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Francois ChaumetteView profile

Research Professor

• Robotics
• Computer Vision
• Visual Servoing
• +17 more

François Chaumette is a Senior Research Scientist (Directeur de recherche) at Inria, affiliated with IRISA and the Centre Inria de l'Université de Rennes. He has been a key researcher in robotics and computer vision since 1990 and led the Lagadic research team from 2004 to 2017. His research interests are centered on robot vision, particularly visual servoing and active perception . He has made foundational contributions to image-based and position-based visual servoing, and his work integrates control theory, computer vision, and robotics. His research spans applications in mobile robotics, aerial systems, medical robotics, space robotics, and soft object manipulation. The recent publications highlight a consistent focus on visual servoing under complex constraints—such as motion blur, occlusions, and deformations—applied to drones, cable-driven robots, and space systems. There is a strong emphasis on robustness , stability analysis , and hybrid sensing (e.g., vision + proximity, vision + force). His work with the RemoveDebris mission demonstrates real-world impact in space robotics. AFCET/CNRS Prize for best Ph.D. in Automatic Control Best paper awards at RFIA 1996 & 2004 Best paper in IEEE T-RA (2002) Best paper in IEEE RA-L (2019) Best paper in IEEE RAM (2020) IEEE Fellow (2013) He has advised over 30 Ph.D. students, many of whom have become active researchers in robotics. He has served in editorial roles for top journals including IEEE Transactions on Robotics , IEEE Robotics and Automation Letters , and the International Journal of Robotics Research . He was elected to the IEEE RAS Administrative Committee (2016–2018) and served on ERC grant panels for robotics. Chaumette is the main developer of ViSP (Visual Servoing Platform), a widely used C++ library for visual tracking and servoing. His leadership in both theoretical advances and software tools has significantly shaped the visual servoing community.

• Tomography
• Inverse Problems
• Medical Imaging
• +3 more

Peter Elbau is a Senior Lecturer (Privatdozent) in the Faculty of Mathematics at [University Name, if known]. His research focuses on mathematical imaging techniques, particularly in tomography and inverse problems, with applications in medical and biomedical imaging. He is actively involved in projects such as the "Tomography across the scales" initiative, aiming to develop quantitative imaging methods from molecular to astronomical scales. His primary research interests include the mathematical foundations of tomographic imaging, inverse scattering problems, and quantitative parameter reconstruction in optical coherence tomography (OCT) and photoacoustic imaging. He explores topics such as motion detection in diffraction tomography, curvature flows on surfaces, and the development of algorithms for medical diagnostics and material characterization. Elbau leads the research project "Tomography across the scales - Quantitative optical imaging from single molecules to stars," funded from 2018 to 2026. This project highlights his role in bridging fundamental mathematical research with practical applications in imaging technologies. His work often involves cross-disciplinary teams focusing on biomedical and material imaging challenges.

Mario SelvaggioView profile

Assistant Professor

• Shared control/autonomy
• Robot teleoperation
• Passivity-based control
• +7 more

Mario Selvaggio is an Assistant Professor at the Department of Electrical Engineering and Information Technology, University of Naples Federico II. He actively contributes to robotics research through his involvement with PRISMA Lab and ICAROS , while co-founding spinoff companies BeyondShape and Herobots . His academic background includes a Ph.D. in Information Technology and Electrical Engineering (2020) under Prof. Bruno Siciliano, with previous degrees in Mechanical Engineering (2013, 2015). Ph.D. in Information Technology and Electrical Engineering (2020) Bachelor's and Master's in Mechanical Engineering (2013, 2015) His research interests focus on shared control/autonomy, robot teleoperation, passivity-based control, soft robotics, and robotic surgery. He has developed innovative solutions including: Shared-control teleoperation for soft growing robots Non-prehensile object transportation frameworks Advanced modeling of cable-suspended dual-arm systems Medical robotics with force-sensor equipped tools Virtual reality-based teleoperation architectures Recent publications (2021-2025) span topics from semi-autonomous aerial manipulation to cyber-physical measurement systems, with emphasis on human-robot interaction and industrial applications. He serves as Associate Editor for several IEEE conferences and journals. Scientific Awards IEEE RAS Technical Committee on Haptics grant ($2500, 2018) Second prize at Bioengineering Congress (2018) Finalist for 'Fabrizio Flacco' Best Paper Award (2020) Prof. Selvaggio teaches the Master's Course in Automation Engineering and Robotics (2024/2025) using Robot Operating System (ROS) curriculum. He actively collaborates with international research groups at IRISA/INRIA Rennes , Rainbow team , and University of California Santa Barbara (mechanical engineering department). His research combines theoretical advancements with practical implementations, demonstrated through extensive validation in simulated environments and real robotic platforms including KUKA LWR IIWA manipulators, dVRK systems, and humanoid platforms.

Maolin GaoView profile

Researcher

• Computer Vision
• 3D Shape Matching
• Geometry Processing
• +5 more

Maolin Gao is a Researcher at the Computer Vision Group , TUM School of Computation, Information and Technology , and a PhD candidate at the Department of Informatics 9 , Technical University of Munich (TUM). He is supervised by Prof. Daniel Cremers and was a visiting scholar at Stanford University's Geometric Computing Lab under Prof. Leonidas J. Guibas in Summer 2024. Education : Bachelor in Physics from Tongji University (Shanghai), with exchange experience at TUM and RWTH Aachen Master's in Electrical Engineering and Information Technology from TUM, thesis on Blind Deconvolution supervised by Dr. Michael Hirsch, Prof. Philipp Hennig, and Prof. Bernhard Schölkopf at Max Planck Institute for Intelligent Systems (MPI-IS) in Tübingen Research Focus : Maolin's work bridges geometry and learning to enhance human and machine understanding of 2D/3D data. His research spans 3D shape matching , non-rigid correspondence , geometric consistency , and deep learning applications in computer vision and graphics. He also explores photometric stereo , bundle adjustment , and scale-invariant matching methods . Article Trends : His publications emphasize 3D shape analysis , including partial-to-partial matching , non-rigid deformations , and geometric consistency algorithms . Recent works like EchoMatch (CVPR 2025) and CoE (3DV 2025) highlight advancements in point cloud correspondences and cycle-consistency . Earlier papers address distributed optimization (3DV 2020) and isometric multi-shape matching (CVPR 2021). Scientific Awards : Oral Presentation Award at 3DV 2020 for Distributed Photometric Bundle Adjustment Labs & Collaborations : Affiliated with TUM CVAI Lab and Stanford Geometric Computing Lab . Collaborates with researchers like Prof. Florian Bernard and Dr. Michael Hirsch.

Aljaž BožičView profile

Researcher

• Computer Vision
• Computer Graphics
• Artificial Intelligence
• +7 more

Aljaž Božič is a Research Scientist at Meta Reality Labs Research , focusing on neural rendering, 3D reconstruction, and AI-driven geometry modeling. He earned his Ph.D. in Computer Science from the Technical University of Munich (TUM) and holds a Master's in Computer Science from TUM and a Bachelor's in Mathematics from the University of Ljubljana . His research spans computer vision, graphics, and artificial intelligence , with a focus on neural rendering , generative AI , and 3D deformable object modeling , targeting applications in VR/AR and robotics. His work includes time-consistent dynamic scene reconstruction (SceNeRFlow), volumetric hair appearance modeling, and high-fidelity walkable VR spaces (VR-NeRF), alongside efficient NeRF distillation and calibration methods (Neural Lens Modeling). Key article trends include Transformer-based monocular reconstruction (TransformerFusion), neural parametric shape models (NPMs), and self-supervised non-rigid tracking (Neural Deformation Graphs). He has contributed to open-source projects like the TransformerFusion GitHub repository , emphasizing MIT-licensed tools for scene reconstruction. At TUM, he served as a Teaching Assistant for courses such as 3D Scanning and Spatial Learning and 3D Vision Seminar , bridging academic instruction with research innovation. His work integrates advanced neural networks with practical optimization techniques, advancing fields like RGB-D reconstruction (DeepDeform) and variational SLAM.

Xu ChenView profile

Researcher

• 3D Generative Models
• Computer Vision
• Human Activity Simulation
• +5 more

Xu Chen is a doctoral researcher at ETH Zurich specializing in 3D generative models and neural implicit shape animation . His work focuses on creating photo-realistic simulations of human activity for applications in human-centric perception tasks .

• Computer Vision
• Robotics
• Machine Learning
• +5 more

Saurabh Gupta is an Associate Professor in the Electrical and Computer Engineering Department at the University of Illinois Urbana-Champaign (UIUC), where he is based at the Coordinated Science Lab (CSL 319). He previously served as a Research Scientist at Facebook AI Research in Pittsburgh working with Prof. Abhinav Gupta. His educational background includes a PhD in Computer Science from UC Berkeley advised by Prof. Jitendra Malik, and an undergraduate degree in Computer Science and Engineering from IIT Delhi, India. Gupta's research focuses on building intelligent agents that can interact with the physical world, with particular emphasis on computer vision, robotics, and machine learning. His work explores representations that enable physical interaction and learning from active engagement with environments. Key research directions include spatio-semantic and topological representations for visual navigation, skill discovery, learning from videos, and active visual learning. His recent publications reveal a strong trend toward practical robotics applications with an emphasis on real-world deployment. The research spans multiple domains including humanoid robotics, egocentric vision, physical reasoning, and wireless sensing. Significant attention is given to bridging the sim-to-real gap and developing systems that work in practical environments rather than controlled laboratory settings. As an educator, Gupta teaches advanced courses including Deep Learning for Computer Vision (CS 444/ECE 494), Computer Vision (ECE 549/CS 543), and Learning-Based Robotics (ECE 598 SG). Gupta advises multiple PhD students including Arjun Gupta, Shaowei Liu, Aditya Prakash, Xiaoyu Zhang, Runpei Dong, and Xialin He, with former student Matthew Chang completing his PhD in 2024 on Robot Learning from Videos. His research group operates within the Coordinated Science Laboratory at UIUC, collaborating with researchers across computer vision, robotics, and machine learning domains. The group maintains strong connections with industry research labs including Meta AI Research, where former students have pursued research scientist positions.

Jon SporringView profile

Professor

• Computer Science
• Mathematical Image Processing
• Medical Image Analysis
• +6 more

Jon Sporring is a Professor at the Department of Computer Science, University of Copenhagen, specializing in theoretical and applied image processing, stochastic geometry, and biomedical imaging. He leads research in mathematical and medical image analysis, computer graphics, and pattern recognition. Education: Ph.D. in Computer Science (1998), Master in Computer Science (1995), both from University of Copenhagen Affiliations: Pioneer AI section, Applied Geometry Lab, and Faculty of Science External Roles: Visiting professor at McGill University (2012-13), co-founder of DigiCorpus Aps (2012-16) His research integrates scale-space theory, statistical shape analysis, and advanced imaging techniques for applications in medical diagnostics, materials science, and neuroscience. Recent work focuses on 3D reconstruction, persistent homology for bias correction, and AI-driven biomedical analysis. Jon teaches computer science at all academic levels, currently offering courses in bioimaging, signal processing, and deep learning. He emphasizes collaborative projects with external partners and has held administrative roles including Vice-Chair for Research at DIKU. His 15 most recent publications reflect expertise in medical imaging, 3D modeling, and AI applications, with subfields spanning neurodegenerative disease analysis, mitochondrial ultrastructure, and multi-scale image processing. Articles demonstrate interdisciplinary impact across medicine, biology, and materials science.

• Geometric Shape Analysis
• Deep Learning
• 3D Shape Matching
• +2 more

Maks Ovsjanikov is Professor of Computer Science at École Polytechnique and Visiting Research Scientist at Google DeepMind. Leading the GeomeriX team, his ERC-funded research develops deep learning methods for 3D shape analysis with applications in protein modeling and computer vision. Recent breakthroughs include CrAI for antibody detection in cryo-EM data and surface-based protein representations. His group pioneers neural approaches for non-rigid shape matching and geometric deep learning fundamentals. Collaborations span structural biology, with work featured at top venues including SIGGRAPH, CVPR, and Nature journals.

Werner HaslehnerView profile

Professor

• International Taxation
• European Union Tax Law
• Tax Policy
• +4 more

Werner Haslehner is a Full Professor of Law at the University of Luxembourg, holding the ATOZ Chair in European and International Taxation. He directs the LL.M. program in EU and International Tax Law and has held visiting professorships at the University of Paris I Sorbonne (2018) and the University of Turin (2019), while also serving as a Global Research Fellow at NYU School of Law (spring 2020). His academic work focuses on international taxation, particularly the intersection of EU law, tax policy, and global minimum taxation frameworks. Research interests center on cross-border tax implications, BEPS (Base Erosion and Profit Shifting) countermeasures, tax treaty harmonization, and the legal aspects of artificial intelligence in taxation. Articles demonstrate expertise in OECD Model Convention applications, Global Anti-Abuse Rules (GAAR), Pillar 2 implementation challenges, and EU anti-avoidance directives, with collaborations in collective works and book chapters addressing computational tax issues and treaty network reconstruction. Key sub-fields span minimum tax rate setting, investment treaty analysis, judicial review of tax rulings, digital taxation, and taxpayer rights protection. Publications appear in journals like Intertax , Common Market Law Review , and Cahiers de Droit Fiscal International , reflecting interdisciplinary engagement with legal, economic, and policy dimensions of taxation.

• Robotics
• Agricultural Robotics
• 3D Imaging
• +6 more

Dr. Alen Alempijevic is the Deputy Head of School (Teaching and Learning) at the School of Mechanical and Mechatronic Engineering, University of Technology Sydney (UTS). He holds the academic rank of Senior Lecturer and coordinates Mechatronic Engineering programs. His research focuses on robotic perception, multi-modal sensor fusion, and AI applications in physical systems, with industry-driven projects in livestock monitoring, critical infrastructure, and autonomous systems. He completed his PhD in Robotics at UTS and contributed to the DARPA Urban Grand Challenge with the Sydney-Berkeley team. Education: PhD in Robotics (2004-2008), B.Eng (Computer Systems) with First Class Honors (2000-2003), B.E and B.Sc in Computer Science and Engineering from University of Belgrade (1995-1999). Research Interests: Livestock monitoring via 3D imaging, autonomous vehicles, sensor fusion, and robotics perception. His work includes developing technologies for cattle trait estimation, crowd dynamics prediction, and industrial automation. Funded Research: Notable grants include UTS Wool Bioharvesting (2024-2027), Objective real-time live cattle assessments (2020-2023), and Advanced livestock measurement technologies (2017-2018).

• Medical Imaging
• Artificial Intelligence in Radiology
• Magnetic Resonance Imaging
• +5 more

Sergios Gatidis is a prominent researcher in the Department of Diagnostic and Interventional Radiology at the Faculty of Medicine, Eberhard Karls University of Tübingen. His work bridges medical imaging and artificial intelligence, with a particular focus on MRI and PET/CT applications. He has established himself as a key collaborator in numerous multi-institutional research projects, frequently working with Thomas Küstner, Bin Yang, and Konstantin Nikolaou. Dr. Gatidis's research centers on applying deep learning techniques to solve critical challenges in medical imaging. His work spans biological age estimation from MRI scans, motion correction in MRI, lesion segmentation in PET/CT imaging, and the application of large language models to radiology reports and hospital course documentation. He has made significant contributions to the autoPET challenge for automated lesion segmentation and has developed novel approaches for attention-aware image registration and reconstruction. His publication record shows a clear evolution from foundational work in motion correction and image reconstruction (2016-2018) to increasingly sophisticated AI applications, with a recent strong focus on large language models for medical text processing (2023-2025). The breadth of his work demonstrates expertise spanning technical aspects of medical imaging physics to clinical applications of AI. His recent publications indicate active research in several key areas: (1) development of foundation models for medical imaging interpretation, (2) robust evaluation frameworks for medical AI systems, and (3) practical clinical integration of AI tools for radiology workflow enhancement. These trends reflect the broader field's movement toward more comprehensive, clinically validated AI solutions. Though no specific awards are listed in the available publications, his consistent presence as a key contributor to high-impact medical imaging research suggests recognition within the field. His work appears regularly in top journals including IEEE Transactions on Medical Imaging, Nature Machine Intelligence, and Medical Image Analysis. Dr. Gatidis actively collaborates across disciplines, working with computer scientists developing novel AI architectures and clinicians implementing these tools in real-world settings. His recent work on MedHELM and CheXagent demonstrates commitment to creating evaluation frameworks and practical tools that address real clinical needs while maintaining scientific rigor.

Zorah LähnerView profile

Professor

• Computer Vision
• Shape Matching
• Machine Learning
• +3 more

Prof. Dr. Zorah Lähner is a Professor at the University of Siegen, leading research in the Department of Computer Vision. She will be transitioning to an Assistant Professor position at the University of Bonn starting January 2025. Her work bridges computer vision, machine learning, and quantum computing, focusing on fundamental geometric and algorithmic challenges in 3D shape analysis. Research interests span: Advanced shape matching methodologies Neural field representations on manifolds Quantum annealing applications in computer vision Scale-invariant correspondence frameworks Latent space alignment techniques Her publications demonstrate consistent innovation in geometric deep learning, with recent works exploring quantum-hybrid approaches for shape matching and neural fields for manifold learning. Publications predominantly appear in top-tier venues like CVPR, ICCV, and NeurIPS. While no specific awards are listed in the provided text, she maintains active collaborations across European institutions and supervises research in computer graphics and vision through her Lehrstuhl position.

Anne BonninView profile

Researcher

• Bioimaging
• Cardiac Imaging
• X-ray Tomography
• +4 more

Dr. Anne Bonnin serves as a Beamline Scientist at the Paul Scherrer Institute (PSI) in Switzerland, where she has been instrumental in X-ray imaging research since joining the X-ray Tomography Group in 2014 and assuming her current role at the TOMCAT Beamline in 2016. Affiliated with PSI's Center for Photon Science and Laboratory for Macromolecules and Bioimaging, she operates at the forefront of synchrotron-based imaging techniques. Her academic foundation includes a PhD from INSA de Lyon focused on material properties for explosive detection, followed by postdoctoral work at the European Synchrotron Radiation Facility (ESRF) in X-ray diffraction and phase contrast tomography, and an NSF Research Fellowship for paleontology research at Harvard University and ESRF. Specializing in X-ray imaging (micro/nano-tomography, phase-retrieval) and powder diffraction, Dr. Bonnin leads the bioimaging program at TOMCAT with particular emphasis on the international Heart Imaging Project. Her research develops novel methodologies for materials characterization across diverse domains including cardiac microstructure analysis, paleontology, and neurodegenerative disease modeling, with significant contributions to understanding material behavior at microscopic scales. Her recent publications (2019-2021) demonstrate strong interdisciplinary impact, advancing X-ray imaging applications in energy storage (battery materials), biomedical research (cardiac/auditory systems), and materials engineering (aerogels). A defining trend is the integration of machine learning for image analysis, alongside methodological innovations like non-rigid image stitching and Fourier ptychography. These works reflect extensive international collaboration and address critical challenges in healthcare, energy, and fundamental material science. Dr. Bonnin leads the Heart Imaging Project to quantify cardiac microstructure using contrast-agent-free X-ray phase-contrast imaging, while actively contributing to the SLS2.0 upgrade project preparing TOMCAT for multiscale, multimodal, and dynamic tomographic capabilities. Her collaborative framework spans global researchers in materials science, paleontology, and biomedical engineering. As manager of the TOMCAT nanoscope—a full-field imaging setup achieving 150 nm 3D resolution—she enables cutting-edge research in absorption and phase-contrast imaging. Her team within the X-Ray Tomography Group drives the bioimaging program forward, particularly through the Heart Imaging Project's dynamic cardiac studies using modified Langendorff setups.

Jun ChenView profile

Professor

• Experimental fluid dynamics
• Flow diagnostic techniques development
• Stratified flow dynamics
• +5 more

Jun Chen serves as Professor and Associate Head for Facilities and Operations at Purdue University's School of Mechanical Engineering. His career spans experimental fluid dynamics research, academic leadership, and global engineering initiatives focused on sustainable energy solutions for underserved communities. Education: PhD in Mechanical Engineering, Johns Hopkins University (2005) MS in Aerospace Engineering, Beijing University of Aeronautics & Astronautics (1997) BS in Aerospace Engineering, Beijing University of Aeronautics & Astronautics (1994) Professor Chen's research centers on experimental fluid dynamics with emphasis on flow diagnostic techniques including digital holography, tomographic PIV, and interferometry. His work spans fundamental turbulence studies in stratified environments and applied energy systems for wind, hydrokinetic, and cardiovascular applications. Key innovation areas include multiphase flow measurement, low-Mach-number aeroacoustics, and wind energy conversion systems. His 15 most recent publications reveal consistent focus on advanced optical diagnostics for complex flows, with growing emphasis on renewable energy applications (wind/hydrokinetic systems) and biomedical fluid dynamics . The research demonstrates methodological evolution from fundamental turbulence characterization toward practical implementations in energy and healthcare. Scientific Awards: 2018 ASCE Sustainable Development Award for African grain storage system 2016 EPA P3 YCOSST Award for off-grid wind energy in Africa 2014 ASME Robert T. Knapp Award for holography uncertainty quantification 2005 Measurement Science Outstanding Paper Award for PIV peak-locking elimination Professor Chen actively mentors graduate students (16 theses supervised) and leads global engineering initiatives through Purdue's GEPP program, having conducted multiple field deployments in Cameroon for rural energy solutions. His research portfolio includes significant grants from NSF, DOE, and industry partners focused on fluid diagnostics and sustainable energy. His laboratory work integrates optical diagnostics with field-deployable energy systems , maintaining strong industry partnerships while advancing fundamental fluid mechanics knowledge. Current projects emphasize scalable solutions for resource-constrained environments through the Purdue Global Design Teams.