Diego Borro is a Full Professor (Catedrático) in Computer Science and Artificial Intelligence at TECNUN, Technological Campus of the University of Navarra , where he has been part of the faculty since 2004. He is a leading researcher at CEIT since 2003, focusing on Robotics, Virtual/Augmented Reality, Computer Vision, and Artificial Intelligence. His academic credentials include a PhD in Computer Science (2003) and an MS in Computer Science (2000) from the University of Navarra and University of Basque Country respectively. His research spans from 3D tracking and haptics to industry 4.0 applications and medical robotics, with over 34 journal papers and 65 conference articles. He has supervised 14 doctoral theses and participated in 55+ research projects. His leadership roles include heading CEIT's Simulation Unit (2012-2016) and Vision and Robotics (V&R) research line (2016-2022), currently serving as main researcher at Intelligent Systems for Industry 4.0 group (SS4I4). Accredited as Full Professor (Catedrático) by ANECA (3 sexenios) Member of IEEE, ACM, and Eurographics societies Key projects: STEPbySTEP exoskeleton benchmark, WARM AR maintenance systems, and inner ear drug delivery research
Dr. Hendrik Hachmann is a researcher at the Institute for Information Processing (TNT) at Leibniz University Hannover . His work focuses on medical image processing , 3D reconstruction , and computer vision , with applications in biomedical imaging, multimedia systems, and interactive segmentation tools. He has published in top conferences like CVPRW, ISBI, and journals such as The Visual Computer . Medical Image Processing Computer Vision Biomedical Imaging 3D Reconstruction Deep Learning Multimedia Systems Hachmann studied Electrical Engineering and Information Technology at RWTH Aachen University , earning his Dipl.-Ing. degree in 2012. His PhD research at TNT explores practical applications in medical imaging and computational modeling. His publications highlight interdisciplinary trends bridging computer vision with biomedical imaging , leveraging deep learning and 3D modeling for clinical tools. Recent works address spine motion capture , electrode localization , and hair braid simulations . No scientific awards or grants are explicitly mentioned in the provided texts. His projects include Braided Hairstyle Reconstruction and Interactive 3D Segmentation , emphasizing practical applications in medical and visual computing domains.
Róbert Tóth is an Assistant Professor at the University of Debrecen's Faculty of Informatics, Department of Information Technology. His work focuses on enhancing spatial abilities through emerging technologies, including gamification and augmented reality. Contact details: toth.robert@inf.unideb.hu, Office: 2nd floor, I228 Faculty of Informatics building. Research interests include: Spatial skill assessment and training using 3D modeling and interactive tools Integration of gamification and augmented reality in educational contexts Development of open-source software frameworks for cognitive training (e.g., viskillz-blender) Analysis of transportation data (GTFS/RT) and geospatial visualization techniques Optimization of educational systems and Smart Campus services Recent publications emphasize spatial reasoning development, gamified learning environments, and efficient handling of geospatial data through Python-based tools and Blender integrations. His work bridges software engineering, educational technology, and human-computer interaction.
Dr. Nikita Araslanov is a Postdoctoral Researcher at the Technical University of Munich (TUM) in the School of Computation, Information and Technology, Department of Informatics 9 (Computer Vision Group). He also serves as a visiting faculty member at Google. His research focuses on semantic and 3D visual inference from video data, aiming to bridge perception and understanding in complex visual scenes. Dr. Araslanov earned his PhD in Computer Science from TU Darmstadt in the Visual Inference Lab, graduating with highest distinction. He holds a Master's degree in Computer Science from the University of Bonn, where he graduated with distinction in 2016. His research spans multiple areas of computer vision, with a particular emphasis on 3D reconstruction, semantic segmentation, and deep learning approaches for visual understanding. His work often combines theoretical insights with practical applications, addressing challenges in dynamic scene understanding, vision-language correspondence, and unsupervised learning paradigms. He has made significant contributions to bundle adjustment for dynamic scenes, hierarchical semantic segmentation using hyperbolic geometry, and novel approaches to unsupervised panoptic segmentation. Dr. Araslanov's research has been recognized with several prestigious awards, including being selected as a Best Paper Candidate at ICCV 2025 for his work on dynamic scene reconstruction, and having his Scene-Centric Unsupervised Panoptic Segmentation paper designated as a Highlight Paper at CVPR 2025 (top 3% of submissions). He has also received multiple oral presentation awards at major computer vision conferences including GCPR 2024, CVPR 2024, and ICLR 2024. Actively involved in the academic community, Dr. Araslanov serves as an Area Chair for CVPR 2025. He is committed to mentoring the next generation of researchers and regularly supervises master's theses, guided research projects, and research assistant positions (HiWi). His teaching includes courses on Deep Learning for Spatial AI (Summer Semester 2025) and Computer Vision 3: Segmentation, Detection and Tracking (Winter Semester 2024/25). As a member of the Computer Vision Group led by Prof. Dr. Daniel Cremers at TUM, Dr. Araslanov collaborates with a diverse team of researchers working on cutting-edge computer vision problems. The group maintains strong connections with industry partners and contributes significantly to the advancement of computer vision research through publications at top-tier conferences and journals.
Zhenzhang Ye is a researcher affiliated with the Computer Vision Group at the Technical University of Munich (TUM) , part of the TUM School of Computation, Information and Technology. His work focuses on Photometry-Based Reconstruction , Optimization , and Geometry Processing , with additional interests in Visual SLAM , Deep Learning , and Biomedicine . Research Interests : Optimization techniques, Photometry-Based Reconstruction, and geometric processing for computer vision tasks. Publications : Active contributor to conferences like CVPR, AISTATS, AAAI, and ICCV, with recent works on 3D human motion prediction, hypergradient estimation, and photometric stereo. Contact: yez@in.tum.de
Szymon Rusinkiewicz is the David M. Siegel ’83 Professor and Chair of the Department of Computer Science at Princeton University. His research bridges computation with the visual and tangible world, focusing on 3D shape, motion, and appearance. Department Chair: Computer Science University: Princeton University Research Areas: Computational fabrication, 3D scan acquisition/registration/reconstruction, machine learning for image/shape analysis, robotic localization/planning, and appearance/performance capture for digital humans. Applications include cultural heritage documentation, non-photorealistic shading models for illustrative depiction, and interdisciplinary work with robotics and machine learning.
Nils Daniel Meyer-Kahlen is a Postdoctoral Researcher at Aalto University's Department of Information and Communications Engineering in Espoo, Finland. Affiliated with the Virtual Acoustics research group and Aalto Acoustics Lab, his work bridges theoretical audio engineering with practical virtual reality applications through cutting-edge spatial audio research. His research focuses on room acoustics modeling, binaural rendering, and perceptual evaluation in virtual environments. Key interests include blind estimation of acoustic parameters, machine learning applications for audio synthesis, and the development of transfer-plausible audio for augmented reality. He investigates how humans perceive spatial audio cues and develops methods to improve authenticity in mixed reality through psychoacoustic validation. Recent publications reveal strong trends in deep learning for room impulse response generation, novel reverberation techniques like Dark Velvet Noise, and perceptual evaluation frameworks. His work consistently addresses virtual reality audio challenges including motion-to-sound latency, room transition rendering, and the impact of early reflections on spatial perception. As part of Aalto's Acoustics Lab team, Dr. Meyer-Kahlen contributes to Finland's leading spatial audio research hub known for chamber music hall studies, sauna acoustics exploration, and open dataset creation like the multi-room transition energy decay collection. The lab maintains strong industry collaborations while advancing fundamental audio science.
Dr. Marcel Köster is a researcher affiliated with the Ubiquitous Media Technology Lab at the German Research Center for Artificial Intelligence (DFKI) and the Saarland Informatics Campus. His work focuses on GPGPU computing, particle simulations, compilers, and optimization techniques. Email: Marcel.Koester@dfki.de Phone: +49 681 85775 7750 Location: Gebäude D3 1, Room 0.13, Saarbrücken Research Interests Dr. Köster's research integrates GPU computing with simulation algorithms and compiler optimization. He contributes to advancements in parallel processing, domain-specific languages, and scientific visualization through both theoretical exploration and practical implementations. His publications highlight innovative applications of GPU acceleration to heuristic optimization, state generation, and particle simulations. These works demonstrate expertise in thread compaction, shared memory utilization, and warp scheduling. Teaching Experience Dr. Köster has taught multiple courses at HBK Saar, including: Artificial Intelligence (Summer 2019) Grundlagen der Medieninformatik (Winter 2016/17) Physical Simulations on Media Facades (Winter 2015/16) Core Lecture: Compiler Construction (Winter 2013/2014)
Donald Degraen is a Lecturer at the University of Canterbury 's Human Interface Technology Laboratory (HIT Lab NZ) within the Faculty of Engineering . His research intersects haptic perception , digital fabrication , and virtual reality , focusing on physical artifacts that enhance digital experiences. Current appointments: Lecturer at HIT Lab NZ (2024-present) Education: PhD in Computer Science (2023), M.Sc. in Electrical Engineering (2012), B.Sc. in Industrial Engineering (2005) Research Expertise spans multiple domains: Human-Computer Interaction : User-centered design methods, psychophysical experiments Virtual Reality : Physical gamification, haptic feedback systems Digital Fabrication : 3D printing (FDM, SLA, SLS), procedural generation Haptic Experience Design : Tactile texture generation, sensory substitution Living Media Interfaces : Ambient feedback systems, plant-based interfaces Recent publications demonstrate expertise in: Haptic feedback mechanisms (TactStyle, WinDirect) Physical gamification (EcoMeal, Hakoniwa) VR interaction techniques (CollabJam, spatial haptics) Exergaming applications Metamaterials for haptics Passive haptic devices Supervision : Registered to guide Master's/Doctoral students with 6 research-based degrees supervised (2023-2025). Courses taught include Human Interface Technology - Design and Evaluation (HITD602) and Human Interface Technology - Prototyping and Projects (HITD603).
Kai Han is an Assistant Professor at The University of Hong Kong's School of Computing and Data Science, where he directs the Visual AI Lab. His research focuses on computer vision, machine learning, and artificial intelligence with specific interests in open-world learning, 3D vision, generative AI, and foundation models. He aims to achieve principled visual understanding and build reliable AI systems that close the intelligence gap between machines and humans. Dr. Han's research interests span multiple areas in visual AI, with particular emphasis on developing methods for open-world visual understanding. His work addresses fundamental challenges in category discovery, visual correspondence, 3D reconstruction, and generative modeling. He has made significant contributions to novel category discovery, open-set recognition, and visual correspondence problems, with his AutoNovel framework being particularly influential in the field. His current research explores the intersection of generative models and visual understanding, particularly focusing on how foundation models can be leveraged for comprehensive visual analysis. His publication record demonstrates a clear evolution from traditional computer vision problems toward more challenging open-world scenarios and generative approaches. Early work focused on 3D reconstruction of transparent and mirror surfaces, while more recent publications explore category discovery, visual correspondence, and generative AI. The trend shows increasing focus on foundation models, large language model integration with vision systems, and creating more robust visual understanding systems that can handle real-world open-set scenarios. Best Paper Runner-Up Award at CVPR Workshop on Continual Learning in Computer Vision, 2022 Outstanding Reviewer for ICCV 2021 (top 5%) Outstanding Reviewer for CVPR 2021 Outstanding Reviewer for CVPR 2020 Travel Award, ICLR 2020 Doctoral Consortium Travel Grant, ICCV 2017 Dr. Han actively mentors PhD students and postdocs, with numerous students appearing as first authors on his publications. His lab has secured multiple funding opportunities including HKU-PS, HKPFS, PGS, HKU-BICI, and HKU-ASTRI scholarships. He serves as Area Chair for major conferences including CVPR 2026, ICLR 2026, and AAAI 2026, demonstrating his standing in the research community. His lab, the Visual AI Lab, focuses on creating robust visual understanding systems that can handle real-world scenarios beyond closed-set recognition.
Michal Španěl is an Associate Professor in the Department of Computer Graphics and Multimedia at the Faculty of Information Technology, Brno University of Technology. His academic work centers on visual computing with emphasis on practical implementations and system architectures. His research spans Computer Graphics , Virtual Reality , and 3D Visualization , focusing on real-time rendering techniques, scientific visualization frameworks, and multimedia system design. Key methodologies include GPU programming, interactive visualization pipelines, and cross-platform graphics applications. Dr. Španěl maintains active scholarly identifiers including ORCID iD 0000-0003-0193-684X, ResearcherID G-9639-2016, and Scopus Author ID 22836945200 for tracking his academic contributions.
Mårten Sjöström is a Professor in Signal Processing at Mid Sweden University, where he serves as the highest representative of the research subject Computer and System Sciences and is part of the managerial group of the Department of Information and Communication Systems (IKS). He leads the Realistic 3D research group and has extensive experience in both academic and industrial settings. His educational background includes a Master of Science from Linköping University (Applied Physics and Electrical Engineering, 1992), a Technical Licentiate degree from the Royal Institute of Technology, Stockholm (Signal Processing, 1998), and a PhD from Ecole Polytechnique Federale de Lausanne (Modelling of Non-linear Systems, 2001). He obtained his Docent degree (Associate Professor) in 2008 and Professor's degree in Signal Processing in 2013. His primary research focuses on Multi-Dimensional Signal Processing with emphasis on System Modelling and Identification. He has successfully applied these techniques to Image and Video Processing, Multi-media Communications, and currently specializes in Multi-Scopic 3D and Light Field Technology including capture, processing, coding, and presentation/visualization. His work spans theoretical foundations to practical implementations across various application domains. His recent publication record demonstrates a clear trajectory toward advanced light field and 3D imaging technologies, with significant contributions to compression algorithms, depth estimation techniques, quality assessment metrics, and telepresence applications. His research bridges theoretical signal processing with practical industrial implementations, particularly in remote operation, mining applications, and immersive visualization systems. Best Paper Award at MMEDIA 2013 Quality Reviewer Award at ICME 2013 Professor Sjöström has supervised an extensive number of doctoral and licentiate students, with numerous current PhD candidates expected to complete their degrees in 2025. His teaching portfolio covers a wide range of subjects including Applied Signal Processing, Automatic Control, Computer Hardware and Architecture, and specialized PhD courses in Video Processing and Realistic 3D. He has led numerous research projects both current and completed, including IMMERSE, PLENOPTIMA, and various initiatives in 3D video technology and visualization. As founder and head of the Realistic 3D research group, he directs activities focused on synthesis and capture of 3D images and video, rendering techniques for virtual perspective views, system modeling for 3D capture and presentation, coding of 3D content, quality metrics and assessments, and remote control and measurement systems. The group maintains strong industrial collaborations across multiple sectors.
Sergio Canazza is an Associate Professor at the Department of Information Engineering , University of Padova, Italy. He holds key roles in academic leadership as advisory editor for the Journal of New Music Research and as founder of the Sound and Music Processing Lab . His work bridges music technology , audio restoration , and cultural heritage preservation . Degree in Electronic Engineering, University of Padova CEO, AudioInnova (University spin-off) Research Interests : Expressive information processing in music Auditory displays and cross-modal interaction Preservation of musical cultural heritage Interactive multimedia systems for education AI-driven audio restoration Digital philology for time-based media Scientific Contributions span 20+ years of European/National projects and 200+ publications. His recent work focuses on: Generative AI for IoT sound communication Standardization of audio preservation (ARP technology) Reactivation of historical computer music systems Visual anomaly detection in audio tapes Interactive environments for music education 3D reconstruction of ancient instruments Awards : StartCup Veneto 2010 (Sound and Music Lab) StartCup Veneto 2012 (TechnoTale project) Start Cup 2006 (ARCHIMEDES project) Leadership Roles : Project Manager, EU Culture Program Director, University of Padova's Multimedia Center (2013-2016) Owner of audio preservation patents
Aniruddha Kembhavi is an Affiliate Associate Professor at the University of Washington's Computer Science & Engineering department and currently serves as Director of Science Strategy at Wayve AI in London, UK. Previously, he led computer vision efforts as Senior Director at Allen Institute for AI (AI2) in Seattle and contributed to Microsoft's Image and Video Search division. His research spans 20+ years in Computer Vision , Robotics , and Embodied AI , focusing on open-source frameworks like AI2-THOR and Molmo. His work emphasizes procedural environment generation , vision-language integration , and 3D asset creation , with large-scale datasets such as Objaverse becoming foundational in 3D computer vision. CVPR 2025 Best Paper (Honorable Mention) CVPR 2023 Best Paper Winner Neurips 2022 Outstanding Paper CoRL 2024 Outstanding Paper IROS 2024 Best Mobile Manipulation Paper ICRA 2024 Best Paper Winner Allen Institute Test Of Time Award 2020 NVIDIA Pioneer Award 2018 His recent publications analyze vision-language models , 3D generation evaluation , and diffusion architectures for unified generation. He actively contributes to community-building as Program Chair for ICCV 2025 and Senior Area Chair for CVPR 2024.
Raoul de Charette is a Research Director in computer vision at Inria Paris, leading the Astra-Vision group within the ASTRA team. His academic journey includes a PhD from Mines Paris (2012) and Habilitation (HDR) in 2022, with research stints at Carnegie Mellon University (2011), Mines Paris (2013), and the University of Makedonia (2014). His educational background comprises: PhD from Mines Paris (2012) Habilitation (HDR) (2022) De Charette's research centers on robust and interpretable visual scene understanding , spanning 3D scene reconstruction, domain adaptation, material recognition, and physics-grounded vision foundation models. His work integrates physical principles and synthetic data to enhance model robustness in real-world scenarios like autonomous driving and urban environments. Key contributions include uncertainty-aware 3D scene completion (PaSCo), material extraction from single images (Material Palette), and prompt-driven domain adaptation (PODA). Recent publications reveal a strategic shift toward vision-language integration, material-centric scene understanding, and foundation models that minimize labeled data dependency. His group pioneers physics-informed approaches to improve interpretability and resilience against environmental challenges like adverse weather conditions. Key scientific recognition includes: Best Paper Honorable Mention at EGSR 2025 for MatSwap ELLIS Membership PR[AI]RIE-PSAI Fellowship De Charette actively mentors four PhD students—Fatima Balde, Mohammad Fahes, Ivan Lopes, and Tetiana Martyniuk—often in industry collaborations with Valeo.ai and Kyutai. He secures funding through fellowships and industry partnerships, regularly opening PhD positions (including a 2025 opening for Physics-Grounded Vision Foundation Models). As an area chair for CVPR, ECCV, WACV, and IROS, he shapes the field through conference leadership and co-organizing initiatives like the African Computer Vision Summer School. He directs the Astra-Vision group within Inria Paris' ASTRA team, driving interdisciplinary research at the intersection of computer vision, machine learning, and physics-based modeling for real-world deployment in robotics and intelligent transportation systems.
