جست‌وجوی استاد و پژوهشگر

Sarah Clinchمشاهده پروفایل

مدرس ارشد

• Human-Computer Interaction (HCI)
• Pervasive Computing
• Cognitive Science
• +۵ مورد دیگر

Sarah Clinch is a Senior Lecturer in Computer Science at the University of Manchester, specializing in Human Computer Systems. She is affiliated with the Healthy Ageing Research Group and leads the 'Human-Driven Pervasive Data Application' project. Her research focuses on memory augmentation, queer HCI, pervasive computing, and IoT, with contributions to dementia care technology and mobile sensing interventions. Education details are not explicitly stated in the text, but her expertise spans interdisciplinary areas such as cognitive science, digital health, and user-centered design. She actively supervises PhD students in topics like episodic memory technology, aphantasia/hyperphantasia, and queer HCI theories. Her work contributes to UN SDGs related to health and well-being. Notable projects include the Healthy Ageing Research Group (HARG) and the Human-Driven Pervasive Data Application initiative. She has organized events like the 34th British HCI Conference and Gender Advancement workshops. Collaborations include work on dashboards, pervasive displays, and IoT workflows. She co-created datasets on programmer strategies and website use impacts. Her research emphasizes ethical considerations in cognitive augmentation and privacy in memory technologies.

• Artificial Intelligence
• Machine Learning
• Knowledge Graphs
• +۳ مورد دیگر

Zifeng Ding is a Research Fellow at the Department of Computer Science and Technology, University of Cambridge, affiliated with the School of Technology. His research focuses on artificial intelligence, machine learning, knowledge graphs, and their applications in natural language processing and computer vision. Key interests include temporal knowledge graph reasoning, multimodal learning, and the reliability of large language models (LLMs). He has contributed to benchmark development (e.g., TCP, FOReCAst), dataset creation (AVerImaTeC), and methods addressing LLM limitations (e.g., hallucination mitigation, bias analysis). His work bridges theoretical advancements in graph neural networks with practical applications in real-world systems like supply chain analysis and evidence-based fact verification. Current projects explore parameter-efficient model tuning (Perft) and cross-lingual knowledge editing via in-context learning. Publications span top venues, emphasizing temporal dynamics, causal reasoning, and trustworthiness in AI systems. He collaborates widely on topics ranging from dynamic graph modeling (DyGMamba) to future outcome prediction (FOReCAst benchmark).

Nori Jacobyمشاهده پروفایل

استادیار

• Cognitive Science
• Computational Neuroscience
• Music Cognition
• +۳ مورد دیگر

Nori Jacoby is an Assistant Professor in the Department of Psychology at Cornell University and a Research Group Leader at the Max Planck Institute for Empirical Aesthetics in Frankfurt. His research examines how internal representations shape sensory and cognitive abilities through computational approaches, cross-cultural studies, and large-scale behavioral experiments. Ph.D. from Hebrew University of Jerusalem (Edmond and Lily Safra Center for Brain Sciences) Postdoctoral training at MIT, UC Berkeley, and Columbia University Research areas include: Computational modeling of human perception Cross-cultural analysis of musical cognition Collective behavior in social networks Recent publications focus on: Global rhythm perception patterns Machine learning alignment with human cognition Oral transmission mechanisms in music evolution Scientific awards: NSF grant for collaborative creativity research Presidential Scholar in Society and Neuroscience Lab: Cornell Computational Cognition (CoCoCo) Lab

• Neural Engineering
• Biomedical Engineering
• Human-Computer Interaction
• +۳ مورد دیگر

Alexandre Armengol Urpi is a Postdoctoral Associate at the Massachusetts Institute of Technology (MIT), affiliated with the Department of Mechanical Engineering within the School of Engineering. His research focuses on neural engineering, biomedical engineering, human-computer interaction, IoT systems, and signal processing. He explores applications such as brain-computer interfaces (BCI), EEG-based diagnostics, and augmented reality (AR) integration with IoT devices. His work emphasizes translating tacit expert knowledge into actionable data, improving medical diagnostics through machine learning, and enhancing cybersecurity in BCI systems. Projects include developing wireless health-monitoring systems and hands-free AR navigation tools using SSVEP signals. Recent studies address covert spatial attention decoding, industrial IoT-AR systems, and RFID innovations beyond traditional identification. While no formal student advisees are listed, his research collaborations involve interdisciplinary teams across MIT. His contributions span theoretical frameworks and practical implementations, with a focus on closing the gap between human expertise and technological systems. Key research trends include integrating biosignals with smart devices, enhancing neural interface accuracy, and applying deep learning to object pose estimation in industrial contexts. His publications highlight a balance between foundational neuroscience and applied engineering solutions. No scientific awards or grants are explicitly mentioned in the provided texts.

• Bioinformatics
• Machine Learning
• Health Informatics
• +۳ مورد دیگر

Dr. Matloob Khushi is an academic staff member at the University of Sydney's School of Computer Science, where he has held roles since joining in 2017. He earned his Ph.D. from the University of Sydney and has published over 50 research papers. His research focuses on big data analysis, machine learning applications in health, finance, and genomics, with notable contributions to adverse drug event prediction, bioinformatics tools, and predictive modeling in healthcare. Dr. Khushi's work has been recognized in media for innovations like the MatCol tool aiding cancer research. Education: Ph.D. in Computer Science, University of Sydney (2008). Research Interests: Machine learning for big data, biomedical informatics, financial analytics, and health informatics. Recent articles highlight advancements in graph neural networks, predictive models for chronic diseases, and tools for genomic analysis. He advises two Ph.D. students and collaborates on interdisciplinary projects.

John Simminsمشاهده پروفایل

استاد پژوهشی

• Renewable Energy Systems
• Power Grid Management
• Augmented Reality Applications
• +۳ مورد دیگر

Dr. John Simmins serves as Director of Alfred University's GE Vernova Advanced Power Grid Lab within the Inamori School of Engineering. His expertise spans renewable energy systems, power grid operations, and innovative technologies like augmented reality (AR) in utility settings. Previously, he led the New York State College of Ceramics' Center for Advanced Ceramic Technology (CACT) and worked at the Electric Power Research Institute (EPRI) for a decade, focusing on AR-AI-GIS integration and grid modernization. His research emphasizes AR's impact on utility worker safety, smart grid interoperability via Common Information Models (CIM), and distributed energy resource management. Key projects include optimizing grid automation through data analytics and developing geolocation systems for utility infrastructure using photographic imagery. He promotes workforce mobility solutions and sustainable energy policies through academic-industry collaborations. Dr. Simmins has advised on over 20 EPRI smart grid demonstration projects and pioneered tools for DER integration. His work bridges advanced ceramics research with contemporary energy challenges, including high-temperature superconductors and magnetic materials for early career contributions.

Sharon L. Burtonمشاهده پروفایل

استاد مدعو

• Cyber Security
• Organizational Change Management
• Telemedicine/Telehealth
• +۴ مورد دیگر

Dr. Sharon L. Burton is an Adjunct Professor at Capitol Technology University, specializing in Cyber Security, Organizational Change Management, Telemedicine/Telehealth, and Product Management. She holds a Ph.D. in Cybersecurity Leadership (2022) and prior degrees in Quality Systems Management, Management, and Criminology. With over 85 academic publications and 30 conference presentations, her research focuses on cybersecurity leadership, AI applications, and organizational development. Her work emphasizes bridging technical and non-technical communication, agile solutions, and cross-functional team leadership. Dr. Burton’s research interests span cybersecurity strategies, AI-driven customer service, telemedicine innovation, and inclusive organizational culture. Her publications highlight interdisciplinary approaches to technology integration, risk management, and human capital development. Notable awards include the 2019 Outstanding Contribution to FOD ECN and the 2013 Presentation Excellence Award. She advises on business resilience, cybersecurity governance, and leadership development. Active in professional organizations like the Human Capital Institute and Toastmasters International, she also volunteers as an academic reviewer for journals and conferences. Her consulting firm, S. L. Burton Consulting, LLC, offers expertise in process improvement, change management, and instructional design.

Sergej Dogadovمشاهده پروفایل

پژوهشگر

• Probabilistic Machine Learning
• Bayesian Methods
• Approximate Neighbor Search
• +۴ مورد دیگر

Sergej Dogadov is a Doctoral Researcher at the Technical University of Berlin's Department of Machine Learning and Intelligent Data Analysis. His primary roles include academic supervision of Bachelor's and Master's thesis students while conducting advanced research in Biomedical Sensing and Machine Learning applications. Education: Master's in Computer Science (2015) Research interests focus on latent space analysis using Recurrent Neural Networks, multimodal data augmentation techniques, and applications in biomedical signal processing (MEG/EEG/NIRS). He has pioneered projects like Motor Load Detection for Bosch Siemens Haushaltsgeräte GmbH and accessibility-focused language translation tools for disabled communities. Current work explores latent space correlations in biomedical sensing data. Teaching contributions include Python Programming for Machine Learning and Bayesian Analysis courses at TU Berlin and industry training for Petrobras Brasil professionals.

Markus Wenzelمشاهده پروفایل

دانشیار

• Medical Imaging
• Deep Learning
• Healthcare AI
• +۳ مورد دیگر

Prof. Markus Wenzel is an Associate Professor of Medical Cognitive Computing at the School of Computer Science & Engineering, Constructor University Bremen. His research focuses on interpretable deep learning and computer-aided diagnosis systems for healthcare applications, particularly in medical imaging. He holds a PhD in 'Complexity Reduction in Image-Based Breast Care' (2014) from University Bremen and a BSc in Computer Science (With Distinction) from the Distance Learning University, Hagen. Wenzel has extensive industry experience, including roles at Fraunhofer MEVIS as a Key Scientist in Computational Breast Care (since 2021) and Senior Scientist (2005–2021). His work emphasizes clinical practicality, such as breast MRI segmentation and AI-driven diagnostic tools. Research highlights include developing the QuantMed platform for translational research and exploring generative adversarial networks (GANs) for medical data synthesis. He has authored over 50 peer-reviewed publications, with recent work focusing on CNN-based lesion detection, swarm learning, and uncertainty quantification in medical imaging. Awards include a prestigious scholarship from the Studienstiftung des Deutschen Volkes and certification as a Scientific Trainer (2022). Wenzel collaborates with clinicians to bridge AI research and clinical practice, addressing challenges like model interpretability and data scarcity in healthcare. His contributions span medical imaging analysis, AI-driven decision support systems, and robust deep learning frameworks for heterogeneous medical data.

Carla Amanda Rueمشاهده پروفایل

پژوهشگر ارشد

• Physiological Responses to Physically Demanding Occupations
• Physical Employment Standards
• High Altitude Health and Performance
• +۳ مورد دیگر

Dr. Carla Amanda Rue is a Research Fellow at the University of Chichester, specializing in Exercise Physiology. She leads the OPRG in delivering commercial contracts for the British Ministry of Defence (MOD) and National Ambulance Resilience Unit (NARU), focusing on Physical Employment Standards (PES) and high-altitude health. Her research explores physiological responses to physically demanding occupations and military performance optimization. Dr. Rue holds a PhD from the University of Chichester (2014), an MSc from Bangor University (2010), and a BSc from the University of Chichester (2009). Affiliations: University of Chichester, Birmingham Medical Research Expeditionary Society (BMRES), MOD, NARU. Teaching: Physiology modules across multiple degree pathways at the University of Chichester. Her research interests include military physical performance, casualty management, and high-altitude physiology. Key projects include developing PES for Ground Close Combat roles and analyzing load carriage in military operations. Dr. Rue has contributed to over 15 peer-reviewed articles and technical reports, focusing on biomechanics, fitness testing, and operational stress adaptation. She has presented at international conferences such as the International Conference on Soldier Physical Performance and the Physical Employment Standards Conference. Her work bridges applied physiology with practical military and emergency response applications, emphasizing evidence-based standards for physical performance and resilience.

Lauren Herckisمشاهده پروفایل

پژوهشگر

• Implementation Science
• Educational Technology
• Anthropology of Technology
• +۷ مورد دیگر

Lauren Herckis is an anthropologist affiliated with Carnegie Mellon University's Simon Initiative, School of Computer Science (Human-Computer Interaction Institute), and University Libraries. Her research focuses on implementation science, faculty culture, educational technology, and sociotechnical systems in higher education. She explores how technology adoption impacts teaching practices and organizational culture, with a particular emphasis on issues like digital literacy, VR pedagogy, and AI-Augmented Learning. Her work is funded by organizations including the National Science Foundation, the Chan Zuckerberg Initiative, and the Hillman Family Foundations. She has held a Fulbright Fellowship and collaborated with institutions such as the U.S. Department of Veterans Affairs and the University of Pittsburgh's Center for Teaching and Learning. Her research methodologies combine ethnography, mixed-methods approaches, and design-based research. Key projects include developing the Digital Robotics Archive and Multimodal Archive Toolkit, analyzing barriers to technology adoption in academia, and designing interventions for workforce readiness in community colleges. She advises students like Rotem Guttman and contributes to initiatives like the CyberSim Lab and AI-literacy programs in academic libraries. Key Awards: Fulbright Fellowship Funding Sources: NSF, CZI, Carnegie Corporation, Hillman Foundations Major Projects: Simon Initiative collaborations, Digital Robotics Archive, AI-Augmented Learning in Community Colleges Her interdisciplinary approach bridges anthropology, education science, and computer science to address challenges in postsecondary education transformation and sociotechnical systems.

• Neurotrauma
• Traumatic Brain Injury
• Functional Magnetic Resonance Imaging
• +۳ مورد دیگر

Zhifeng Kou is an Associate Professor in the Department of Bio-Medical Engineering at the New Jersey Institute of Technology (NJIT). His research focuses on advanced neuroimaging techniques, particularly in understanding neurotrauma, traumatic brain injury (TBI), and brain network dynamics. Key areas include functional MRI, susceptibility weighted imaging, and arterial spin labeling methods. He has led projects such as the 2015 Federal Grant-funded study on assessing brain tissue viability post-TBI using susceptibility mapping. His work integrates clinical applications, such as 3D augmented reality for surgical planning, and AI-driven models like the Segment Anything Model (SAM) for brain tumor segmentation. Collaborations span institutions globally, with notable contributions to studies like the characterization of microstructural brain abnormalities in cirrhotic patients. His research has been featured in journals like World Neurosurgery and Brain Imaging and Behavior , and he has contributed to textbooks on TBI neuroimaging. Kou’s work bridges biomedical engineering with clinical neuroscience, emphasizing translational applications in diagnostics and surgical planning.

Will Hurstمشاهده پروفایل

دانشیار

• Data Science
• Extended Realities (XR)
• Critical Infrastructures
• +۷ مورد دیگر

Dr. Will Hurst is an Associate Professor in Data Science and Extended Realities at Wageningen University & Research's Information Technology Group. He holds a PhD in Computer Science (focused on Critical Infrastructures) and has over 100 international publications across data science, creative technologies, and critical infrastructure security. His research emphasizes XR applications in education, healthcare, and sustainability, alongside smart meter analytics and cybersecurity. Education: PGCert (Distinction) in Higher Education, MSc Web Computing, BSc Product Design Grants: EPSRC-funded 'Data Analytics for Health-Care Profiling using Smart Meters' and InnovateUK's Productivity Accelerator Research interests include digital twins, immersive learning technologies, and interdisciplinary hackathon methodologies. Recent work explores AR/VR in medical training, NFTs in art, and sustainable consumer behavior. Awards include recognition as an award-winning Reader in Creative Technologies at Liverpool John Moores University (2016-2022). Teaching includes courses on data visualization, digital game development, and advanced multimedia systems. Engages in active aging initiatives through co-design hackathons and community-based projects.

• Clinical Biomechanics
• Sports Physiotherapy
• Functional Assessment
• +۳ مورد دیگر

Néstor Pérez Mallada is a Professor at the Department of Physiotherapy in the School of Nursing and Physiotherapy San Juan de Dios at Universidad Pontificia Comillas. He holds a Doctorate in Biomedicine and Health Sciences from Universidad Europea de Madrid (2008) and additional master’s degrees in Health Law and Physiotherapy. His academic roles include former Head of Physiotherapy Studies (2009–2017), Director of the Master’s in Biomechanics Applied to Damage Assessment (2009–2020), and currently serves as Head of the Biomechanics Laboratory since 2024. His research focuses on clinical biomechanics, sports physiotherapy, and biomechanical instrumentation. He collaborates on injury prevention protocols for elite athletes, particularly in basketball and soccer, using advanced biomechanical tools. His work integrates hardware/software development, augmented reality in education, and precision measurement technologies in clinical, sports, and workplace settings. Key publications span knee injury biomechanics, diagnostic thermography, and goniometer validation. He advises on biomechanical laboratory management and maintains ties with industry for equipment development. Professional certifications include basketball coaching and manual lymphatic drainage expertise. His academic leadership emphasizes evidence-based practices and interdisciplinary innovation in healthcare education and clinical research.

• Computer Graphics
• Medical Imaging
• Computer Vision
• +۴ مورد دیگر

Dr. Hammadi Nait-Charif is an Associate Professor in Animation at Bournemouth University. His research spans computer graphics, medical imaging, and neural networks, with a focus on applications in healthcare technology and interactive systems. He has contributed to advancements in motion tracking, 3D reconstruction, and assistive elderly care frameworks like ANGELAH. His work integrates interdisciplinary approaches combining computer vision, biomedical engineering, and machine learning. Research Interests : His key areas include computer animation techniques, medical image analysis (e.g., mammography and vertebra tracking), human motion capture systems, and fault-tolerant neural network architectures. Recent projects involve multi-modal feature fusion for medical report generation and holographic display technologies. Grants & Contributions : He secured a grant for developing a handheld POS system (InnovateUK, 2011). His collaborations span institutions worldwide, addressing challenges in elderly care, surgical training simulators, and spinal imaging. Labs & Teams : His work often involves interdisciplinary teams focusing on medical imaging solutions and animation systems integration.