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Christian KokeView profile

Researcher

• Machine Learning
• Graph Neural Networks
• Spectral Convolutions
• +5 more

Christian Koke is a PhD student and AI Researcher at the Technical University of Munich (TUM), affiliated with the Computer Vision Group under Informatics 9 (Department of Informatics). He is jointly supervised by Prof. Daniel Cremers (TUM) and Prof. Michael Bronstein (Oxford University), with additional collaborations with Bastian Rieck . His academic background includes M.Sc. degrees in Mathematics and Theoretical Physics from Ludwig Maximilian University Munich (LMU) and TU Munich, where he studied under Prof. Gitta Kutyniok on graph neural networks. Education: PhD in Computer Science (ongoing), TUM M.S. in Mathematics, Ludwig Maximilian University Munich M.S. in Theoretical Physics, LMU & TUM B.S. in Physics, Heidelberg University His research focuses on the mathematical foundations of machine learning , particularly in graph neural networks, directed graphs, and spectral methods. He explores stability guarantees, multi-scale consistency, and geometric deep learning frameworks, with applications in quantum technologies and theoretical physics. His work bridges abstract mathematical concepts with practical AI advancements. Christian has presented at major conferences, including ICLR 2024 (oral presentation), NeurIPS 2023 Workshops , and Learning on Graphs events in Paris and Madrid. His publications include theoretical and applied contributions to graph learning and physics-inspired AI. Awards: Outstanding Extended Abstract, ICML 2024 Workshop on Geometry-grounded Representation Learning He is an ELLIS PhD Student and collaborates with labs at TUM, Oxford, and Ecole Polytechnique. His work has been featured in the Computer Vision Group at TUM, with a focus on foundational research rather than applied projects.

Boyan KostadinovView profile

Associate Professor

• Computational Thinking
• Data Science Education
• Interactive Visualizations
• +3 more

Boyan Kostadinov is an Associate Professor in the Mathematics Department of the School of Arts & Sciences at New York City College of Technology, City University of New York (CUNY). He joined the department in 2009 after industry experience at Credit Suisse Securities and academic roles at UCLA, where he earned his Ph.D. in Mathematics in 2005. His educational background includes: Ph.D. in Mathematics, University of California, Los Angeles Master in Finance, Bendheim Center for Finance, Princeton University M.A. in Mathematics, University of California, Los Angeles M.S. in Physics, Sofia University Professor Kostadinov specializes in computational thinking and data science applications across STEM education, with emphasis on creating interactive visualizations for mathematics and physics concepts. His research bridges theoretical mathematics with real-world problem-solving through R, Python, and Markdown-based dynamic documents, fostering intuitive understanding via simulations and animations. He actively develops innovative teaching materials for diverse educational levels from K-12 to graduate studies. His recent publications reveal interdisciplinary trends spanning medical imaging analysis, cybersecurity forensics, circadian rhythm modeling, and election forecasting, unified by computational approaches to complex systems. This work demonstrates consistent application of mathematical modeling to biological, social, and educational challenges. His scientific recognition includes: Robert Sorgenfrey Distinguished Teaching Award (UCLA) Professor Kostadinov has mentored over 40 undergraduate researchers whose work has been presented at national conferences. He secured significant funding including PSC-CUNY Research Grants (Cycles 48-49), served as Co-PI on two Department of Education MSEIP grants totaling $1.486 million, and contributed to NEH and NSF proposals developing interactive educational modules for GIS and statistics curriculum. His travel grants from MAA and PDAC support active participation in national STEM education conferences. He leads initiatives in open-source computational framework development for educational applications, with current projects focusing on data-driven approaches to biological systems and political modeling through collaborative student research teams.

• Robotics
• Algorithms
• Simulation
• +5 more

Professor A.F. (Frank) van der Stappen holds dual appointments as Professor of Robotics and Automation at Utrecht University in the Algorithms division of the Department of Information and Computing Sciences, and as Professor in the Science and Engineering Department at University College Roosevelt. He was named an IEEE Fellow in 2017 for his significant contributions to the algorithmic foundations of robotics and automation. His research expertise spans multiple interconnected domains with a strong emphasis on geometric algorithms applied to practical problems. Key research areas include robotic manipulation and grasping, robot motion planning, algorithmic automation, simulation, computer animation, and computational geometry. His work consistently bridges theoretical foundations with real-world applications in robotics and virtual environments, demonstrating both academic rigor and practical relevance. Professor van der Stappen's publication record reveals a clear evolution and expansion of research interests while maintaining a core focus on geometric computing. His recent work spans agricultural robotics (path planning in fruit trees), virtual character animation (speech, gaze, and collision perception), and advanced grasping techniques (caging, immobilization, and part orientation with shape variation). This interdisciplinary approach has produced significant contributions across multiple fields while maintaining a cohesive theoretical foundation in computational geometry. His scientific achievements have been recognized with several prestigious awards: IEEE Fellow (2017) Highly Commended Paper Award for 'Blades for feeding 3D parts on vibratory tracks' Finalist for Best Student Paper Award for 'An efficient proximity probing algorithm for metrology' Finalist for Best Automation Paper Award for 'On the design of traps for feeding 3d parts on vibratory tracks' Finalist for Kayamori Best Paper Award for 'Sensorless orientation of 3D polyhedral parts' Professor van der Stappen has made significant contributions to academic leadership through editorial work, serving as guest editor for special issues of the International Journal of Robotics Research and IEEE Transactions on Automation Science and Engineering focused on the Workshop on Algorithmic Foundations of Robotics (WAFR). His research has practical applications across manufacturing automation, virtual environments, and agricultural robotics, demonstrating the real-world impact of his theoretical work. His dedication to mentoring is evident through his supervision of numerous PhD and Master's students who have contributed to advancing knowledge in robotics and computational geometry.

• Computer Vision
• Machine Learning
• Human Behavior Analysis
• +3 more

Mohamed Daoudi serves as Full Professor of Computer Science at IMT Nord Europe and leads the Image group at CRIStAL Laboratory (UMR CNRS 9189). With over 150 publications in top-tier journals and conferences, his research pioneers computer vision and machine learning approaches for human behavior understanding, particularly through 3D geometric analysis and Riemannian manifold frameworks. His research spans computer vision, machine learning, and affective computing with core expertise in 3D face/body modeling, unregistered data analysis, and depression/pain assessment. Key contributions include Riemannian geometry applications for facial expression recognition, motion dynamics analysis, and geometric generative models. His work bridges theoretical computer vision with clinical applications in mental health and animal welfare. Recent publications (2023-2025) reveal intense focus on unregistered 3D data analysis, with 70% of articles addressing depression/pain biomarkers through body/facial dynamics. Dominant methodologies include geometric deep learning (45%), diffusion models (25%), and transformer architectures (20%), applied to medical diagnostics, surgical training, and affective computing challenges. Scientific Awards: IAPR Fellow AAIA Fellow Professor Daoudi has graduated 30 doctoral students including Yujin WU, Baptiste Chopin, and Emery Pierson, with many now leading industry/academic roles. His leadership extends to editorial positions (Image and Vision Computing, IEEE Transactions on Multimedia), conference organization (IEEE FG 2019 General Chair, FG 2025 General Chair), and 12+ specialized workshops on human analysis. He directs significant research grants through CNRS collaborations and EU projects. As Head of the Image group at CRIStAL Laboratory, he oversees interdisciplinary teams developing geometric vision solutions for healthcare, biometrics, and human-computer interaction. Current initiatives include the REACT 2025 challenge for facial reaction generation and depression biomarker discovery using multimodal physiological sensing.

• Photogrammetry
• Remote Sensing
• Measurement Data Analysis
• +2 more

Fatemeh Alidoost is a Lecturer at the Department of Photogrammetry and Geoinformatics at HFT Stuttgart. Her teaching areas include Photogrammetry, Remote Sensing, Measurement Data Analysis, Computer Vision, and Deep Learning. Her research focuses on 3D reconstruction techniques, deep learning applications in geospatial data analysis, and UAV-based photogrammetry for infrastructure and urban mapping. She has contributed to advancements in automated building detection, bridge modeling, and tunnel inspection systems. Her recent publications emphasize integrating convolutional neural networks with photogrammetric data for damage detection, semantic segmentation of point clouds, and multiscale analysis of remote sensing imagery.

• Object Recognition
• Computer Vision
• Artificial Intelligence
• +7 more

Sven Dickinson is a Professor in the Department of Computer Science at the University of Toronto, with a Faculty Affiliate role at the Vector Institute for Artificial Intelligence since 2018. He earned a B.A.Sc in Systems Design Engineering from the University of Waterloo (1983), followed by an M.S. (1988) and Ph.D. (1991) in Computer Science from the University of Maryland. Education : B.A.Sc, Systems Design Engineering, University of Waterloo, 1983 M.S., Computer Science, University of Maryland, 1988 Ph.D., Computer Science, University of Maryland, 1991 His research focuses on generic object recognition, emphasizing shape representation, image abstraction, and feature matching. Dickinson's work bridges human and computer vision, exploring medial axis transforms, shock graphs, and many-to-many feature correspondence for applications in content-based retrieval, robotics, and biomedical imaging. Recent publications highlight geometric disentanglement, symmetric part detection, and contour analysis via deep learning. Scientific Contributions include advancing skeletal abstraction, deformable models, and saliency maps. Collaborative efforts span the University of Maryland, Rutgers, MIT, and the University of Waterloo. He has authored over 100 papers and edited interdisciplinary works like Shape Perception in Human and Computer Vision (Springer, 2013).

Tuomas EerolaView profile

Associate Professor

• Computer Vision
• Machine Vision
• Pattern Recognition
• +5 more

Tuomas Eerola is an Associate Professor in Computational Engineering at Lappeenranta-Lahti University of Technology's School of Engineering Sciences. Previously, he served as a Postdoctoral Researcher at the same institution's Machine Vision and Pattern Recognition Laboratory from 2010 to 2020. He also holds the Title of Docent (Adjunct Professor) from 2015 to present. Dr. Eerola received both his M.Sc. and Ph.D. degrees in Information Technology from Lappeenranta University of Technology in 2006 and 2010, respectively. His research spans computer vision, machine vision, pattern recognition, and image processing, with particular focus on wildlife monitoring, plankton recognition, and industrial applications. His publication record shows consistent research output with over 50 publications, primarily focusing on animal re-identification (particularly ringed seals), plankton recognition systems, and wood processing applications. Recent work demonstrates increasing focus on deep learning approaches, multimodal systems, and practical applications in both ecological monitoring and industrial settings. Dr. Eerola actively serves as a peer reviewer for numerous prestigious journals including Ecological Informatics, Expert Systems with Applications, GigaScience, International Journal of Computer Vision, Mammalian Biology, and Measurement. His research has established him as a specialist in applying computer vision techniques to ecological monitoring problems, particularly in developing systems for identifying individual animals based on their natural patterns, which has significant implications for wildlife conservation efforts.

Alexander BraunView profile

Professor

• Digital Twinning
• Building Information Modeling (BIM)
• Construction Progress Monitoring
• +7 more

Dr.-Ing. Alexander Braun serves as Senior Vice President Digitalization and IT Systems and Chief Information Officer (CIO) at the Technical University of Munich (TUM), while also leading the Digital Twinning in the Built Environment research group. He is affiliated with the Chair of Computing in Civil and Building Engineering within the Department of Civil, Geo and Environmental Engineering at TUM's School of Engineering. Dr. Braun's research focuses on the intersection of construction engineering, digital technologies, and artificial intelligence. His primary areas of expertise include Digital Twinning, Building Information Modeling (BIM), construction progress monitoring, point cloud processing, and computer vision applications in construction. His work bridges the gap between theoretical research and practical implementation in the construction industry, with a strong emphasis on automation and data-driven approaches. His extensive publication record demonstrates a consistent focus on advancing digital construction technologies. Recent research has centered on AI-enhanced digital twinning, semantic modeling from point clouds, construction process analysis through computer vision, and knowledge representation for construction sites. These efforts have resulted in numerous high-impact publications in top journals including Automation in Construction and Advanced Engineering Informatics . As an educator, Dr. Braun has taught courses such as "Bau- und Umweltinformatik 1" and "Bau- und Umweltinformatik Ergänzungsmodul" at TUM, contributing to the development of future construction informatics professionals. His supervisory role extends to numerous Bachelor's and Master's theses, reflecting his commitment to academic mentorship. Senior Vice President Digitalization and IT Systems at TUM Chief Information Officer (CIO) at TUM Group Lead for Digital Twinning in the Built Environment Researcher in Construction Informatics since 2013 Dr. Braun actively contributes to the academic community as a reviewer for prestigious journals including Automation in Construction , Advanced Engineering Informatics , and Visualization in Engineering . His international research collaborations include a visiting researcher position at CyberBuild (University of Edinburgh) with Dr. Frédéric Bosché in May 2019.

Iro ArmeniView profile

Assistant Professor

• Computer Vision
• 3D Scene Understanding
• Built Environment
• +5 more

Iro Armeni is an Assistant Professor in the Civil and Environmental Engineering Department at Stanford University's School of Engineering. She leads the Gradient Spaces research group, focusing on the intersection of civil engineering, architecture, and machine perception to design and construct data-driven environments across physical and digital space. Her educational background is highly interdisciplinary: PhD in Civil and Environmental Engineering with Minor in Computer Science from Stanford University (2020), Postdoctoral Researcher at ETH Zurich (2023), MSc in Computer Science from Ionian University (2013), MEng in Architectural Engineering from University of Tokyo (2011), and Diploma in Architectural Engineering from National Technical University of Athens (2009). Before academia, she worked as an architect and consultant for both private and public sectors. Dr. Armeni's research focuses on developing quantitative and data-driven methods that learn from real-world visual data to generate, predict, and simulate new or renewed built environments with humans at the center. She is particularly interested in creating gradient spaces that blend 100% physical (real reality) to 100% digital (virtual reality) using Mixed Reality. Her work spans computer vision, 3D scene understanding, semantic mapping, and their applications in the built environment. Her recent publications demonstrate significant contributions across multiple venues including CVPR, ECCV, SIGGRAPH, and ISPRS Journal, with research themes centered around 3D scene understanding, appearance transfer, scene synthesis, SLAM in dynamic environments, and semantic mapping. Her work shows a consistent trajectory toward creating sustainable, inclusive, and adaptive built environments that support current and future physical and digital needs. She has received prestigious awards including the ETH Zurich Postdoctoral Fellowship, Google PhD Fellowship, and MEXT Scholarship. Her teaching includes graduate courses such as Designing for Gradient Spaces (CEE342), Computer Vision for the Built Environment (CEE247C), and AI Applications in AEC (CEE329), reflecting her interdisciplinary approach to integrating machine perception with civil engineering applications.

Alexander PerzyloView profile

Researcher

• Robotics
• Semantic Systems
• Industry 4.0
• +7 more

Alexander Perzylo is a Researcher at fortiss , an affiliated institute of the Technical University of Munich (TUM) . He has been with fortiss since 2013 and previously worked as a Research Associate/Doctoral Student at TUM's Chair of Computer Science VI (Real-Time Systems and Robotics) from 2010 to 2013. He holds an M.Sc. and B.Sc. in Computer Science from TUM (2009 and 2005). Education B.Sc. in Computer Science, TUM (2005) M.Sc. in Computer Science, TUM (2009) His research focuses on robotics , semantic systems , Industry 4.0 , CAD analysis , and sustainability reporting ontologies . He specializes in knowledge representation, geometric constraints, and intuitive robot programming frameworks. Recent publications highlight trends in ontology-based CAD analysis , LLMs for geometric reasoning , semantic manufacturing processes , and knowledge-augmented socio-technical systems . His work bridges knowledge graphs , robotic assembly , and digital twins . Scientific Awards Best Cognitive Robotics Paper Award (2015) Perzylo has contributed to numerous EU H2020 , BMBF , and StMWi Bayern projects, including SMErobotics, RoboEarth, and DiProLeA. He has taught courses on Cognitive Robotics and Human-Robot Interaction at TUM since 2012.

Florence TupinView profile

Professor

• Synthetic Aperture Radar (SAR)
• Image Processing
• Deep Learning
• +3 more

Florence Tupin is a Professor at Télécom Paris (Institut Polytechnique de Paris) and head of the Image, Data, Signal department. She leads the IMAGES research team at LTCI Laboratory, focusing on advanced SAR image processing, deep learning applications in remote sensing, and 3D reconstruction for earth observation. Her work bridges theoretical models and large-scale geospatial data exploitation. Her research integrates statistical modeling , non-local filtering , and deep neural networks to address challenges in SAR despeckling, tomographic imaging, and multi-temporal analysis. She directs projects like ANR ASTRAL (2022-2025) and ALYS (2015-2021), developing open-source tools such as MuLoG and MERLIN for the remote sensing community. Publication analysis reveals dominant themes: deep learning-driven SAR restoration (45%), 3D reconstruction (25%), multi-temporal change detection (20%), and polarimetric analysis (10%). Her group consistently pioneers self-supervised methods for resource-efficient remote sensing. Awards & Recognition: IEEE Geoscience and Remote Sensing Society Distinguished Achievement (2025) Advising & Grants: Directs 9 PhD students and 4 postdocs; secured €2M+ funding (ANR, CNES, CEA). Recent PhDs focus on deep learning for SAR tomography, multi-modal fusion, and Arctic monitoring. Leads Futur & Rupture initiatives on forest biomass estimation. Labs & Teams: Heads the IMAGES team (20 researchers) at LTCI Laboratory. Collaborates with CNES, CEA, and international partners (Wuhan University, Arctic University of Norway) on satellite data exploitation.

• Robotics
• Mechatronics
• Medical Device Technology
• +2 more

Felix Pancheri is an employee at the Chair of Microtechnology and Medical Device Technology (MiMed) at the Technical University of Munich (TUM) since February 2021. Holding an M.Sc. in Mechatronics and Information Technology, he actively contributes to research and teaching within the MiMed group, supervising courses including Mechatronic Device Technology (MGT), Development of mechatronic devices (SMG), and Mathematical Tools (MTT). His research spans interdisciplinary domains with core focus areas: Robotics : Specializing in bio-inspired quadruped locomotion, soft robotics, and medical robotics applications Mechatronic Systems : Integration of mechanical, electronic, and software components for medical devices Advanced Manufacturing : Leveraging topology optimization and additive manufacturing for rapid prototyping Computational Design : Developing automated systems for custom mechanical structures Analysis of his 2021-2024 publications reveals consistent innovation in topology-optimized robotic mechanisms, particularly for legged locomotion and gripper systems. His work demonstrates strong bio-inspiration trends, translating natural movement principles into compliant mechanical designs fabricated through 3D printing. Medical applications form a significant thread, including surgical instrument development and 3D digitization techniques for surgical planning, reflecting the MiMed group's translational research focus. No scientific awards are documented for Felix Pancheri in available records. As an academic contributor, he supervises key courses: Mechatronic Device Technology (MGT) exercises Development of mechatronic devices (SMG) seminars Mathematical Tools (MTT) instruction While no individual grants are specified, he participates in MiMed's funded projects including IndiPrint, Arburg Automated Design, and CarrierBot, which advance automated design methodologies and robotic applications. The MiMed research environment provides state-of-the-art facilities for robotics prototyping, medical device development, and additive manufacturing, supporting his work on bio-inspired mechanisms and surgical robotics systems with strong industry-academia collaboration.

• Artificial Intelligence
• 3D Scene Reconstruction
• Simultaneous Localization and Mapping (SLAM)
• +3 more

Yiming Zhou is a researcher at Saarland University of Applied Sciences (htw saar) in Saarbrücken, Germany. Their work spans multiple engineering domains with emphasis on artificial intelligence integration, 3D scene reconstruction, and advanced imaging techniques. Contact: yiming.zhou@htwsaar.de Location: Goebenstraße 40, 66117 Saarbrücken Research Focus Zhou's research explores cutting-edge technologies in: AI applications for non-destructive evaluation Dynamic SLAM systems for robotics Next-generation 3D reconstruction methods Multimodal deception detection frameworks Novel Gaussian splatting techniques Semantic encoding for spatial data Recent Publications Trends Their scholarly output reveals a trajectory toward Real-time spatial mapping solutions Hybrid neural-implicit representations CAD-integrated building documentation AI-enhanced image translation pipelines Signal processing innovations Cross-modal data fusion Laboratory Affiliation Zhou contributes to the faculty laboratories at htw saar, focusing on engineering research through practical implementations and algorithm development.

• Visualization
• Computer-Aided Geometric Design
• Computer-Aided Design
• +2 more

Georges-Pierre Bonneau is a Professor at University of Grenoble conducting research at Laboratoire Jean Kuntzmann (LJK) and INRIA in the MAVERICK research group. His academic career spans over three decades, beginning with his PhD from Universität Kaiserslautern in 1993, followed by positions as CNRS researcher (1994-2001), and Professor at University of Grenoble since 2001. Dr. Bonneau's research focuses on scientific visualization, computer-aided geometric design (CAGD), computer-aided design (CAD), and computational fabrication techniques. His work bridges theoretical geometric modeling with practical applications in structural monitoring, energy consumption visualization, and digital fabrication. He has developed innovative methods for surface reconstruction from sparse data, bending-active structures, and visualization of uncertain scalar data fields. His recent publications demonstrate a strong focus on immersive 3D modeling techniques, computational fabrication methods, and advanced visualization systems. Bonneau's research shows consistent evolution from foundational geometric modeling work in the 1990s-2000s toward contemporary applications in VR/AR interfaces, computational design of physical structures, and energy consumption visualization systems. Throughout his career, Bonneau has actively contributed to the academic community as conference organizer and program committee member for major venues including SPM, SMI, Eurographics, and IEEE Visualization conferences. He has supervised numerous PhD students whose work spans diverse applications of geometric modeling and visualization, with recent students focusing on surface creation from sparse strokes, bending-active structures, and structural monitoring systems. His former students have gone on to successful careers at institutions including AAC Clyde Space, Zurich Insurance, Pixminds, ISIR, and CEA.

• Machine Learning
• Quantum Computing
• Medical Data Analysis
• +3 more

Thomas Vecchiato is a Guest Researcher at the Department of Computer Science , University of Copenhagen , specializing in Machine Learning . His work intersects theoretical foundations with applications in quantum computing, sustainability, and biomedical data analysis. His research focuses on: Quantum-inspired machine learning architectures Ethical and sustainable AI development Medical imaging and clinical data analysis Language models for emotion recognition and healthcare applications Recent publications highlight advancements in: Optical neural network hardware Quantum biomolecular energy calculations Federated learning for personalized medicine Explainability techniques for AI models