Find Academic Supervisors

• Artificial Intelligence
• Trustworthy Federated Learning
• Collaborative AI
• +3 more

Dr. Han Yu is an Associate Professor at the College of Computing and Data Science (CCDS), Nanyang Technological University (NTU), Singapore. He leads the TrustFUL Research Lab, focusing on trustworthy federated learning and ethical AI systems. His work bridges AI, data privacy, and collaborative computing, with over 300 publications in top conferences/journals. He holds prestigious fellowships and chairs roles in IEEE and AAAI. Dr. Yu earned his PhD in Computer Science from NTU in 2014, followed by postdoctoral research at the Lee Kuan Yew Fellowship. His research has been recognized with multiple awards, including World's Top 2% Scientists in AI and the TOYP Singapore honor. Education: PhD (2014), BEng (2007), both from NTU's School of Computer Engineering. Professional roles include Associate Editor of IEEE TNNLS and sponsorship roles in IJCAI/WWW conferences. He has secured grants exceeding S$10M for projects like federated learning in healthcare and edge-cloud networks. His lab develops tools like FedVision and CrowdFL, addressing real-world challenges through federated learning frameworks. Dr. Yu also contributes to AI ethics education, teaching courses on ethical AI governance and sustainable computing. His research spans federated learning security, multi-modal LLMs, and healthcare applications, with recent breakthroughs in quantum federated learning and privacy-preserving algorithms. Over 25 PhD students have graduated under his mentorship, contributing to industry and academia. Awards highlight his impact, including the Innovative Application of AI Award (AAAI 2025) and recognition in federated learning’s foundational work.

• Computer Science
• Privacy Protection
• Computer Vision
• +3 more

Riccardo Mazzon is a Researcher at the School of Electronic Engineering and Computer Science, Queen Mary University of London. As a CIS Research Manager, he focuses on privacy protection, computer vision, and machine learning applications. His work bridges technical innovation with ethical considerations, particularly in adversarial attacks, underwater imaging, and multi-target tracking. Research Interests: Privacy-Preserving Technologies Adversarial Machine Learning Computer Vision for Surveillance and Environmental Monitoring Image Quality Assessment and Enhancement Multi-Camera and Crowd Analysis Systems Publications highlight a focus on privacy challenges in digital systems, with recent contributions on adversarial reversibility and underwater image filtering. Earlier work explores crowd dynamics and multi-target tracking algorithms. No scientific awards or grants are explicitly listed in the provided data. Labs/Teams: Likely affiliated with Queen Mary’s Computer Vision and Security research groups, though specific lab affiliations are not detailed.

• Indoor Positioning Systems
• Human Activity Recognition
• Environmental Monitoring
• +3 more

Yonglei Fan is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on interdisciplinary applications of sensor technologies, machine learning, and spatial analytics. Key areas include indoor positioning systems, human activity recognition, environmental monitoring, and public health analysis. He has contributed to studies on smart city infrastructure, pollution impact assessment, and energy resource forecasting. Research interests span technical innovations in sensor fusion (IMU/UWB/FMCW radar), Bayesian-based navigation algorithms, and environmental health studies using big data. His work integrates IoT and behavioral data to address challenges in urban sustainability, healthcare monitoring, and energy systems. Notable projects include analyzing dockless bike-sharing systems for urban mobility insights, developing contactless health monitoring systems, and evaluating indoor positioning technologies through competitive benchmarks. His research often combines experimental methods with advanced computational models to solve real-world problems in smart cities and environmental science.

Frans CornelissenView profile

Professor

• Ophthalmology
• Visual Neuroscience
• Neuroimaging
• +4 more

Prof. Frans Cornelissen is a Professor in the Faculty of Medical Sciences at the University of Groningen, holding the Chair in Ophthalmology and Visual Neuroscience within the Department of Ophthalmology. He leads the Laboratory for Experimental Ophthalmology and is affiliated with the UMCG (University Medical Center Groningen). His research focuses on restoring vision in visually impaired individuals through advancements in ophthalmic diagnosis, visual rehabilitation, and understanding neurodegenerative diseases like glaucoma. Key methods include eye tracking, neuroimaging (fMRI, MRI), and behavioral studies. Research interests span visual neuroscience, glaucoma mechanisms, neuroplasticity, and applications of virtual reality in vision rehabilitation. Recent work includes studies on visual field defects, prosthetic vision enhancement, and systematic reviews on hemianopia scanning behaviors. Cornelissen holds additional roles as President of the Vereniging voor Biofysica en Biomedische Technologie and chairs the Stichting Uitzicht. Publications emphasize innovative techniques like fMRI micro-probing, pupil perimetry, and continuous visual tracking for early disease detection. His work bridges clinical ophthalmology with computational neuroscience, aiming to translate findings into practical diagnostic and therapeutic tools.

Steven HoubenView profile

Associate Professor

• Human-Computer Interaction
• Human-Data Interaction
• Tangible Computing
• +4 more

Dr. Steven Houben is an Associate Professor in Human-Data Interaction at Eindhoven University of Technology (TU/e) and the Artificial Intelligence Systems Institute (EAISI). His research focuses on Human-Computer Interaction (HCI) and Human-Data Interaction (HDI), with emphasis on tangible computing, collaborative augmented device ecologies, and physicalizing data for non-expert interaction. He holds a PhD from the IT University of Copenhagen (2015) and a Master's from Hasselt University (2011). His work spans digital health, education, Industry 4.0, and knowledge work, with notable contributions to medical device design, tele-rehabilitation systems, and cross-device collaboration tools. He leads the TREAT (ITEA) project and teaches courses such as 'Data-enabled design' and 'Designing user interfaces with emerging technologies'. Education: PhD in Computer Science (ITU Copenhagen), Master's in Computer Science (Hasselt) External Position: Visiting Faculty at Lancaster University since 2021 Research interests include physical data embodiment, collaborative device ecosystems, and HCI innovations for healthcare and work environments. Key projects explore tangible tools for data literacy (e.g., SensorBricks, DataChest) and telemonitoring solutions for cardiac rehabilitation. Over 22 students have been supervised, with publications addressing data physicalization challenges, wearable device comfort, and contextual telehealth systems. Grants include the TREAT project (2024–2026) focusing on interoperable medical record systems. His work contributes to UN SDGs in health (SDG 3) and education (SDG 4).

Steven VerstocktView profile

Professor

• Multimodal Data Processing
• Video Analysis
• Computer Vision
• +2 more

Steven Verstockt is a Professor at Ghent University’s Department of Electronics and Information Systems, affiliated with the Internet Technology and Data Science Lab (IDLab) and the Multimedia Lab. His research spans multimodal data processing computer vision video analytics geospatial information technology sports data science domains. He leads interdisciplinary projects in sports, media, and cultural heritage, focusing on spatio-temporal data collection, classification, and visualization. Recent research trends highlight his work in CO2-based occupancy detection BIM semantic enrichment multi-camera sports analytics geosocial media mining historical archive digitization hyperspectral image classification published across 2025–2024. His team combines computer vision, machine learning, and geospatial tools for real-world applications.

• Social Cognition
• Decision Sciences
• Cognitive Processes
• +5 more

Prof. Dr. Mandy Hütter is a Professor of Social Cognition and Decision Sciences at the University of Tübingen’s Faculty of Science, leading the Social Cognition and Decision Sciences research group. Her work focuses on understanding cognitive processes underlying attitudes, judgment, and decision-making under varying contextual conditions. She holds a diploma in Psychology from the University of Tübingen (2002–2008) and a PhD from the University of Freiburg (2010–2013), with postdoctoral research at Heidelberg University and international stays in Belgium and the USA. Her research integrates statistical modeling to analyze cognitive processes in evaluative conditioning, decision framing, and advice-taking behaviors. Key themes include how sampling mechanisms influence evaluations, the role of contingency awareness in learning, and the interplay between social context and cognitive outcomes. Her work bridges experimental psychology with practical applications in consumer behavior and moral decision-making. Her academic career includes roles as Junior Professor for Social Psychology (2013–2017) and Head of the Social and Organisational Psychology Department. Current research explores evaluative learning dynamics, decision-making under uncertainty, and metacognitive biases in AI interactions. She advises on interdisciplinary projects at the intersection of cognitive science and technology.

Roberto CasulaView profile

Assistant Professor

• Biometrics
• Fingerprint Spoofing
• Adversarial Machine Learning
• +6 more

Roberto Casula is an Assistant Professor at the University of Cagliari, specializing in Biometrics and Fingerprint Spoofing. He leads research in liveness detection, adversarial machine learning, and explainability. His work focuses on enhancing security in biometric systems through innovative methods like 3D face reconstruction and fusion techniques for surveillance applications. Education: Bachelor's in Electronic Engineering (University of Cagliari, 2015) Master's in Telecommunications Engineering (University of Cagliari, 2019) PhD in Biometrics, PRALab (2023), thesis: The Art of Fingerprint Spoofing Research Interests: Fingerprint presentation attack detection Adversarial attacks and deepfake mitigation Explainability in security systems Crowd analysis and surveillance 3D face biometrics and fusion methods Competitions & Leadership: Organized the LivDet competition (2021-2023), overseeing spoof creation and sensor integration. Pioneered realistic fingerprint spoofing techniques like ScreenSpoof. Collaborates with PRA Lab and PRALab on biometric security challenges. Labs & Teams: Active member of PRA Lab and PRALab, focusing on fingerprint spoofing, liveness detection, and adversarial machine learning applications.

• Computational Social Science
• Complexity Science
• AI Ethics
• +4 more

Prof. Dirk Helbing is a Full Professor at the Department of Humanities, Social and Political Sciences at ETH Zürich. His work focuses on computational social science, complexity science, and the ethical implications of digital transformation. He explores topics such as AI ethics, smart cities, democracy upgrades, traffic systems, and crisis management. His research integrates multi-agent systems, participatory design, and value-sensitive approaches to address societal challenges. Education details: Not explicitly mentioned in provided texts, but as a Full Professor at ETH Zürich, he likely holds a PhD in a related field. His academic career includes leadership roles in interdisciplinary research. Research interests span digital democracy innovations, socio-economic impacts of technology, and resilience engineering. Notable themes include voting system reforms, digital twin applications for governance, and citizen science in AI development. His work emphasizes participatory frameworks and systemic solutions for sustainability and fairness. Recent articles highlight topics like traffic management optimization, multi-currency systems for crisis response, and ethics in smart city design. He advocates for human-centered technologies and democratic upgrades through digital tools. No awards listed in provided texts. Grants and advising activities are not detailed here but likely central to his academic work. Labs/teams: Implied involvement in interdisciplinary teams focused on computational social science, digital governance, and urban systems modeling, though specific lab names are not mentioned.

Matthew DanishView profile

Lecturer

• Units-of-measure Verification
• Static Analysis
• Programming Languages
• +7 more

Dr Matthew Danish is a faculty member at the Department of Computer Science and Technology, University of Cambridge. His research spans multiple areas within computer science, with a focus on practical applications of theoretical concepts. He is based in room FE25 of the William Gates Building at 15 JJ Thomson Avenue, Cambridge CB3 0FD. Dr Danish's research interests encompass a diverse range of topics including Machine Learning and Artificial Intelligence, Programming Languages, Semantics and Verification, and Systems and Networking. His work bridges theoretical computer science with practical applications in urban computing, privacy-preserving systems, and edge computing. He has made significant contributions to the field of units-of-measure verification, developing techniques to ensure correctness in scientific computing. His recent publications (2019-2025) demonstrate a clear research trajectory focused on applying computer science to real-world problems. Danish has published extensively on units-of-measure verification, static analysis of scientific code (particularly Fortran), privacy-preserving systems for edge devices, and applications of computer vision in urban environments. His work often combines theoretical rigor with practical implementation, as evidenced by projects like DeepDish that run on off-the-shelf hardware like Raspberry Pi. Dr Danish has supervised or collaborated on numerous research projects related to smart environments, adaptive city platforms, and real-time data processing systems. His work on Cerberus demonstrates expertise in developing privacy-preserving solutions for crowd counting and localization, while his research on RACER shows capabilities in complex event recognition systems. Based in the William Gates Building, Dr Danish is part of Cambridge's vibrant computer science research community, contributing to multiple research themes within the department. His interdisciplinary approach connects computer science theory with applications in urban studies, public health, and environmental monitoring.

• Molecular Dynamics
• Drug Design
• Ion Channel Gating
• +4 more

Dr. Yun Lyna Luo is an Associate Professor in the College of Pharmacy at Western University of Health Sciences . Her research focuses on computational biophysics, particularly using molecular dynamics simulations to study ion channel mechanisms and AI-assisted drug design. She joined the university in 2013 and has held leadership roles in editorial boards (Nature Methods, Biophysical Journal) and professional organizations (Biophysical Society, American Chemical Society). Education: PhD, Ecole Normale Supérieure (Paris VI), 2004-2007 Postdoc, Argonne National Laboratory & University of Chicago, 2008-2013 BS/MS, Zhejiang University, 1997-2004 Research Interests: Her lab develops multiscale simulation methods to study ion channel dynamics (e.g., PIEZO1, connexins) and applies these to drug discovery. Key areas include ligand-receptor interactions, membrane protein function, and mechanism-driven drug design. Recent work targets piezo channels for contraception and PRMT inhibitors for leukemia treatment. Grants & Awards: NIH R01 Grant (MPI): Pharmacological Modulation of PIEZO1 Channels (2019-2028) NSF Collaborative Research: Sensory Membrane Protein Dynamics (2024-2027) Over 40 invited talks at conferences including Gordon Research Conferences and ACS National Meetings Teaching & Service: Course instructor: Molecular Simulation, Pharmacological Basis of Therapeutics Leadership roles: College Tenure & Promotion Committee (2023-2024), Department Vice Chair (2018-2019) Laboratory: The Luo Lab operates at the interface of computational biophysics and translational medicine. Key projects include developing FAIR-compliant simulation protocols and collaborating on projects such as CatSper channel-based contraception and cancer immunotherapy strategies.

Benhur Bakhtiari BastakiView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Data Analytics
• +6 more

Benhur Bakhtiari Bastaki is a Senior Lecturer in AI and Big Data Analytics at Staffordshire University. He serves as Course Leader for the BSc Artificial Intelligence program and Programme Coordinator for the international partner EFREI Paris. His academic roles include leading Staffordshire Digital Innovation Partnerships (SDIPs) and SCIMIA projects, focusing on AI solutions for SMEs since 2018. He is also the Program Advisor for Microsoft Learn for Educators Institutional Programme (MLEP), organizing workshops for Azure certifications. Benhur holds a PhD in Computing Science (2019) and multiple Staffordshire University degrees, including a BSc and MSc in Computing Science, alongside a Postgraduate Certificate in Higher Education. His research spans artificial intelligence, applied machine learning, pattern recognition, and smart systems. Notable projects include developing AI-driven marketing tools, smart sensor integration in sports equipment, and health analytics for demand modeling. Professional memberships include Fellowship of the HEA and membership of the British Computing Society. Benhur’s publications address anomaly detection in crowds, IoT forensics, and healthcare frameworks. His work bridges academia and industry, emphasizing practical AI applications. Institutional recognitions include Staffordshire’s outstanding research impact (Times Higher Education 2023) and world-leading research ratings in REF 2021.

Camilo Perez QuinteroView profile

Research Fellow

• Human-Robot Interaction
• Augmented Reality
• Virtual Reality
• +3 more

Camilo Perez Quintero is a Research Fellow at the University of British Columbia's CARIS laboratory, specializing in Human-Robot Interaction systems. His current work includes developing augmented reality multimodal interfaces for aerospace manufacturing in collaboration with the German DLR and creating sidewalk navigation solutions for delivery robots through a Mitacs industry partnership. Education: Ph.D. in Computing Science, University of Alberta, Canada (2017) His research centers on communication mechanisms for human-robot collaboration, with expertise in augmented reality interfaces, haptic feedback systems, and gesture-based control. He excels at translating theoretical concepts into functional prototypes through multidisciplinary teamwork, focusing on mission-critical applications in manufacturing and urban environments where safety and intuitive interaction are paramount. Analysis of his 15 most recent publications (2017-2022) reveals consistent innovation in robot trajectory programming through multimodal interfaces, with significant contributions to safe human-robot collaboration in manufacturing via virtual barriers and pedestrian-aware navigation for sidewalk robots. His work bridges computer vision, robotics, and human-centered design, demonstrating particular strength in integrating tactile feedback with AR/VR systems for industrial applications. Grants and Programs: Mitacs Accelerate program developing HRI systems for sidewalk mobile robots with industrial partner German DLR collaboration on aerospace manufacturing interfaces He operates within UBC's CARIS laboratory, collaborating with mechanical engineering researchers and international partners to advance practical robotics solutions for real-world deployment in constrained environments.

• Internet of Things
• Smart Cities
• Digital Transformation
• +7 more

Bocchi Yann serves as an Ordinary Professor at HES-SO Valais-Wallis' School of Management within the Economy and Services faculty. His primary appointment resides in the Computer Science Institute where he leads research in Internet of Things, Smart Cities, and Digital Transformation. His work bridges technical innovation with practical urban applications through multiple EU and Swiss-funded projects. His research focuses on IoT architecture design, context-aware systems, and human-centered smart environments. Key contributions include nomadic sensing platforms for cost-effective smart city implementation, geo-aware security protocols for cross-border data exchange, and semantic middleware for heterogeneous technology integration. His work demonstrates particular expertise in translating theoretical IoT concepts into deployable urban solutions through the SmartSantander testbed and other large-scale validations. His publication portfolio reveals consistent focus on IoT interoperability (2014-2017), evolving toward human-centric applications (2015-2016) and energy-aware systems (2020). The research trajectory shows progression from foundational protocol development toward practical smart city implementations with emphasis on data valorization and user experience. As principal investigator, he has secured significant research funding including 820,415 CHF for the EU IoT6 project and 50,000 CHF for GeoTrust from the Hasler Foundation. His collaborative approach spans academic institutions, Swiss cantons, and industry partners including Nokia Research Labs and PostAuto Schweiz AG. He directs the Management Information Technology program while teaching Systems Modeling in the BSc HES-SO in Management Information Systems. His industry partnerships demonstrate strong technology transfer capability, particularly in tourism management systems and cultural heritage applications.

Takako AikawaView profile

Senior Lecturer

• Technology in language education
• Natural Language Processing (NLP)
• AI in language learning
• +4 more

Takako Aikawa is a Senior Lecturer in Japanese at the Massachusetts Institute of Technology (MIT), where she leads the Japanese language curriculum and teaches across all proficiency levels. Her research focuses on integrating technology into language education, including virtual reality (VR) for immersive learning and AI-driven error correction in written Japanese. Prior to joining MIT in 2013, she held academic positions in Japan and the U.S., and contributed to Microsoft Research's work in machine translation and NLP. Education includes a PhD and MA from Ohio State University (U.S.) and a BA from Tsuda University (Japan). Her pedagogical interests emphasize corpus-based learning, AI applications in language education, and leveraging VR for kinesthetic learning experiences. She frequently presents at conferences on Japanese language pedagogy and has authored chapters in edited volumes such as ICT×日本語教育 and Digital Resources for Learning Japanese . Her technical work spans statistical machine translation systems, terminology consistency validation, and collaborative translation frameworks. She has pioneered projects like Pecha, an LLM-based language learning tool, and VR child avatars to enhance adult language acquisition. Current initiatives explore AI's role in identifying and correcting common learner errors in writing.