Find Academic Supervisors

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +5 more

Ying Jiang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Data and Computer Science. With an extensive publication record spanning from 1995 through projected 2026 papers, Dr. Jiang has established herself as a leading researcher in interdisciplinary AI applications. Her work bridges theoretical advances with practical implementations across medical imaging, remote sensing, transportation systems, and virtual reality. Dr. Jiang's research focuses on developing novel algorithms in computer vision and machine learning with applications in diverse domains. Her work emphasizes attention mechanisms in deep learning, sensor fusion techniques, physics-based simulations, and predictive modeling. Key contributions include breast MRI classification systems, LiDAR-camera calibration methods, cloud workload prediction models, and 3D outfit simulation frameworks. Her research group consistently publishes in top venues including IEEE Transactions, ACM Transactions, and CVPR. Analysis of Dr. Jiang's recent publications reveals a strong trend toward practical AI applications with real-world impact. Her work spans medical diagnostics (breast cancer detection, liver injury monitoring), environmental monitoring (tunnel mapping, infrared target detection), transportation optimization (vessel scheduling, adaptive platoons), and virtual reality (3D outfit simulation). The consistent theme across these diverse applications is the development of efficient, accurate AI models that operate within practical constraints. Dr. Jiang has mentored numerous students and junior researchers, with frequent collaborators including Tianyi Xie, Chang Yu, Xuan Li, and Ziran Zuo appearing across multiple publications. Her research group demonstrates expertise spanning computer graphics, physics-based simulation, and machine learning, with strong connections to medical institutions, transportation authorities, and technology companies. While specific grant details aren't provided in the publication records, the interdisciplinary nature of her work suggests funding from multiple sources focused on AI applications in healthcare, transportation, and environmental monitoring.

• Cybersecurity
• Authentication Systems
• Blockchain Technology
• +6 more

Jun-Ho Huh is a Professor at Sungkyunkwan University's College of Information and Communication Engineering, Department of Computer Science and Engineering. His extensive research portfolio demonstrates leadership in cybersecurity, authentication systems, and AI applications across multiple domains including financial technology, maritime security, and agricultural technology. Dr. Huh's research interests span cybersecurity, authentication systems, blockchain technology, artificial intelligence, edge computing, and IoT security. His work bridges theoretical computer science with practical applications in smart systems engineering. His research has significant implications for improving security in emerging technologies like DeFi, autonomous systems, and smart infrastructure. Analysis of Dr. Huh's recent publications reveals a strong focus on practical applications of AI and security technologies. His work covers diverse areas including cybersecurity for maritime autonomous systems, authentication mechanisms, financial technology applications, and agricultural technology solutions. The publications demonstrate consistent productivity and relevance in addressing contemporary challenges in computer science and engineering. Dr. Huh has made significant contributions to authentication systems research, particularly in biometric and behavioral authentication methods. His work on keystroke dynamics, voice spoofing detection, and wearable device authentication has advanced the field of usable security. His research extends to practical applications in smart factories, agricultural technology (AgTech), and financial technology. The integration of blockchain with AI for various applications represents a significant thread throughout his recent work, demonstrating interdisciplinary approaches to solving complex technological challenges.

• Virtual Reality
• Human-Computer Interaction
• Mulsemedia
• +3 more

Alexandra Covaci is an Associate Professor in the Department of Computer Science at the University of Bedfordshire, specializing in virtual reality, human-computer interaction, and mulsemedia (multisensory media) systems. With a consistent publication record spanning over a decade, her research focuses on enhancing user experiences through multisensory integration in virtual environments, with particular applications in healthcare, accessibility, and social interaction. Her research interests center on virtual reality systems, human-computer interaction paradigms, mulsemedia technologies, accessibility applications, and dementia care interventions. Dr. Covaci's work bridges technical innovation with practical human-centered applications, particularly exploring how multisensory feedback can enhance presence and usability in virtual environments. She has made significant contributions to understanding cross-modal correspondences in multimedia experiences and developing practical applications for vulnerable populations. Analysis of her recent publications reveals a strong trend toward healthcare applications of VR technology, particularly for dementia care and accessibility solutions. Her work increasingly integrates physiological measurements and user-centered design principles to create more effective and engaging virtual experiences. The interdisciplinary nature of her research connects computer science with psychology, healthcare, and social sciences. Multiple publications in IEEE Transactions on Visualization and Computer Graphics Regular contributions to ACM CHI and DIS conferences Editorial work for Multimedia Tools and Applications journal Dr. Covaci actively collaborates with an international network of researchers across Europe and beyond, with frequent co-authorship with Gheorghita Ghinea, Chee Siang Ang, and Sophia Ppali. Her research has been supported by multiple grants focused on VR applications for social good, though specific funding details aren't visible in the publication record. She appears to supervise PhD students working at the intersection of VR, accessibility, and healthcare applications. Her laboratory work focuses on developing and evaluating mulsemedia systems, with particular emphasis on creating standardized testing methodologies for multisensory experiences. Current projects appear to be exploring the integration of VR technologies in dementia care settings and developing more accessible interfaces for people with various disabilities.

• Automotive Systems
• Biomedical Engineering
• Computer Vision
• +3 more

Edward Jones is a prominent academic researcher with a focus on interdisciplinary studies at the intersection of computer vision, biomedical engineering, and automotive systems. His work spans multiple domains including image processing, sensor optimization for autonomous vehicles, and medical imaging technologies. Collaborating extensively with institutions and researchers in Ireland and beyond, he has contributed to advancing perception systems in challenging environments and improving safety-critical applications in transportation. Research Interests: Automotive perception systems, medical imaging, low-light imaging, wireless communication for vehicular networks, and algorithm optimization for embedded systems. Key Contributions: Published over 90 papers in top-tier journals/conferences such as IEEE Transactions on Intelligent Transportation Systems, Sensors, and IEEE Access. Notable work includes low-light image enhancement algorithms, cyclist safety analysis via driver gaze tracking, and optimizing camera exposure for automotive applications. Recent trends in his publications emphasize cross-disciplinary solutions for autonomous systems, leveraging advances in computer vision and signal processing to address real-world challenges like adverse weather conditions and sensor degradation. Recipient of grants for projects involving automotive networks, breast cancer detection via microwave imaging, and distributed speech recognition systems.

• Computer Vision
• Machine Learning
• 3D Object Recognition
• +4 more

Riccardo Spezialetti is a researcher at the University of Bologna's Department of Computer Science and Engineering, specializing in advanced 3D vision and deep learning. He earned his PhD in 2020 with a thesis titled 'Learning to understand the world in 3D,' focusing on geometric deep learning and 3D object representation. His work bridges computer vision, machine learning, and neural representations, with notable contributions to unsupervised domain adaptation, LiDAR processing, and neural field-based 3D reconstruction. Key research interests include equivariant descriptors, implicit neural representations, and self-supervised learning for 3D data. He collaborates frequently with Samuele Salti and Luigi Di Stefano, co-authoring over 25 publications in top venues like CVPR, ICCV, and IEEE PAMI. His research has practical applications in autonomous systems, robotics, and photorealistic 3D reconstruction.

• Computer Vision
• Machine Learning
• Medical Imaging
• +2 more

Lazaros T. Tsochatzidis is a researcher with significant contributions to computer vision , machine learning , and their applications in medical imaging and autonomous driving . He has published extensively in top-tier venues like Pattern Recognition , IEEE Access , and Robotics and Autonomous Systems , often collaborating with researchers such as Ioannis Pratikakis and Konstantinos Zagoris . Research Interests Specializes in LiDAR-based 3D object detection for autonomous vehicles Developed domain adaptive object detection techniques using mean teacher models Innovated part-aware graph fusion for skeleton-based action recognition Expert in medical image analysis for breast cancer diagnosis Contributed to historical document transcription and sclera segmentation research Scientific Awards No explicit awards mentioned in available data Notable Collaborations Long-term collaboration with Konstantinos Zagoris (6 co-authored papers) Extensive work with Ioannis Pratikakis (11 co-authored publications) Multidisciplinary collaborations with experts in medical imaging , autonomous systems , and biometric security

• Bioinformatics
• Computational Biology
• Machine Learning
• +3 more

Hong Qin is a Professor affiliated with the University of Tennessee at Chattanooga, USA, and previously with Tuskegee University's Department of Agricultural and Environmental Sciences. His research spans computational biology, machine learning, environmental informatics, and healthcare data science. He has co-authored over 20 papers since 2007, focusing on topics like viral fitness modeling, optimization algorithms, and AI applications in healthcare diagnostics. Research interests emphasize interdisciplinary approaches, including bioinformatics for viral mutation analysis, swarm intelligence for optimization problems, and explainable AI for medical imaging. Recent work includes federated learning for healthcare privacy, blockchain interoperability, and environmental monitoring via satellite imagery. His publications reflect a strong focus on AI-driven solutions for global health challenges, such as SARS-CoV-2 variant analysis and equitable AI frameworks for public health. He has contributed as an editor for journals like IEEE ACM Transactions on Computational Biology and Bioinformatics, highlighting his role in shaping bioinformatics research.

Maarten BuisView profile

Lecturer

• Social Stratification
• Educational Attainment
• Statistics
• +3 more

Maarten Buis is a Lecturer of Statistics for the Social Sciences at the Department of Sociology, University of Konstanz. Previously, he held positions such as Senior Research Fellow at the WZB Berlin Social Science Center and Research Fellow roles at Eberhard Karls Universität Tübingen and VU University Amsterdam. He earned his PhD from VU University Amsterdam in 2010 with a thesis on educational inequality in the Netherlands. Education: PhD in Social Science (2010), VU University Amsterdam Master's in Social and Institutional Economics (2003), University of Utrecht His research focuses on social stratification, educational attainment, statistical methodologies, and the application of Stata software. He has contributed significantly to statistical techniques in non-linear models and has developed several Stata packages. His publications span peer-reviewed articles in journals like European Sociological Review and The Stata Journal , as well as edited volumes. He frequently presents at international conferences and Stata Users' Group meetings, focusing on topics like agent-based models and statistical software best practices. Labs/Teams: Former member of the Research Department 'Skill Formation and Labor Markets' at WZB Berlin Grants/Advising: No formal student advising listed, but extensive collaborative research engagements

Ralf MöllerView profile

Professor

• Artificial Intelligence
• Humanities-Centered AI
• Causal and Probabilistic Models
• +5 more

Prof. Ralf Möller is a Professor of Artificial Intelligence in Humanities at the University of Hamburg's Faculty of Humanities, Department of Philosophy. He leads the Institute of Humanities-Centered Artificial Intelligence (CHAI) and serves as spokesperson for the 'Data Linking' research field within the Cluster of Excellence ‘Understanding Written Artefacts’ (UWA, 2019–2025). His research focuses on intellectics, causal and probabilistic-relational models, and AI applications in humanities, emphasizing sustainable data management and multimodal foundation models. He heads the Data Linking Lab and the CHAI Institute, overseeing projects like the TAMAR initiative for manuscript research. His work bridges technical AI advancements with humanities needs, addressing challenges in data curation, federated information systems, and ethical AI integration. He contributes to interdisciplinary collaborations, including ethics committees and cultural heritage preservation initiatives. Key research themes include lifted inference in probabilistic graphical models, temporal data prediction, and synergistic OCR-LLM systems for damaged documents. His projects emphasize scalability, sustainability, and human-centered design principles. Supervising doctoral candidates in AI and humanities intersections, he advocates for AI systems grounded in cultural and ethical considerations. Labs/Teams: CHAI Institute, Data Linking Lab. Current Projects: UWA Cluster (2019–2025), Data Linking Infrastructure development, Humanities-Centered AI applications. Grants include leadership roles in institutional and collaborative research funding.

Frank SteinickeView profile

Professor

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +5 more

Professor Frank Steinicke is the Chair and Head of the Human-Computer Interaction (HCI) Lab at the University of Hamburg's Department of Informatics. He leads the Human-Computer Interaction Lab within the Cluster of Excellence ‘Understanding Written Artefacts’ (UWA) , focusing on merging natural and artificial intelligence to enhance interactions in virtual environments. His research spans virtual/augmented reality, perceptual interfaces, and immersive analytics, with over 400 publications. Education: Ph.D. and Venia Legendi in Computer Science (University of Münster) Mathematics with a Minor in Computer Science (University of Münster) Research Interests: Steinicke's work emphasizes human perceptual and cognitive limitations in XR, AI integration in virtual agents, and applications in healthcare, cultural heritage, and education. Recent projects include immersive tools for analyzing ancient artifacts and exergames for older adults. Awards: IEEE VGTC VR Technical Achievement Award (2023) Induction into IEEE VR Academy (2023) Over 30+ Best Paper Awards across ACM/IEEE venues Labs/Teams: Directs the Human-Computer Interaction Lab and collaborates on interdisciplinary projects like the Mobile Lab Container and Data Linking Lab . He chairs the ACM SUI Symposium and serves on editorial boards for IEEE Transactions on Visualization and Frontiers in Virtual Reality .

• Recommender Systems
• Machine Learning
• Graph Neural Networks
• +7 more

Yiwen Zhang is a researcher affiliated with Anhui University in Hefei, China, with a focus on Recommender Systems , Machine Learning , and Graph Neural Networks . Their work spans Data Mining , Imbalanced Data challenges, and Big Data optimization. Recent publications highlight advancements in robust recommendation via heterogeneous graphs, causal feature selection , and anomaly detection in time series. Key research areas: Recommender Systems, Machine Learning, Graph Neural Networks, Data Mining, Edge Computing. Notable applications: Imbalanced Data Classification, Social Recommendation, Edge Server Placement, Causal Discovery. Their 2025 and 2024 contributions include 15+ papers on topics like parameter-free sampling , graph attention networks , and causal inference . These works emphasize robustness , uniformity , and low-cost optimization in complex scenarios. Collaborations: Lei Sang, Dengcheng Yan, Qiang He, Yun Yang, Zhaolong Ling. Published in journals: Applied Intelligence , IEEE Transactions on Knowledge and Data Engineering , Expert Systems with Applications .

• Augmented Reality
• Virtual Reality
• Human-Computer Interaction
• +5 more

Hirokazu Kato is a Professor at Kyoto University's Graduate School of Informatics, Department of Applied Mathematics and Physics. He has held academic positions at multiple institutions including Osaka University (PhD 1996), Hiroshima City University (1999-2003), and Nara Institute of Science and Technology (2003-2007). His research spans augmented and virtual reality, human-robot interaction, computer graphics, and healthcare technology. Kato has collaborated extensively with institutions like Chuo University and Nagoya University. Key research areas include AR/VR applications in social interaction, medical training, and industrial automation. He has developed systems for pain relief via robotic touch, AR-enhanced job interview training, and mixed-reality educational platforms. Over 217 publications since 1989 reflect his contributions to fields like near-eye display optimization, gesture recognition, and multimodal interaction design. Notable projects include: Augmented Reality face filters for social anxiety mitigation Huggable robots with intra-hug gesture modeling AR-based physical therapy systems using patient-specific motion simulation General software frameworks for AR industrial tasks His work bridges technical innovation with human-centric applications, emphasizing real-world usability across education, healthcare, and manufacturing domains.

• Virtual Reality
• Computer Graphics
• Human-Computer Interaction
• +3 more

Michitaka Hirose is a prominent researcher in Virtual Reality (VR) and Human-Computer Interaction (HCI), with a focus on immersive technologies, haptics, and perceptual systems. His work spans over three decades, contributing to advancements in VR navigation, multisensory feedback, and applications in education, healthcare, and entertainment. Collaborations with institutions like the University of Tokyo (inferred via publication patterns) are evident through frequent co-authorships with researchers such as Tomohiro Tanikawa and Takuji Narumi. His research emphasizes practical implementations of VR, including redirected walking systems, shape illusion controllers, and systems for motor skill learning through virtual co-embodiment. Key Areas: Virtual Reality, Haptics, Perception Models, Collaborative Training, and Immersive Displays Notable Contributions: Development of VR controllers for shape rendering (e.g., Transcalibur), studies on avatar appearance effects, and systems for remote spatial understanding Publications highlight a blend of technical innovation and user-centric design, addressing challenges in VR navigation, sensory feedback, and social interaction within virtual environments. His work often bridges theoretical research with real-world applications, such as training systems and healthcare tools.

• Virtual Reality Applications
• Clinical Psychology
• Healthcare Technology
• +3 more

Albert A. Rizzo is a prominent Professor affiliated with the University of Southern California, specifically associated with the Institute for Creative Technologies. His work focuses on pioneering applications of Virtual Reality (VR) in clinical, psychological, and healthcare contexts. Rizzo has contributed extensively to VR-based solutions for PTSD treatment, ADHD assessment, mental health diagnostics, and neurorehabilitation. His research bridges computer science, psychology, and medicine, emphasizing practical tools for patient care and military veteran support. Key areas of research include virtual exposure therapy, cognitive assessment tools, and the design of immersive systems for healthcare training. Notable achievements include the development of the BRAVEMIND platform for combat-related PTSD and the Virtual Classroom for attention deficit evaluations. Rizzo has received prestigious recognition, including the 2023 VGTC Virtual Reality Lifetime Achievement Award for transformative contributions to VR research. His interdisciplinary collaborations span academia, industry, and clinical settings, addressing challenges such as patient safety in e-mental health systems and gender differences in VR user experience. Ongoing work explores AI-driven virtual agents for mental health screening and crisis intervention strategies during global health crises like the pandemic. Awards: VGTC Virtual Reality Lifetime Achievement Award (2023) Key Projects: BRAVEMIND, Virtual Classroom, ClinicaVR Domains: PTSD therapy, ADHD diagnostics, VR in education, telemedicine

• Augmented Reality
• Computer Vision
• Virtual Reality
• +3 more

Gudrun Klinker is a Professor at the Technical University of Munich, Germany, specializing in Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR). Her research focuses on computer graphics, human-computer interaction, and the application of these technologies in education, cultural heritage, and industrial scenarios. She has contributed significantly to advancements in AR/VR systems, including real-time tracking, collaborative frameworks, and immersive simulations. Her work spans theoretical and applied research, with notable contributions to SLAM systems, cloud-based AR platforms, and neuro-robotics. She has been recognized with the VGTC Lifetime Achievement Award (2022) for her sustained impact in visualization and computer graphics. Dr. Klinker collaborates extensively with academic and industrial partners, publishing in top-tier journals like IEEE Transactions on Visualization and Computer Graphics and conferences such as ISMAR and VR. Her research emphasizes interdisciplinary applications, including gamified urban planning tools (e.g., Game.UP), underwater archaeology exploration via VR, and language-learning AR systems. She actively participates in the VR/AR community, serving on program committees and editorial boards. Her team’s work often bridges theoretical innovation with practical implementations, addressing challenges in device adaptability, real-time systems, and ethical considerations in neurorobotics.