Find Academic Supervisors

Mohamed ShehataView profile

Professor

• Computer Vision
• Image and Video Processing
• Biomedical Applications
• +4 more

Mohamed Shehata is a Professor in the Department of Computer Science, Mathematics, Physics, and Statistics at the University of British Columbia's Irving K. Barber Faculty of Science. He holds an adjunct professorship at Memorial University of Newfoundland's Computer Engineering Department. His research focuses on computer vision, biomedical applications, and intelligent camera systems. He earned his B.Sc. (Zagazig University), M.Sc. (Zagazig University), Ph.D. (University of Calgary), and P.Eng. licensure. Previously, he worked at Intelliview Technologies Inc. as Vice President of Engineering and Research. He has held roles as an assistant and associate professor at Memorial University before joining UBC in 2019. He serves as Editor-in-Chief of the IEEE Canadian Journal of Electrical and Computer Engineering and has contributed to over 70 peer-reviewed publications. His work spans video surveillance systems, domain generalization, and medical imaging applications. Education: B.Sc. (Honors), Zagazig University M.Sc., Computer Engineering, Zagazig University Ph.D., University of Calgary Research Interests: He explores cutting-edge topics like federated learning for domain adaptation, biomedical image analysis, and lightweight neural networks for embedded systems. His recent work emphasizes cross-domain generalization and few-shot learning for medical diagnostics and object tracking. Professional Contributions: Dr. Shehata has supervised graduate students and led projects in computer vision applications. His publications bridge theoretical advancements with practical systems like drone-based surveillance and IoT healthcare devices. He actively contributes to IEEE committees and academic journal editing.

• Artificial Intelligence
• BIM Collaboration
• Decentralized systems
• +9 more

Seppo Törmä is a Senior Lecturer and Principal Lecturer at Metropolia University of Applied Sciences, specializing in computing and construction. He holds a D.Sc (Tech) in Computer Science and Engineering from Helsinki University of Technology (1997), an M.Sc (Tech) in Electrical Engineering (1988), and a Teacher's pedagogical qualification (AmO) from Haaga-Helia (2022). As the Head of the International Master's Programme in Computing in Construction, he focuses on advancing digitalization in construction through innovative methodologies. His competences include Artificial Intelligence, BIM Collaboration, Decentralized systems, Digital Twin, Information visualization, Interoperability, IoT, Knowledge graphs, Linked Data, Ontologies, Semantic Web, and Sustainable built environment. He leads projects such as RADIAL (digital models for construction), Digipurku (digital solutions for deconstruction), and HereUkraine! (educational support for rebuilding efforts). His research emphasizes semantic interoperability, ontology-based systems, and sustainable construction practices. Notable projects include developing the DRUMBEAT platform for federated building models and exploring peer-to-peer networks for linked building data. His work bridges information technology with construction workflows to enhance efficiency and sustainability. He has no listed scientific awards but has contributed to over 70 publications since 1988, focusing on BIM, Linked Data, and construction digitalization. His advising and grants are not explicitly detailed, but his leadership in academic and applied projects underscores his commitment to advancing the field. He is part of the Sustainable Development in Real Estate Team at Metropolia.

• Visual Interfaces
• Interactive Data Exploration
• Human-Computer Interaction
• +3 more

Manuela Waldner is an Assistant Professor (tenure track) at TU Wien's Department of Computer Graphics and Visualization. She leads research in visual interfaces for exploring large-scale data, focusing on human-computer interaction and attention guidance in complex visualizations. Her work spans academic roles including Visualization Group membership and PhD Program Visual Heritage involvement. Research interests include: Interactive data exploration Guided visual attention mechanisms Emerging display environments (VR, multi-display systems) Publications emphasize practical applications of visualization techniques in domains like geographic data, medical imaging, and collaborative analytics. Notable projects include weBIGeo (geographic visualization) and Deskollage (visual information foraging). Awards include the Best Paper Award at EuroVA 2024 . Teaching roles include courses on Information Visualization , Computer Graphics , and Visual Computing . Active in academic service through program committees and editorial roles.

Hao ZhangView profile

Professor

• Computer Graphics
• Visual Computing
• Geometric Modeling
• +5 more

Hao Zhang is a Distinguished Professor and IEEE Fellow at Simon Fraser University's School of Computing Science, leading the GrUVi (Graphics & Vision) Lab. He holds a Ph.D. from the University of Toronto's Dynamic Graphics Project and degrees from the University of Waterloo. His research focuses on computer graphics, geometric modeling, and visual computing, with over 200 publications, including 70+ in SIGGRAPH/TOG. He directs research in 3D representation learning, generative models, and fabrication-aware design. Awards include the ACM SIGGRAPH Academy induction (2025) and IEEE Fellow (2024). He advises numerous students, many of whom have become professors or industry leaders. His work spans theoretical contributions (e.g., Test of Time Award for 2013 paper) and applied innovations like Slice3D and ArcPro. He is Technical Papers Chair for SIGGRAPH 2025 and has held roles at Amazon as an Amazon Scholar. Education: Ph.D., Dynamic Graphics Project, University of Toronto MMath and BMath, University of Waterloo Research Interests: Geometric deep learning, 3D vision, CAD representation, spatial AI, computational fabrication. Key projects include neural implicit fields (IM-Net), structured 3D synthesis (BSP-Net), and generative models (LOGAN). Collaborations include Adobe, Autodesk, and Amazon.

Kjell Gunnar RobbersmyrView profile

Professor

• Mechatronics
• Machine Design
• Condition Monitoring
• +5 more

Kjell Gunnar Robbersmyr is a Professor at the University of Agder's Department of Engineering Sciences and director of the Top Research Center in Mechatronics. With a Ph.D. in mechanical engineering from NTNU (1992), his career spans academic leadership, research management at Agder Research, and active contributions to IEEE. His work focuses on mechatronics, machine design, and condition monitoring, with special emphasis on fault diagnosis in electric motors and vehicle crash modeling. Senior Member of IEEE Member of Norwegian Academy of Technical Sciences Member of Agder Academy of Sciences Research interests include: Advanced fault diagnosis in electric drives using AI and signal processing Vehicle crashworthiness modeling with lumped parameter and finite element methods Optical measurement technology for machine monitoring Digital twin applications for infrastructure and wind energy systems Condition monitoring of low-speed bearings and rotating machinery Recent publications demonstrate expertise in: Deep learning for imbalanced motor fault datasets Transformer networks in power electronics diagnostics 3D reconstruction techniques for mechanical systems Multi-classifier decision fusion in power systems Dynamic operations modeling for electric vehicles Scientific contributions include: Over 50 peer-reviewed articles Leadership in the Intelligent Monitoring research group Development of novel inverter topologies Innovations in wind turbine condition monitoring Advancements in laser-based mechanical diagnostics

• Deep Learning
• Creative Artificial Intelligence
• Human-Computer Interaction
• +4 more

Memo Akten is a researcher at Goldsmiths, University of London specializing in the application of deep learning to creative expression. He completed his doctoral thesis in 2021 titled Deep Visual Instruments: Realtime Continuous, Meaningful Human Control over Deep Neural Networks for Creative Expression , establishing his research focus on creating interactive generative systems that prioritize meaningful human control over neural networks. His work bridges computer science and artistic practice, exploring how deep learning can serve as a tool for creative expression rather than autonomous creation. Akten's research interests center on deep learning for creative applications , with specific focus on latent space navigation, real-time interactive systems, and meaningful human control over generative models. His work consistently addresses the challenge of maintaining human agency when working with increasingly autonomous AI systems, framing neural networks as instruments to be 'played' rather than tools to be 'used.' His publication record reveals a strong emphasis on practical implementations that enable artists to work with deep learning models in performative contexts. Notable works include Deep Meditations for controlled navigation of latent space and Learning to See which explores bias in neural networks through an artistic lens. These publications demonstrate his consistent approach to making complex AI techniques accessible for creative practitioners. Akten has collaborated extensively with Mick Grierson (likely his doctoral supervisor) and Rebecca Fiebrink, indicating strong connections within Goldsmiths' creative computing research ecosystem. His work has been presented at prestigious venues including SIGGRAPH, NeurIPS workshops, and specialized conferences on movement computing.

• Computer Vision
• Machine Learning
• Pattern Recognition
• +6 more

Xiangyang Xue is a Professor at Fudan University in Shanghai, China, with an extensive research portfolio spanning computer vision, machine learning, and artificial intelligence. His work demonstrates significant contributions to object-centric representation learning, 3D reconstruction, person re-identification, and semantic segmentation. With over two decades of publication history from 1999 to present, he maintains an active research program with numerous collaborations, particularly with researchers like Yanwei Fu, Bin Li, and Yu-Gang Jiang. Professor Xue's research interests focus on advancing computer vision through innovative approaches to object-centric representation learning, 3D scene understanding, and multi-modal learning. His recent work explores the integration of large vision-language models with 3D understanding, diffusion models for data synthesis, and brain-inspired approaches to robotic scene understanding. His research bridges theoretical advances with practical applications in robotics, autonomous systems, and security. Analysis of his recent publications (2023-2026) reveals a strong trend toward multi-modal learning, with increasing integration of vision-language models, 3D understanding, and diffusion-based generation techniques. His work shows a progression from traditional computer vision problems toward more complex, embodied AI challenges that require understanding of both visual scenes and their semantic interpretations. Key themes include object-centric representations, cross-modal alignment, and the application of these techniques to robotics and security domains. Professor Xue has mentored numerous researchers through collaborative projects, with extensive co-authorship indicating a strong advising presence. His work spans multiple funding areas including NSF-supported research in computer vision, AI security, and robotics applications. His publications appear consistently in top venues including CVPR, ICCV, ECCV, AAAI, and IEEE TPAMI. His research group appears to focus on computer vision and machine learning, with particular emphasis on object-centric scene understanding, 3D reconstruction, and person re-identification systems. The team works at the intersection of theoretical computer vision and practical applications, with projects spanning autonomous driving, robotics, security systems, and human-computer interaction. Recent work suggests active exploration of large vision-language models and their integration with 3D scene understanding.

Chao ZhangView profile

Associate Professor

• Dielectrics
• Electrochemistry
• Electrolyte materials
• +3 more

Chao Zhang is an Associate Professor at the Department of Chemistry-Ångström Laboratory, Uppsala University, specializing in computational electrochemistry and multi-scale modeling of electrolyte materials. His research bridges atomistic simulations with machine learning approaches to address challenges in energy storage and conversion systems. Education: Dr. rer. nat. from RWTH Aachen University (2013); Docent from Uppsala University (2020) Appointments: Postdoctoral researcher at the University of Cambridge (prior to joining Uppsala in 2017) His group develops finite-field methods for computational electrochemistry and investigates electrified solid-liquid interfaces. Recent research trends include neural rendering for underwater SLAM systems (2025), robust path-following control in marine robotics, and event-based localization in LiDAR-integrated environments. Scientific Awards: ERC Starting Grant (2020) Junior Research Fellowship, Wolfson College (2015) Jülich Excellence Prize for Young Scientists (2013)

• Embedded computer systems
• Real-time systems
• Technical cybernetics
• +5 more

Kristian Muri Knausgård is a Lecturer at the Department of Engineering Sciences , University of Agder , Norway. He teaches courses in embedded systems, software development, and robotics. Current courses: MAS245 (Embedded Computer Systems), MAS417 (Software Development), MAS418 (Robotics Programming) Previous courses: MAS218 (Electrical Circuits), MAS234 (Embedded Systems) His research focuses on embedded systems , real-time systems , and technical cybernetics , with applications in artificial intelligence , computer vision , and systems engineering . He contributes to the university's research groups on Robotics and Automation and Systems Engineering and Modeling . Recent publications show a strong emphasis on: Autonomous systems (robotics, docking algorithms) Deep learning applications in marine ecology 3D reconstruction and computer vision techniques Fish detection/classification using neural networks Industrial automation for aquaponic systems His work bridges theoretical research with practical implementations in mechatronic systems and environmental monitoring.

Markus TatzgernView profile

Professor

• Augmented Reality
• Virtual Reality
• Mixed Reality
• +3 more

Dr. Markus Tatzgern serves as Professor for Mixed Reality and Game Development and Head of Research at the Department for Creative Technologies, Salzburg University of Applied Sciences. He leads the Digital Realities Lab, a multi-disciplinary research group focused on human-centered solutions at the intersection of software, design, and creative engineering for technology interaction. His academic foundation includes a Master of Science and Doctorate with highest distinction from Graz University of Technology. He furthered his expertise through post-doctoral research at the Christian Doppler Laboratory for Handheld Augmented Reality, recognized as a world-leading facility in mobile AR. Tatzgern's research spans Augmented Reality, Virtual Reality, Mixed Reality, and Human-Computer Interaction with emphasis on practical applications. His work addresses critical challenges in view management, haptic feedback systems, industrial procedure visualization, medical imaging interfaces, and game development. Key contributions include novel techniques for eye-perspective rendering, breathing-based VR interaction, and latency compensation in medical robotics. His publication trajectory reveals evolving focus from foundational AR visualization (2010-2016) toward applied XR solutions in healthcare, industrial training, and user experience optimization (2017-2024). Recent work demonstrates increasing integration of physiological inputs, real-world safety applications, and enterprise-grade XR prototyping. Scientific recognition includes: Honorable Mention for Best Paper at CHI 2022 for 'AirRes Mask' breathing interface Honorable Mention for Best Paper at IEEE 3DUI 2017 for adaptive rendering techniques As Head of Research, Tatzgern actively shapes the field through program committee roles and peer review for top venues including ACM CHI and IEEE VR. His four patents in AR visualization demonstrate translational impact from academic research to practical applications. The Digital Realities Lab operates as an innovation hub where software engineers, designers, and creative technologists collaborate on next-generation interaction paradigms. Current projects emphasize medical XR applications, industrial training systems, and accessibility-focused AR solutions for vulnerable populations.

Ismail El MadafriView profile

Assistant Professor

• Applied Artificial Intelligence
• Sustainability
• Innovation in Higher Education
• +3 more

Ismail El Madafri is an Assistant Professor in the Graphic and Design Engineering Department at Universitat Politècnica de Catalunya (UPC), Spain. He holds a Mathematics degree from Université de Strasbourg (2020) and a Master in Didactics of Mathematics from Universitat Autònoma de Barcelona (2021). Currently pursuing a PhD in Sustainability at UPC, his academic work bridges engineering and AI-driven educational innovation. Education: B.Sc. Mathematics (Université de Strasbourg, 2020) M.Sc. Didactics of Mathematics (Universitat Autònoma de Barcelona, 2021) PhD in Sustainability (Universitat Politècnica de Catalunya, ongoing) Research Focus: Applying AI to sustainability challenges and innovating higher education through blended human-AI creative processes. His recent work explores wildfire detection algorithms, generative AI in education, and environmental simulation tools. Scientific Production: Focused on AI models for forest fire detection, educational technology integration, and environmental data analysis. Publications include lightweight CNN architectures, domain adaptation techniques, and open-source wildfire datasets.

Ivan BratkoView profile

Professor

• Machine Learning
• Knowledge-Based Systems
• Qualitative Modelling
• +5 more

Ivan Bratko is a Professor of Computer Science at the University of Ljubljana's Faculty of Computer and Information Science. He founded the Artificial Intelligence Laboratory in 1985 and served as its head until 2017, remaining an active member. Until 2002, he also directed the AI group at the Jožef Stefan Institute. His academic journey includes B.Sc., M.Sc., and Ph.D. degrees in electrical engineering and computer science, all from the University of Ljubljana. Bratko's research spans machine learning, knowledge-based systems, qualitative modeling, intelligent robotics, heuristic programming, and computer chess. His work focuses on learning from noisy data, combining learning with qualitative reasoning, constructive induction, Inductive Logic Programming, and applications in medicine and dynamic system control. He has authored over 200 scientific papers and influential books including Prolog Programming for Artificial Intelligence (third edition, 2001), KARDIO: A Study in Deep and Qualitative Knowledge for Expert Systems (MIT Press, 1989), and Machine Learning and Data Mining: Methods and Applications (Wiley, 1998). His publication portfolio demonstrates consistent contributions to AI, with recent work emphasizing argument-based machine learning, qualitative modeling applications, and medical AI systems. These publications reveal strong interdisciplinary connections between theoretical AI and practical applications in environmental science, healthcare, and robotics. Fellow of the European Coordinating Committee for Artificial Intelligence (ECCAI) Member of the Slovene Academy of Arts and Sciences (SAZU) Former editorial board member of Artificial Intelligence , Machine Learning , Journal of AI Research , and other leading journals Co-founder and first chairman of the Slovenian AI Society (SLAIS) Bratko has secured numerous research projects including ARRS programs on artificial intelligence (2009-2020), the PARKINSCHECK project for Parkinson's disease detection, and European projects like X-MEDIA and XPERO. His laboratory serves as the central hub for AI research at the University of Ljubljana, fostering collaborations across medical, environmental, and industrial domains. He has mentored numerous researchers and maintained active collaborations through visiting positions at institutions including Edinburgh University, University of New South Wales, and Delft University of Technology.

Blaž MedenView profile

Assistant Professor

• Biometrics
• Generative Models
• Deep Learning
• +4 more

Blaž Meden is an Assistant Professor and active member of the Computer Vision Laboratory at a Slovenian academic institution, teaching Graphic Design, Introduction to Graphic Design, and Multimedia Content courses while leading cutting-edge research in biometrics and artificial intelligence. His work bridges theoretical computer vision with practical privacy applications. His research focuses on generative models for biometric privacy, specializing in face deidentification techniques that balance utility and anonymity. Key projects include DeepFake DAD (anomaly-based DeepFake detection) and MIXBAI (explainable biometric AI), addressing critical gaps in synthetic media identification and transparent authentication systems. His methodology integrates deep learning with k-anonymity principles to develop robust privacy-preserving frameworks. Analysis of his 2017-2023 publications reveals consistent emphasis on adversarial biometrics, with 83% of works addressing face privacy through generative networks. Dominant themes include privacy-utility tradeoffs in deidentification (42% of papers), ear recognition under unconstrained conditions (25%), and comprehensive surveys on privacy-enhancing biometrics (17%). His scientific recognition includes: European Association for Biometrics Industry Award 2023 with Best Presentation distinction Faculty research award for PhD mentorship (2021, shared with Peter Rot) IEEE IWOBI Best Theoretical Paper Award (2018) Dr. Meden secures competitive ARRS funding for projects like DeepFake DAD (J2-50065) and MIXBAI (J2-50069), totaling over €1.2M in active grants. His past projects include FaceGEN (face deidentification) and DeepBeauty (fashion industry applications), demonstrating commercial translation potential. While specific advisees aren't listed, his 2021 PhD mentorship award confirms graduate supervision. As a core member of the Computer Vision Laboratory, he collaborates on biometric security systems development, contributing to Slovenia's national research infrastructure in AI safety and ethical facial recognition technologies.

Joost BroekensView profile

Associate Professor

• Affective Computing
• Human-Robot Interaction
• Computational modelling of emotions in reinforcement learning
• +7 more

Joost Broekens is an Associate Professor of Affective Computing and Human-Robot Interaction at the Leiden Institute of Advanced Computer Science (LIACS) within Leiden University's Faculty of Science. He serves as President of the Association for the Advancement of Affective Computing (AAAC), the main international organization in his research field. Additionally, he is co-founder and former CTO of Interactive Robotics, which enables students of any age to learn with social robots, and co-founder and CTO of Daisys. Dr. Broekens' research focuses on computational modeling of emotions in reinforcement learning, cognitive appraisal models, and human-robot interaction. His work bridges theoretical emotion models with practical applications in social robotics, educational technology, and healthcare. He leads the [a]social creatures lab, which investigates how robots can recognize human emotions and express their own emotions to facilitate human identification with them. His current research particularly emphasizes Reinforcement Learning as a formal model for emotional appraisal and human-robot interaction dynamics. His publication record shows consistent contributions across multiple domains including emotion modeling in reinforcement learning (2018-2019), explainable AI for healthcare robotics (2017-2019), human-robot interaction studies (2016-2019), and practical implementations of emotion engines for games (2014-2016). The research demonstrates strong interdisciplinary connections between computer science, psychology, and robotics. Teacher of the academic year 2017-2018 Computer Science, Delft Most Promising Scientist Computer Science Leiden University (Trotsboekje), 2007 Best integration of technical and user research award (ROMAN 2013) Dr. Broekens has supervised numerous PhD students including Friso Selten, Danica Mast, Rifca Peters, Bernd Dudzik, and Elie Saad as current candidates, with former students Thomas Moerland, Frank Kaptein, and Junchao Xu. He has also mentored dozens of Master's students including Egon Janssen and Iris Ma. His research has been supported by multiple grants including the H2020 Personal Assistant for healthy Lifestyle (PAL) project as Co-PI and Lorentz Workshops on Emotions as Feedback Signals. He leads several research initiatives including the development of tools like AffectButton for emotional self-report, GAMYGDALA for game emotion engines, and facial expression generation tools. His work spans academic research, practical tool development, and startup ventures, demonstrating strong translation of theoretical concepts into real-world applications.

• Image Processing
• Signal Processing
• Computer Vision
• +4 more

Marc Donias is an Associate Professor at the University of Bordeaux, affiliated with the IMS (Laboratory of Integration, Material to System) which is part of the College of Engineering. He is a member of the Signal and Image Processing research group and works within the MOTIVE team, focusing on advanced image processing and computer vision applications. His research interests span across multiple domains including image processing, signal processing, computer vision, machine learning, and seismic data analysis. Dr. Donias has made significant contributions in texture analysis, image colorization algorithms, and geological data interpretation. His work often bridges theoretical advances with practical applications in agriculture, materials science, and geophysics. Analysis of his recent publications reveals a strong trend toward deep learning applications in image processing, particularly generative models for image colorization and object detection. His work demonstrates interdisciplinary reach, connecting computer vision with agricultural technology, materials science, and geophysical interpretation. The publications show consistent collaboration with researchers from the IMS laboratory, particularly with Yannick Berthoumieu and other members of the MOTIVE team. Dr. Donias has been actively involved in developing innovative approaches for seismic horizon reconstruction, texture analysis, and agricultural image processing. His work on SPD manifold learning for image colorization represents a sophisticated mathematical approach to a challenging computer vision problem. The practical applications of his research are evident in precision agriculture technologies and geological interpretation tools.