Find Academic Supervisors

• Computer Vision
• Human Mobility Modeling
• Infrastructure Monitoring
• +4 more

Takehiro Kashiyama is an Assistant Professor at the Institute of Industrial Science, University of Tokyo, specializing in civil engineering with a focus on infrastructure monitoring and urban mobility systems. His work bridges computer vision, machine learning, and civil infrastructure applications. Dr. Kashiyama's research interests center around Computer Vision applications for infrastructure monitoring , Human Mobility Modeling , and Disaster Response Systems . His work develops innovative approaches for road damage detection using deep learning techniques, traffic flow reconstruction from moving camera systems, and human mobility modeling for urban planning and disaster scenarios. His research integrates cutting-edge computer vision techniques with practical civil engineering applications. His publication record shows a consistent focus on applying AI techniques to civil infrastructure problems, with a particular emphasis on road damage detection systems and human mobility modeling. His work often involves collaboration with Yoshihide Sekimoto and other researchers at the University of Tokyo. Dr. Kashiyama has made significant contributions to road damage detection challenges, human mobility modeling using agent-based approaches, and traffic flow analysis systems. His work has been published in journals such as Computer-Aided Civil and Infrastructure Engineering, Sensors, and IEEE conferences including Big Data and ITSC.

Gjoreski HristijanView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Healthcare Technology
• +3 more

Gjoreski Hristijan is an Assistant Professor at the Faculty of Electrical Engineering and Information Technologies (Ss. Cyril and Methodius University in Skopje), with expertise in Artificial Intelligence and Machine Learning. He leads research in healthcare technology, focusing on developing intelligent systems using wearable sensors for activity recognition, fall detection, stress monitoring, and elderly care applications. His work bridges theoretical machine learning with practical healthcare solutions. PhD (2015): Jožef Stefan International Postgraduate School, Slovenia MSc (2011): Jožef Stefan International Postgraduate School, Slovenia BSc (2010): Ss. Cyril and Methodius University, Faculty of Electrical Engineering and Information Technologies, Skopje Research interests include: Machine learning for healthcare Wearable sensor systems Activity recognition algorithms Federated learning for privacy-aware applications Smart glasses integration for health monitoring His awards highlight impactful contributions, including first-place wins in international competitions like ChallengeUP (2019) and EvAAL (2013), and the prestigious Best Young Scientist Award (2017) from the President of Macedonia. Recent work focuses on OCOsense smart glasses for facial expression monitoring and real-time health diagnostics. He actively collaborates with institutions like the University of Sussex (UK) and Jožef Stefan Institute (Slovenia), advancing interdisciplinary research in AI and healthcare.

• Machine Learning
• Distributed Systems
• Sensor Networks
• +7 more

Miloš Stanković is a Professor at the Faculty of Informatics and Computing, Singidunum University. His research focuses on distributed systems, reinforcement learning, machine learning applications, and sensor networks. He has contributed significantly to areas like multi-agent systems, optimization algorithms, and cybersecurity. Recent work includes distributed sensor network calibration, adaptive consensus algorithms, and AI-driven solutions for healthcare and infrastructure monitoring. Education: Not explicitly listed in provided texts. Likely holds advanced degrees in computer science or electrical engineering based on his publications. Research Interests: His primary areas include distributed multi-agent reinforcement learning, metaheuristic optimization for machine learning models, and real-world applications of AI in healthcare, surveillance, and smart infrastructure. He explores topics like sensor fusion, fault detection in networks, and consensus-based algorithms for decentralized systems. Article Trends: Recent publications emphasize AI-driven solutions (e.g., Parkinson’s disease detection via LSTM tuning, student dropout analysis with explainable AI) and distributed algorithms for multi-agent systems. His work bridges theoretical advancements in reinforcement learning with practical applications in sensor networks and cybersecurity. Grants/Projects: Not explicitly detailed, but involvement in EU-funded projects like Sinteza and SITCON conferences suggests external funding. Labs/Teams: Likely part of Singidunum’s research groups in AI, distributed systems, and cybersecurity. Collaborates with institutions like KTH Royal Institute of Technology (via co-author K.H. Johansson) on sensor networks and control systems.

Aaron D LantermanView profile

Professor

• Radar Systems
• Image Reconstruction
• Target Recognition
• +2 more

Aaron D Lanterman is Professor at Georgia Institute of Technology's School of Electrical and Computer Engineering. He holds triple B.S. degrees in music, computer science and electrical engineering (1993), plus M.S. (1995) and D.Sc. (1998) in electrical engineering from Washington University. Research spans radar systems, image reconstruction, target recognition, music synthesis, and signal processing. Recent work focuses on statistical inference methods, astronomical imaging, audio circuit analysis, and radar target identification. Honors include the 2006 Richard M. Bass Teaching Award and NIC Certificate of Excellence for intelligence community contributions. Active in IEEE and SPIE societies with chair roles at SPIE Aerosense conference. Industry experience includes concert promotion, recording engineering, and editorial roles in music publications. Maintains keyboard/guitar performance practice alongside technical research.

• Agricultural Robotics
• Computer Vision
• Machine Learning
• +4 more

Gert Kootstra is an Associate Professor in Agricultural Biosystems Engineering at Wageningen University & Research. His research focuses on robotics, computer vision, and machine learning applied to agricultural challenges, including plant phenotyping, UAV path planning, and automated harvesting systems. He leads projects involving 3D plant reconstruction, multi-object tracking in greenhouses, and deep learning for agricultural automation. His work bridges theoretical advancements with practical applications in precision agriculture and autonomous agricultural systems. Education: Not explicitly stated in the text, but his academic rank implies advanced qualifications in agricultural engineering or robotics. Research Interests: Robotics, computer vision, deep learning, UAV-based monitoring, plant phenotyping, and autonomous systems for agriculture. His projects often involve collaboration with industry and other academic institutions to solve real-world agricultural challenges. Recent Trends in Articles: Recent publications emphasize 3D plant segmentation, UAV navigation, and machine learning for object tracking in complex environments. He explores adaptive path planning for drones, automated harvesting techniques using learning from demonstration, and frameworks for uncertainty-aware object assessment in uncontrolled settings. Advising & Grants: Supervises multiple PhD candidates (e.g., Yuan J., Cakaj H., Versmissen T.) in projects funded by grants related to plant phenotyping, UAV applications, and robotics. Collaborates on initiatives like the PicknPack project for automated food processing. Labs & Teams: Involved in robotics and agricultural technology groups at Wageningen, focusing on developing tools for precision agriculture and sustainable farming practices.

Yvan PetillotView profile

Professor

• Marine Robotics
• Autonomous Systems
• Sensor Processing
• +4 more

Yvan Petillot is a Professor of Robotics and Autonomous Systems at Heriot-Watt University, co-academic lead of the National Robotarium, and chair of Robotics at the Scottish Research Partnership in Engineering. His expertise spans marine robotics, autonomous systems, sensor fusion, and subsea domain control. He leads a £3M grant portfolio focused on marine robotics innovations, emphasizing multi-vehicle collaboration and AI-driven autonomy. Petillot co-founded SeeByte Ltd to translate academic research into commercial solutions and pioneered EU robotics competitions fostering talent. His research emphasizes underwater robotics, including navigation, perception, and intervention in hazardous environments. Notable achievements include developing advanced underwater imaging techniques, reinforcement learning platforms (e.g., MarineGym), and frameworks for digital twins in teleoperation. He has received the Royal Society Industry Fellowship (2013) and was part of the 2022 Research Team of the Year award. Key technical contributions include synthetic aperture sonar systems, underwater SLAM (Simultaneous Localization and Mapping), and control strategies for vehicle-manipulator systems. His work addresses challenges in autonomous exploration, environmental monitoring, and safe human-robot collaboration in marine and industrial settings.

• Computer Vision
• Machine Learning
• Sports Analytics
• +3 more

David Lau is a Lab Instructor/Supervisor at the University of Waterloo, specializing in computer vision and machine learning applications across sports analytics and environmental monitoring. His work focuses on developing algorithms for hockey puck localization, player tracking, and sea ice classification using SAR imagery. He contributes to datasets like AI4Arctic and explores synthetic data augmentation techniques for improving model robustness. Research interests include deep learning architectures for multi-task learning, contextual cue utilization in vision systems, and the integration of remote sensing data with climate models. Key areas of exploration involve improving hockey video analysis through homographic projections and leveraging Bayesian methods for uncertainty quantification in environmental predictions. Recent work emphasizes domain-specific adaptations like sports field localization and ice hockey event detection. His contributions span from foundational algorithms (e.g., IceGCN for SAR imagery) to practical systems like PuckNet for puck location estimation in broadcast videos. Advising and grants: No specific grants or advisees listed in available information. Collaborates on projects involving hockey analytics teams and environmental monitoring initiatives, though specific team names are not disclosed.

Michael KohleggerView profile

Professor

• Data Engineering
• Data Science
• Machine Learning
• +7 more

Prof. Michael Kohlegger is a Professor of Data Science & Engineering at MCI | Die Unternehmerische Hochschule and holds concurrent roles as Senior Lecturer in Data Science at FH Kufstein Tirol and Information Systems at WU Wien. His academic leadership includes serving as Vice-Rector of FH Kufstein Tirol (until 2024) and Director of Studies. He focuses on data storage/analysis, holistic data processing frameworks, and industry collaboration in data-driven projects. Education: Earned a Mag. (2008) and Dr. (2013) in Business Informatics from the University of Innsbruck. Prior to academia, he worked as a Business Analyst (SOS Children's Villages International) and Research Assistant (University of Innsbruck). Research emphasizes mobility systems (e.g., Vision2Move multi-object tracking, ALMODA anomaly detection) and human-technology interaction (voice assistants usability studies). Projects also include digital tourism initiatives and SME-focused data analysis models. Scientific Contributions: Co-authored works on AR work instructions, autonomous driving validation, railway monitoring systems, and process improvement frameworks. Leadership: Spearheaded projects like Digital Tourism Expert (2018–2021) and Usability of Voice Assistants (2018–2021).

• Virtual Reality
• Augmented Reality
• Haptic Feedback Systems
• +5 more

Emanuel Vonach is a PreDoc Researcher at the Institute of Visual Computing & Human-Centered Technology at Vienna University of Technology (TU Wien), specializing in virtual reality, haptics, and tangible user interfaces. His work focuses on developing innovative VR systems with robotic haptic feedback, health monitoring technologies for children, and motion capture applications. He maintains an active research profile with publications spanning from 2011 to 2025, demonstrating continuous contribution to the field of human-computer interaction. Vonach's research interests primarily center around enhancing virtual reality experiences through physical interaction. His work explores how robots can provide haptic feedback in VR environments, developing systems like CoboDeck (a large-scale haptic VR system using collaborative mobile robots) and Action-Origami inspired haptic devices. He has also pioneered applications in health monitoring for children through projects like MediCubes, which transforms medical examinations into engaging storytelling experiences using tangible interfaces. His research bridges the gap between theoretical computer science and practical applications in healthcare, education, and emergency response. His publication record shows a clear progression from foundational work in motion capture and tracking systems to increasingly sophisticated haptic VR applications. Recent publications focus on environment-independent locomotion prediction for haptic VR and safety monitoring systems for encountered-type haptics, indicating a maturation of his research toward more complex, real-world applications. The consistent funding from organizations like the Austrian Research Promotion Agency (FFG) and European Commission demonstrates recognition of his work's significance. Vonach actively supervises graduate students, with thesis topics covering mobile collaborating robots for haptics, in-situ questionnaires for haptic experience, and visual assessment with eye-tracking displays. His role as a PreDoc Researcher with FWF funding suggests he is either completing or has recently completed his doctoral studies while maintaining an active research program. His collaborations with Professor Hannes Kaufmann and other researchers at TU Wien form a cohesive research group focused on advancing VR and haptic technologies.

François BérardView profile

Associate Professor

• Human-Computer Interaction
• Real-Time Computer Vision
• 3D Interaction
• +5 more

François Bérard is an Associate Professor at Grenoble INP - Ensimag , part of the LIG laboratory 's Computer-Human Interaction group. His research focuses on real-time computer vision for human-computer interaction, 3D interaction, and touch interaction latency. He holds a PhD from Joseph Fourier University and has held visiting roles at MIT Media Lab and Xerox PARC. Education: PhD in Computer Science (1995-1999), Joseph Fourier University DEA in Computer Science (1993-1994), Joseph Fourier University MST ESI (1991-1993), Joseph Fourier University Research Interests: His work spans interactive surfaces, augmented reality, and the sensory-motor basis of natural interaction. Notable projects include the 'Magic Table' and contributions to the SmartSpaces SIGGRAPH demo. Publications: Over 20 peer-reviewed articles since 1996, with recent focus on latency effects in touch interaction and handheld perspective-corrected displays. Key contributions address user performance under latency, 3D interaction techniques, and AR/VR applications. Awards: Honorable Mention at ACM CHI 2017, Best Demonstration at IEEE ISMAR 2018, and recognition for early work on facial tracking (PR00). Labs/Teams: Active in LIG's IIHM group and collaborates with teams at MIT and Grenoble INP on perceptual interfaces and interactive systems.

Iana PodkosovaView profile

Researcher

• Virtual Reality (VR)
• Augmented Reality (AR)
• Mixed Reality (MR)
• +5 more

Iana Podkosova is a PostDoc Researcher at the Technical University of Vienna, affiliated with the Mixed Reality Lab. Her work focuses on Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) systems, with an emphasis on multi-user collaboration, embodiment, and real-world applications in construction and industrial design. She contributes to projects like BIMFlexi-VR, which integrates Building Information Modeling (BIM) with VR for early-stage industrial building design, and VRNetzer, a platform for interactive network analysis in VR. Her research interests include collaborative VR systems, hand tracking technologies, user interaction design, and the application of MR in education and industry. She has led or contributed to numerous research projects, including GeoSemanticCrow, Circular Twin, and BIMCheck, addressing challenges in spatial awareness, navigation, and real-time optimization in immersive environments. Podkosova's work often bridges technical innovation with human-centric design. Notable contributions include studies on mutual collision avoidance in multi-user VR, hybrid sound models for immersive audio games, and methods for large-scale VR system localization using point cloud registration. Her research has been published in top-tier conferences and journals, including Frontiers in Virtual Reality and IEEE proceedings. She has also been involved in educational initiatives, such as developing VR courses using consumer hardware like the HTC Vive and Leap Motion. Her interdisciplinary approach addresses both technical and social aspects of immersive technologies, aiming to enhance collaboration and decision-making in professional settings.

Yun Raymond FuView profile

Professor

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +4 more

Yun Raymond Fu is the COE Distinguished Professor of Electrical and Computer Engineering at Northeastern University, with a joint appointment in the Khoury College of Computer Sciences. His research focuses on Artificial Intelligence, Machine Learning, Computer Vision, and Big Data Analytics, leading the SMILE Lab. He holds a PhD from the University of Illinois at Urbana-Champaign and joined Northeastern in 2012. His work spans anomaly detection, video analysis, and multimodal learning, with over 200 publications in top journals and conferences. Education: PhD in Electrical and Computer Engineering, University of Illinois, 2008. Research Interests: Machine Learning, Computer Vision, Pattern Recognition, Deep Learning, and Cyber-Physical Systems. His lab develops algorithms for video anomaly detection, social media analytics, and domain adaptation. Awards: Includes 7 Young Investigator Awards, 12 Best Paper Awards, and fellowships from IEEE, IAPR, AAAS. Recently recognized as a AAAI Fellow and recipient of the Edward J. McCluskey Technical Achievement Award (2024). Labs & Entrepreneurship: Founded AI startup Giaran (acquired by Shiseido in 2017). SMILE Lab explores cutting-edge AI applications in healthcare, autonomous systems, and multimedia.

David MercierView profile

Professor

• Information Fusion
• Belief Functions
• Uncertainty Management
• +3 more

David Mercier is a Professor at the University of Artois, France, affiliated with the LGI2A laboratory and the IUT de Béthune's Department of Networks and Telecommunications. He holds a HDR (Habilitation à Diriger des Recherches) from 2015 and a PhD from the University of Technology of Compiègne (UTC), France (2006). His research focuses on information fusion, belief functions, and uncertainty management, with applications in electrical engineering, transportation systems, and machine learning. Education DEA (M.S. Equivalent) in Artificial Intelligence, University Paul Sabatier, Toulouse (2003) PhD in Information Fusion for Postal Address Recognition, UTC (2006) HDR (Research Habilitation), University of Artois (2015) Research & Awards Notable contributions include work on contextual belief function corrections, vehicular network information fusion, and multi-target tracking. He received a co-best student paper award at BELIEF 2016. His research spans electrical machines diagnosis, stochastic simulations, and deep learning integration with Dempster-Shafer theory. Students & Involvement Supervised PhD theses include studies on evidential deep learning and motor fault diagnosis. Active in the Belief Functions and Applications Society (board member since 2010). Co-edited special issues in International Journal of Approximate Reasoning and authored book chapters on belief function applications. Labs & Teams Primary affiliation with LGI2A laboratory, collaborating with institutions like LSEE and industrial partners such as Solystic and SNCF. Research integrates theoretical advances with practical applications in smart mobility and fault detection.

• Biological Sensors
• Semiconductor Device Technology
• Infrared Sensing and Imaging
• +3 more

Brett Nener is a Professor in the School of Engineering (Electrical, Electronic and Computer Engineering) at the University of Western Australia. With an h-index of 23 and 24 h-index citations, he has contributed to 189 publications and 27 grants. His expertise spans III-nitride-based sensors, semiconductor devices, and infrared imaging, aligning with UN Sustainable Development Goals for sustainable technology innovation. Research Interests : Biological sensors using III-nitrides, semiconductor device technology, atmospheric optical propagation, and infrared sensor systems. His work emphasizes sensor stability, material characterization, and adaptive optical systems. Grants & Collaborations : Leads projects on missile detection via multispectral imaging, laser communication ground stations, and cloud climatology for satellite communications. Collaborates with institutions like the Defence Science and Technology Group and SmartSat CRC. Labs & Teams : Involved in MEMS spectrometer development and interdisciplinary teams focusing on sensor integration and optical communication systems. His research bridges material science, robotics, and bioinformatics.

• Electrical and Electronic Engineering
• Artificial Intelligence and Image Processing
• Distributed Computing
• +1 more

Dr. Du Yong Kim is a Senior Lecturer in the School of Engineering at RMIT University, Australia. His research focuses on Electrical and Electronic Engineering, Artificial Intelligence and Image Processing, Distributed Computing, and Biomedical Engineering. He is actively involved in supervising research projects, including topics like mental health monitoring using wearables, decentralized multi-platform search algorithms, and sensor fusion for autonomous systems. His work spans advanced tracking algorithms, autonomous systems, and biomedical visualization tools such as CellTrackVis. Recent publications emphasize innovations in multi-object tracking, radar systems, and optimization frameworks for target localization. Dr. Kim collaborates on projects with organizations like CSIRO and has a strong record of industry-relevant research. Key research trends include the application of possibility theory to autonomous search, integration of machine learning in tracking systems, and development of sensor fusion techniques for maritime and indoor environments. His contributions bridge theoretical advancements with practical implementations in fields like healthcare monitoring and autonomous robotics.