Find Academic Supervisors

• Brain Imaging
• Data Analysis
• Three-Dimensional Imaging
• +3 more

Jean-Dominique Gallezot is a Research Scientist in the Department of Radiology & Biomedical Imaging at Yale School of Medicine . His work spans brain imaging , molecular imaging , and positron emission tomography (PET) with a focus on data analysis and whole-body imaging . Doctor of Philosophy (PhD) in Neuroscience from Université Pierre et Marie Curie (2006) Master of Science (MS) in Neuroscience from Université Pierre et Marie Curie (2002) Master of Engineering (ME) in Digital Telecommunications from École Supérieure d'Électricité (2002) Bachelor of Science (BS) in Physics from École Polytechnique (2000) Jean-Dominique’s research centers on neuroimaging and radiotracer development , particularly for studying dopamine receptors , neurotransmitter activity , and brain metabolism . His work leverages PET and SPECT to explore conditions like alcohol use disorder , parkinson’s disease , and frontotemporal dementia . Recent publications highlight his contributions to dynamic PET denoising using deep learning , test-retest reproducibility of novel radioligands, and kinetic modeling for drug interactions. Collaborations include key figures like Richard Carson and Nabeel Nabulsi , with co-authors across neuroscience , radiology , and pharmacology . His research integrates biomedical data science and computational imaging to advance quantitative analysis in PET studies. No scientific awards are explicitly mentioned in the provided texts.

Eugene FiumeView profile

Professor

• Computer Graphics
• Animation
• Fluid Simulation
• +3 more

Eugene Fiume is a Professor and Dean of the Faculty of Applied Sciences at Simon Fraser University (SFU). He holds a Ph.D. in Computer Science from the University of Toronto (1986), an M.Sc. from the same institution (1982), and a B.Math (Hons.) in Computer Science from the University of Waterloo (1981). His research focuses on computer graphics, animation techniques, and educational applications of technology. He leads the Tangent Lab, exploring advanced topics in fluid simulation, facial animation, and physics-based motion retargeting. His work spans interdisciplinary areas, including fluid dynamics, mesh processing, and game development. Notable contributions include VEMPIC (particle-based fluid simulation), animator-centric tools like Jali for lip synchronization, and vocal animation frameworks for expressive speech. He has held leadership roles in academic administration and has contributed to industry-research collaborations through his deanship. Publications emphasize innovation in animation systems, real-time rendering, and procedural modeling. His research has been applied in gaming (e.g., Cyberpunk 2077 facial animations) and medical imaging, reflecting a blend of theoretical and applied computer science.

Richard PalmerView profile

Researcher

• 3D Facial Analysis
• Rare Disease Diagnosis
• Medical Imaging
• +3 more

Dr. Richard Palmer is a Researcher at Curtin University, affiliated with the School of Earth and Planetary Sciences (EPS) within the Faculty of Science and Engineering. He holds a portfolio role in the Office of the Provost, demonstrating leadership in academic governance. His research focuses on innovative applications of 3D facial analysis for rare disease diagnosis, treatment monitoring, and public health initiatives. Key collaborations include the CLINIFACE project, which integrates non-rigid registration techniques for phenotypic visualization. Palmer’s work spans clinical research, medical imaging, and interdisciplinary projects such as the Western Australian Undiagnosed Diseases Program. His expertise bridges computer science, genetics, and public health, addressing challenges like autism spectrum disorder analysis and hereditary conditions like Silver Russel syndrome and hereditary angioedema. He has pioneered open-source tools for 3D facial growth curves and mobile-based 3D facial reconstruction. His publications emphasize methodological advancements in 3D modeling, algorithmic comparisons for spatial analysis, and the impact of BMI on facial clustering studies. Collaborators include institutions like the Human Phenotype Ontology consortium and global medical research networks. Palmer actively contributes to precision medicine through both technological innovations and policy-driven healthcare solutions.

• Geometric Shape Analysis
• Deep Learning
• Computer Vision
• +5 more

Maks Ovsjanikov is a Professor in the Department of Computer Science at École Polytechnique , France, and a Visiting Research Scientist at Google DeepMind. His research focuses on geometric 3D shape analysis , deep learning for non-rigid shape comparison , and applications of AI to scientific discovery , supported by the ERC Consolidator Grant VEGA . Research Interests : 3D shape processing, functional maps, deep learning, cryo-EM analysis, and robust non-rigid matching. Awards : ERC Consolidator Grant, Best Paper Awards at 3DV 2022 and 2019, Best Student Paper Award at 3DV 2020, Replicability Stamp (2017). Teams : Member of the GeomeriX team at LIX, École Polytechnique. Recent work trends highlight his leadership in zero-shot 3D keypoint detection , cross-domain video model evaluation , and diffusion-based shape matching priors . His students, including Souhaib Attaiki , have received top recognition like the IP Paris Best PhD Thesis award .

• Geometric Analysis
• Differential Geometry
• Relativity Theory
• +4 more

Martin Kell is a Senior Lecturer at the Department of Mathematics within the Faculty of Mathematics and Natural Sciences at the University of Tübingen. His work bridges geometric analysis and optimal transport theory , with a focus on metric spaces, curvature conditions, and non-linear PDEs. Education : Diplomarbeit (Master's Thesis): Conley Index of Isolated Equilibria , University of Rostock (2010) Dissertation: On Curvature Conditions Using Wasserstein Spaces , University of Leipzig (2014) Research Interests Kell explores the interplay of geometry and analysis , particularly in generalized curvature , geometric flows , and curvature-isoperimetry relations . His work includes studying metric spaces where Sobolev spaces are non-linear, aiming to define analogues of Alexandrov curvature for non-Riemannian settings. Publications Trends Kell’s research spans geometric analysis , differential geometry , and optimal transport . Key themes include non-linear gradient flows , Ricci curvature bounds , and metric space convergence . Teaching He has taught advanced courses such as: Optimal Transport (2016/2017): Covering transport mappings, entropy functionals, and geometric inequalities. Linear Partial Differential Equations (2017): Focusing on elliptic/parabolic equations, Sobolev spaces, and regularity theory. Limits of Spaces (2019): Introducing Gromov-Hausdorff convergence, Alexandrov geometry, and stability theorems. Contact: room_c6p34@uni-tuebingen.de (reconstructed from departmental contact structure).

Michael WandView profile

Professor

• Computer Graphics
• Machine Learning
• Geometry Processing
• +5 more

Michael Wand is a Professor at the Institute of Computer Science , Johannes Gutenberg University Mainz , leading the Visual Computing Group . His research bridges Computer Graphics and Computer Vision , focusing on data-driven methods and structure-aware processing of geometric data. Research Themes Machine learning for geometric data analysis Symmetry and regularity detection in 3D shapes Generative models of visual data Big data in geometry processing Publication Trends : His recent work (2015-2016) explores integrating Markov Random Fields and Convolutional Neural Networks for image synthesis, generative adversarial networks for real-time texture synthesis, and algebraic models for parameterized shape editing. Earlier contributions (2007-2013) focus on point cloud processing , non-rigid shape reconstruction , and interactive editing of complex geometries. Academic Background : Doctoral degree (2004) in Computer Science from University of Tübingen Diploma (2000) in Computer Science from Universität-GH Paderborn

Sami BrandtView profile

Professor

• Mathematical imaging
• Computer vision
• Machine learning
• +4 more

Sami Brandt is a Professor in Data Science and Machine Learning at the IT University of Copenhagen. Specializing in Audio-Visual Computing, they lead a research group focused on cutting-edge applications in computer vision, neural networks, and extended reality (XR). Their work bridges mathematical imaging with practical technological solutions. Research Interests: Latent Space Analysis in Diffusion Models Non-rigid Structure-from-Motion Techniques Human Motion Prediction and Smoothing Algorithms Tensor-based Methods for Emotion Recognition Metaverse Applications for Parkinson's Disease Therapies Scientific Awards: Best Student Paper Award (2022), 11th International Conference on Pattern Recognition Applications and Methods Projects: MotiVerseP (2024-2025): Motivation for movement therapies in a metaverse for Parkinson's disease XTREME (2024-2026): Extended Reality Environment for Immersive Experience of Art and Music

Giovanni TrappoliniView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Multimodal Deep Learning
• +5 more

Giovanni Trappolini serves as Assistant Professor at Sapienza University of Rome within the Department of Computer, Control, and Management Engineering, conducting research at the RSTLess Lab under Prof. Fabrizio Silvestri. Previously, he completed his Ph.D. in Machine Learning at Sapienza under Prof. Emanuele Rodolà, following an MSc in Data Science where he graduated cum laude as a Sapienza honor graduate. His educational background includes: MSc in Data Science, Sapienza University of Rome (cum laude, Sapienza honor graduate) Ph.D. in Machine Learning, Sapienza University of Rome (2022) BSc from Luiss Guido Carli Trappolini's research bridges Machine Learning and Deep Learning with emphases on multimodal systems and information retrieval . He pioneers applications in Graph Neural Networks security, Federated Learning architectures, and Italian-language Large Language Models —notably creating Fauno , Italy's leading LLM. His work extends to operating system innovation through generative AI, 3D shape analysis using transformers, and creative applications like AI-driven music generation. Analysis of his 2023 publications reveals dominant trends toward integrating retrieval systems with generative models (RAG), developing robust neural databases, and enhancing cross-modal understanding. Key themes include adversarial defense for graph networks, privacy-preserving federated retrieval, and transformer-based geometric learning—demonstrating consistent focus on foundational AI infrastructure. Scientific recognition includes: Sapienza honor graduate Cum laude graduate distinction Trappolini actively contributes to academic instruction through courses including Advanced Data Mining and Language Technologies (Sapienza, 2023) and multiple iterations of Python Programming for Data Science (2019-2023). He maintains significant research collaborations with Stanford, Technion, Meta, Amazon, TII, and UniPi while preparing new PhD-level coursework in Geometric Deep Learning for 2024. His research operates within the RSTLess Lab ecosystem, focusing on scalable AI systems and multimodal integration.

Dan B GoldmanView profile

Adjunct Associate Professor

• Computer Graphics
• Computer Vision
• Artificial Intelligence
• +3 more

Dan B Goldman is an affiliate associate professor at the University of Washington's Computer Science and Engineering department, while serving as a Principal Engineer and AI/ML architect for Lucasfilm & ILM. His career bridges academia and industry, with prior roles at Adobe's Creative Technologies Lab (2007-2015) and Google (2015-2024), where he led computer vision R&D for Project Starline. He is a member of the Visual Effects Society and holds a PhD in Computer Science (2007) from the University of Washington. Research Focus: Computer graphics, computer vision, artificial intelligence, and human-computer interaction Notable Projects: Content-Aware Fill (Photoshop), Project Starline (Google), HyperNeRF (neural radiance fields), and Nerfies (deformable 3D modeling) Academic Legacy: Mentored by David Salesin, with an Erdős-Bacon number of 6 (via Rose Byrne and David Salesin's academic lineage). His work appears in top venues like SIGGRAPH, CVPR, and ECCV, covering topics from neural rendering to 3D reconstruction. Industry Impact: Pioneered technologies used in major films (Star Wars, Jurassic Park) and Adobe Photoshop features. His recent work at Google Labs (2022-2024) focused on generative media, reflecting his dual expertise in creative tools and computational methods.

• Computer Graphics
• Physics-Based Simulation
• Fluid Dynamics
• +3 more

Prof. Dr. Jan Bender holds a professorship in Computer Animation at RWTH Aachen University's College of Engineering. As a leading researcher in physics-based simulation methods, his work focuses on developing advanced numerical techniques for fluid dynamics, deformable solids, and multi-physics interactions through Smoothed Particle Hydrodynamics (SPH) and Finite Element Methods (FEM). Key Contributions: Invented PF-FLIP for two-phase flows, developed SymX symbolic framework for energy-based simulations, created STARK unified solver for robotics applications, and introduced implicit boundary handling for SPH Methodologies: Specializes in hybrid Eulerian/Lagrangian approaches, differentiable physics, adaptive discretization, and machine learning integration for simulation acceleration Research Impact: 2023 & 2024 Best Paper Awards in VMV and SCA conferences. His work enables billion-particle fluid simulations and realistic multi-body interactions for robotics, with applications in welding, thermal spraying, and soft robotics. Collaborations: Works extensively with robotics institutes (Gazebo Fluids extension) and materials science departments (TIG welding, thermal spray modeling). Maintains open-source code repositories for simulation frameworks.

Slobodan IlicView profile

Adjunct Professor

• 3D Computer Vision
• Computer Vision
• Medical Imaging
• +3 more

PD Dr. Slobodan Ilic is a Senior Key Expert Research Scientist at Siemens AG (since 2014) and an Adjunct Professor at the Chair of Computer Science Applications in Medicine , Technical University of Munich (TUM). His work bridges 3D computer vision and medical imaging , with a focus on real-time object detection, deformable surface modeling, and depth data analysis. Current Roles: Adjunct Professor at TUM, Senior Key Expert at Siemens Research Themes: 6D pose estimation, non-rigid 3D reconstruction, RGB-D data processing Labs: CAMP Chair (TUM), Siemens AG Research Division His recent work explores LLM-driven control systems , semantic-aware 3D generation , and cross-modal medical imaging . Articles highlight advancements in point cloud registration , rotation-invariant descriptors , and hyperspectral calibration . While no specific awards are documented in the provided text, his team at Siemens/TUM advises PhD candidates in 3D vision for robotics and medical applications .

• Robotics
• Control Systems
• Human-Machine Interaction
• +4 more

F. Tajdari is a Research Fellow in the Department of Mechanical Engineering at Delft University of Technology, specializing in intelligent vehicles and robotics. Tajdari completed a PhD at TU Delft in 2023 with a dissertation on non-rigid 3D/4D human mesh registration, establishing expertise at the intersection of mechanical engineering and computer vision. Education: PhD in Mechanical Engineering, Delft University of Technology, 2023 Research focuses on advanced control systems for robotics and human-vehicle interaction, with significant contributions to non-rigid mesh registration, adaptive feedback control, and intelligent transportation. Tajdari's work integrates deep learning (Graph Convolutional LSTM networks) with mechanical control theory to solve dynamic shape registration and vehicle navigation challenges, particularly for vulnerable road users. Recent publications (2022-2025) reveal a cohesive research trajectory: evolving from Stewart robot control (2022) to sophisticated non-rigid registration techniques (2024), culminating in vehicle systems for human-robot coexistence (2025). The work consistently bridges mechanical engineering with computer science, emphasizing real-world applications in safety-critical environments. No scientific awards were documented in the available records. No information regarding student advising or research grants was identified in the source materials. Tajdari operates within TU Delft's Intelligent Vehicles research ecosystem, collaborating extensively with the Delft Robotics Institute and contributing to projects involving multi-agent vehicle systems and human-centered robotics, as evidenced by co-authorship with leading researchers in autonomous systems.

Timo PlathView profile

Researcher

• Multiscale Modeling
• Suspensions Rheology
• Granular Matter
• +5 more

Timo Plath is a Researcher at the Multiscale Mechanics (MSM) group within the Faculty of Thermal and Fluid Engineering at the University of Twente, Netherlands. He is affiliated with the Department of Engineering Technology and the MESA+ Institute for Nanotechnology. As a PhD candidate, his work bridges engineering technology and computational modeling for industrial applications. Plath specializes in multidisciplinary engineering with research interests spanning contact mechanics, granular matter, non-Newtonian flow rheology, concrete simulation, and industrial process modeling. His expertise particularly focuses on developing multiscale models where both micro and macro-scale effects are critical, with significant contributions to wet granulation processes and particle dynamics simulation. His publication trend shows consistent advancement in particle-based modeling techniques, particularly through the open-source MercuryDPM software platform. Recent work emphasizes quadrature method of moments applications, non-dimensionalization approaches, and virtual prototyping for industrial granulation processes, demonstrating a clear trajectory from fundamental particle dynamics to practical industrial implementation. Plath actively collaborates within the University of Twente research ecosystem, working closely with supervisors including Thomas Weinhart, Stefan Luding, and Anthony Thornton. His activities include multiple oral presentations at international conferences between 2022-2024, focusing on model order reduction, quadrature methods, and granulation process characterization. His research infrastructure includes five significant datasets published through 4TU.Centre for Research Data between 2021-2024, supporting reproducibility in twin-screw wet granulation modeling, quadrature method applications, and rapid prototyping of granulation equipment. These datasets demonstrate his commitment to open science and computational reproducibility in particle technology research.

• Computer Vision
• Biometrics
• Face Recognition
• +3 more

Xiaoming Liu is a Professor at Michigan State University with extensive contributions to computer vision and biometrics. His research spans face recognition, 3D reconstruction, image forgery detection, and adversarial machine learning, with over 300 publications from 1999 to 2025. His primary research interests include Computer Vision , Biometrics , and Adversarial Machine Learning . Liu's work focuses on developing robust systems for face recognition at scale, detecting image manipulations, and creating 3D reconstruction techniques. Recent projects include SapiensID for human recognition, FRCSyn for synthetic face recognition, and proactive watermarking schemes. Liu's publication trends show increasing focus on multi-modal biometrics (combining face, body, and gait), image forgery detection using hierarchical approaches, and adversarial defense mechanisms . His 2023-2025 work emphasizes synthetic data applications and physics-driven recognition systems. Liu has mentored numerous students including Feng Liu, Minchul Kim, and Xiao Guo, who frequently co-author his papers. His research has been supported by grants enabling projects like FarSight (long-range biometrics) and ProMark (proactive watermarking). He leads research in biometrics security and computer vision, with recent work focusing on ethical AI applications and robust recognition systems. His team develops tools for detecting deepfakes and improving recognition in challenging conditions.

Michael PosaView profile

Assistant Professor

• Robotics
• Control Theory
• Machine Learning
• +3 more

Michael Posa is an Assistant Professor in Mechanical Engineering and Applied Mechanics at the University of Pennsylvania's School of Engineering and Applied Science. He also holds affiliations with the Departments of Computer and Information Science, and Electrical and Systems Engineering. As the head of the Dynamic Autonomy and Intelligent Robotics (DAIR) Lab, part of the GRASP Lab, his research focuses on control, learning, and planning for robots interacting dynamically and safely with complex environments. Key interests include non-smooth dynamics of contact, machine learning, and numerical optimization, with applications in legged robots and robotic manipulation. His work emphasizes computationally efficient algorithms for real-time control and decision-making. Recent achievements include the 2024 Best Paper Award for contributions to multi-contact model predictive control. He actively mentors students like Brian Acosta and William Yang, whose theses address bipedal walking and dynamic manipulation. The DAIR Lab collaborates on interdisciplinary projects and participates in robotics conferences like ICRA. Michael’s lab emphasizes diversity and innovation, recruiting students across MEAM, ESE, and CIS departments. His research bridges theory and practice, with publications spanning model reduction for legged systems, vision-based contact localization, and impact-aware control strategies. Ongoing efforts explore contact-implicit MPC frameworks and integrating physics-driven perception (e.g., Vysics) for robust autonomy.