PhD students in these domains. His work contributes to UN Sustainable Development Goals related to health and innovation. Research interests include neural networks, medical image analysis, and computational imaging. Notable achievements include developing methods for digitizing material appearances and estimating diagnostic difficulty in skin lesion diagnosis. He collaborates internationally on topics such as monocular 3D pose estimation and multimodal data fusion. Morten has supervised PhD students in areas like crowd counting through remote sensing, generative AI for cancer diagnostics, and diffusion models for image segmentation. His projects often bridge theory and application, such as creating the 'Sportspose' 3D sports pose dataset. His email is mohan@dtu.dk , and his website is mortenhannemose.github.io .

David A. Robbمشاهده پروفایل

پژوهشگر ارشد

• Human-Computer Interaction
• Human-Robot Interaction
• Image-based emotion feedback
• +۵ مورد دیگر

David A. Robb is a Research Fellow in the School of Mathematical and Computer Sciences at Heriot-Watt University, where he has been actively contributing to academic research since completing his PhD in 2015. His career progression shows a clear trajectory from PhD student (2011-2015) to Research Associate (2015-2020) to his current Research Fellow position. Robb is currently leading research efforts across three major EPSRC/UKRI projects: DeMILO (studying expert laser aligners' thinking and strategies), @tas_trust (investigating trust in autonomous systems), and HUME (focusing on human-machine teaming for AUVs). Robb's research interests span multiple interconnected domains within human-centered computing. His primary focus areas include Human-Computer Interaction (HCI) and Human-Robot Interaction (HRI), where he investigates how humans interact with and trust autonomous systems. Additional interests include image-based emotion feedback mechanisms, image summarization techniques, cognitive aspects of human-computer interaction, computer-supported cooperative work (CSCW), and visualization of complex data. His work often bridges theoretical understanding with practical applications in industrial and service environments. Analysis of Robb's publication record reveals a strong trajectory of scholarly contribution with 54 publications to date. His recent work demonstrates a clear focus on practical applications of human-robot interaction in real-world settings, particularly in industrial automation (laser alignment systems) and service robotics (robo-barista studies). A notable trend is his increasing focus on understanding human factors in automation adoption, trust dynamics in human-machine teams, and the practical implementation challenges of deploying autonomous systems in complex environments. His publications span top-tier venues in HCI, HRI, and robotics, demonstrating both breadth and depth in his scholarly contributions. ACM ICMI2021 Best Reviewer Awards (Top 5%) ACM ICMI 2023 Outstanding Reviewer Award ACM ICMI 2024 Outstanding Reviewer Award Honourable Mention ACM CHI 2017 Research Papers and Notes Honourable Mention ACM DIS 2017 Research Papers and Notes Robb has been instrumental in securing and executing multiple significant research grants, particularly through EPSRC/UKRI funding mechanisms. His current role as Experimental Lead for the HRI theme of the ORCA Hub project (a major £5.7 million EPSRC-funded initiative) demonstrates his capability in managing substantial research programs. While specific details about student supervision aren't prominently featured in the provided materials, his active research program and teaching responsibilities (Experimental Design and Web Design and Databases) suggest engagement with graduate students. His work extends beyond pure research to practical implementation, as evidenced by projects like the MIRIAM multimodal interface for autonomous systems and the robo-barista field studies. Robb is affiliated with the Strategic Futures Lab at Heriot-Watt University and has been involved with the ORCA Hub (Offshore Robotics for Certification of Assets), a major UK Robotics and Artificial Intelligence Hub. His collaborative network is extensive, with co-authors from multiple departments at Heriot-Watt as well as international collaborators. Recent projects like the DeMILO study of laser alignment expertise and the @tas_trust project on human trust in autonomous systems indicate his work is increasingly focused on translating fundamental HRI research into practical industrial applications.

Azfar Khalidمشاهده پروفایل

مدرس ارشد

• Digital Twins and Cyber Physical Production Systems
• Human Robot Collaboration
• Robotic Manipulators
• +۶ مورد دیگر

Azfar Khalid is a Senior Lecturer of Mechanical Engineering in the Department of Engineering at the School of Science & Technology, Nottingham Trent University (NTU). He holds a BS in Mechanical Engineering from GIK Institute of Engineering, Pakistan and a PhD in Precision Engineering from University of Manchester (2009). Dr. Khalid teaches across Mechanical and Electronics Engineering disciplines, including Control Systems, Robotics, Cybernetics & Biomechatronics. His research focuses on smart factory systems, human robot collaboration, cyber physical production systems, digital twins, and Industry 4.0. He serves as the research coordinator of the Department of Engineering and leads the Digital Innovation Research Group within the Imaging, Materials & Engineering Centre. Dr. Khalid's publications demonstrate a strong trend toward human-centric digital twin technology, with emphasis on occupational safety, mental stress factors in human-robot collaboration, and cognitive workload assessment in smart manufacturing environments. His work bridges engineering, human factors, and neuroscience to create safer, more efficient industrial systems. Fellow of The Higher Education Academy (FHEA) Member of Institute of Mechanical Engineers (MIMechE) Chartered Engineer (CEng) Lead Guest Editor for special issues on Cyber Physical Systems Associate Editor for Industrial Robotics and Automation As a research leader, Dr. Khalid has secured significant funding including EPSRC grants totaling over £120K from Connected Everything initiatives, with PepsiCo as an industrial partner. He supervises multiple PhD students working on digital twins for workplace safety, mental stress management, and modular robotic systems. His lab collaborates with industrial partners including Olympus Technologies, Raruk Automation, WMH Robotics, and The Care Machine, as well as international academic institutions across Germany, Spain, and Pakistan.

Scott Jacksonمشاهده پروفایل

استاد

• Financial Accounting
• Managerial Accounting
• Auditing
• +۱ مورد دیگر

Dr. Scott Jackson is Professor of Accounting and Cramer Research Fellow at the University of South Carolina's Darla Moore School of Business. He teaches financial and managerial accounting courses and has won multiple teaching awards, including the Alfred G. Smith Award. His research on depreciation methods received the Impact on Management Accounting Practice Award twice (2012, 2015). Research examines capital investment decisions, earnings management, and auditing judgments. He serves on editorial boards for top accounting journals and has directed the Accounting Doctoral Program since 2010. Awards: Cramer Research Fellow, Impact on Management Accounting Practice Award (2012, 2015)

Gary Jonesمشاهده پروفایل

استاد

• Language Acquisition
• Developmental Psychology
• Cognitive Science
• +۳ مورد دیگر

Professor Gary Jones is affiliated with the Department of Psychology at Nottingham Trent University, where he has held roles since 2006. He leads the Language, Literacy and Psycholinguistics Research Group and chairs the School Research Degrees Committee. His research focuses on how real-world experience influences task behavior, particularly in language-related domains, leveraging computational models to analyze linguistic input and learning processes. Educational Background: BSc in Artificial Intelligence and Psychology (First Class Honors, 1995), University of Nottingham PhD (1999), University of Nottingham, focusing on cognitive architectures and developmental processes Research Interests: Investigating how children and adults utilize linguistic experience from caregivers and environmental input to shape task performance. Key areas include associative learning mechanisms, language acquisition challenges, and optimizing educational materials based on linguistic knowledge. His work addresses individual differences in learning abilities, particularly for children with language difficulties. External Roles & Collaborations: Economic and Social Research Council (ESRC) Peer College Review member Editorial roles at Frontiers in Cognitive Science and First Language Collaborations with institutions like Cardiff University and the Max Planck Institute Grants & Funding: Supported by The Leverhulme Trust and the Experimental Psychology Society for projects on language learning and developmental psychology. Labs & Teams: Leads the Language, Literacy and Psycholinguistics Research Group, focusing on computational modeling of language acquisition and developmental processes.

Balakumar Balasingamمشاهده پروفایل

دانشیار

• Battery Management Systems
• Human-Machine Interaction
• Signal Processing
• +۳ مورد دیگر

Dr. Balakumar Balasingam is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Windsor, Faculty of Engineering. His research focuses on Battery Management Systems, Human-Machine Systems, Signal Processing, Machine Learning, and Information Fusion. He holds a Ph.D. in Electrical Engineering from McMaster University (2008). He is a Professional Engineer of Ontario and a Senior Member of the IEEE. His work emphasizes robust battery management techniques, including parameter estimation for lithium-ion batteries and real-time state-of-charge/state-of-power estimation. He also investigates human factors in automated systems, leveraging eye-tracking and pupilometry for cognitive load assessment. Key contributions include the book *Robust Battery Management Systems With MATLAB* (Artech House, 2023) and over 100 peer-reviewed articles in IEEE Transactions, Energies, and other journals. Dr. Balasingam directs the Battery Management Systems Lab and Human Machine Systems Lab, advancing innovations in battery diagnostics, autonomous driving interfaces, and adaptive automation. His research integrates signal processing, machine learning, and control systems to address challenges in energy storage and human-technology interaction. His professional affiliations include the International Society of Information Fusion (ISIF) and active roles in IEEE technical committees. Research grants include funding for battery thermal management systems and cognitive load detection in automated driving scenarios.