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Yavar KianView profile

Professor

• Inverse Problems for Partial Differential Equations
• Fractional Diffusion Equations
• Spectral Inverse Problems
• +7 more

Yavar Kian is a Professor at the University of Rouen Normandy, affiliated with the Raphaël Salem Mathematics Laboratory (LMRS), UMR 6085 CNRS. He is based in the Faculty of Science and Technology at the university's Saint-Étienne-du-Rouvray campus. His research focuses on advanced mathematical problems related to partial differential equations and their applications. His primary research interests include inverse problems for nonlinear partial differential equations (elliptic, hyperbolic, parabolic), fractional diffusion equations, spectral inverse problems, inverse problems on Riemannian manifolds, and the Calderón problem. His work specifically addresses coefficient determination, source identification, and stability analysis for various evolution equations. Key methodological approaches in his research involve Carleman estimates, Strichartz estimates, and boundary control methods. Analysis of his recent publications (2022-2023) reveals a strong focus on fractional diffusion equations, nonlinear inverse problems, and time-dependent coefficient determination. His work demonstrates increasing sophistication in handling non-smooth coefficients, distributed order fractional derivatives, and partial boundary data. The research spans theoretical developments in stability analysis and practical applications in imaging and tomography. Kian maintains extensive international collaborations with mathematicians across Europe, Asia, and North America, particularly working with researchers from institutions in France, Japan, Finland, and the United States. His work appears in top-tier mathematics journals including SIAM Journal on Mathematical Analysis, Archive for Rational Mechanics and Analysis, and Communications in Contemporary Mathematics.

Charles MallesonView profile

Research Fellow

• Computer Vision
• 3D Reconstruction
• Motion Capture
• +5 more

Dr. Charles Malleson is a Research Fellow in Computer Vision at the Centre for Vision, Speech and Signal Processing (CVSSP) at the University of Surrey, UK. His work focuses on computer vision, 3D reconstruction, and motion capture technologies using RGB-D sensors and multi-view video systems. Malleson's educational background includes a PhD, MSc, and BEng (Hons), though specific institutions are not mentioned in the provided text. His research interests span computer vision, 3D reconstruction, motion capture, RGB-D data processing, visual alignment, human pose estimation, graphics, and more recently, animal pose estimation with a focus on canine subjects. His recent publications reveal a strong research trajectory focused on practical applications of computer vision. Beginning with foundational work in 3D reconstruction and motion capture using RGB-D sensors and multi-view video systems, his research has evolved toward increasingly specialized applications including wearable visual correction devices, animal pose estimation, and neural network-based image processing. A notable trend is his expansion from human motion capture to animal pose estimation, developing synthetic datasets like SyDog for training deep learning models. His work often combines multiple sensor modalities and emphasizes real-time processing capabilities for practical deployment. Scientific Awards: Leverhulme Trust Early Career Fellowship Malleson has been instrumental in developing the TotalCapture dataset, which combines multi-viewpoint video, IMU sensor data, and accurate 3D skeletal joint ground truth. His research has received significant funding, including the prestigious Leverhulme Trust Early Career Fellowship. His work bridges academic research and practical applications, particularly in medical devices (visual alignment correction) and animal pose estimation technologies. As a member of CVSSP, Malleson contributes to one of the world's leading vision, speech, and signal processing research centers. His work on hybrid modeling of non-rigid scenes, real-time motion capture, and volumetric graphics demonstrates the center's commitment to pushing the boundaries of visual computing technology. His recent focus on animal pose estimation represents an innovative expansion of traditional human-centered computer vision research.

• Political Geography
• Environmental Geography
• Biopolitics
• +5 more

Kevin Grove is an Assistant Professor in the Department of Global & Sociocultural Studies at Florida International University, USA. His work critically examines resilience, human-environment relations, and disaster governance through interdisciplinary lenses spanning geography, anthropology, and political theory. Academic Affiliation: Florida International University, Department of Global & Sociocultural Studies Research Focus: Biopolitics, postcolonial urbanism, energy insecurity, and the politics of resilience in the Caribbean and U.S. contexts In his book Resilience (Key Ideas in Geography, 2018), Grove traces resilience to cybernetic behavioral science, neoliberal economics, and modernist design, arguing that resilience thinking prioritizes a pragmatic “will to design” over traditional critique. His recent Google Scholar publications (2025–2023) explore spatial stigma in Puerto Rican physician migration, energy independence in disaster zones, reparative governance frameworks, and the intersection of race with resilience planning in Miami. These works reflect his ongoing interest in decolonial approaches to urban experimentation and the ethics of disaster management.

Yuxuan XueView profile

Researcher

• Computer Vision
• 3D Reconstruction
• Neural Networks
• +3 more

Yuxuan Xue is a PhD student in the Real Virtual Humans group at the University of Tübingen, supervised by Prof. Dr. Gerard Pons-Moll. He is affiliated with the International Max Planck Research School for Intelligent Systems (IMPRS-IS). Prior to his PhD, he earned B.Sc. and M.Sc. degrees in Mechanical Engineering and Robotics from the Technical University of Munich (TUM), with his Master's thesis conducted at the Max Planck Institute for Intelligent Systems. His research focuses on 3D human modeling, neural fields, and diffusion models for avatar creation and clothed body reconstruction from monocular data. Education: PhD in Computer Science, University of Tübingen (2022–present) M.Sc. Mechanical Engineering & Robotics, Technical University of Munich (2020–2022) B.Sc. Mechanical Engineering, Technical University of Munich (2017–2020) Research Interests: Yuxuan’s work bridges computer vision and graphics, emphasizing: 3D human reconstruction from sparse/monocular data Neural fields for animatable body modeling Diffusion models for coherent 3D generation Event-based cameras for non-rigid tracking Parameter-efficient model fine-tuning Publications: His contributions include groundbreaking work on NSF (Neural Surface Fields) and Human 3Diffusion, advancing the state-of-the-art in 3D avatar creation and cloth modeling. Awards: Best Student Paper Award at BMVC 2022 OpenAI Research Access Program Grant ($5000, 2024) Multiple top-tier conference acceptances (NeurIPS, ICCV, ICLR) Grants and Funding: Supported by the Carl Zeiss Foundation, DFG Emmy Noether Programme, and BMBF Tübingen AI Center. Labs and Collaborations: Active in the Real Virtual Humans group and collaborates with institutions like Meta Reality Labs and MPI for Informatics.

• Visual Analytics
• Machine Learning
• Digital Geometry Processing
• +6 more

Dr. Gary KL Tam is a Senior Lecturer in the Department of Computer Science at Swansea University's School of Mathematics and Computer Science. He has been with Swansea University since 2012, initially as a Lecturer (2012-2018) and promoted to Senior Lecturer in 2018. He is based at The Computational Foundry on Bay Campus and is actively involved in research through the Visual Computing Group and RIVIC. Dr. Tam's research spans several key areas in computer science, with a strong focus on visual analytics, machine learning, and digital geometry processing. His work demonstrates expertise in analyzing multidimensional data, information retrieval, and pattern recognition. He has developed substantial research in 3D reconstruction techniques, point cloud processing, and their applications in medical imaging and urban modeling. His publication record shows a clear evolution from foundational work in 3D shape matching and non-rigid registration to current applications in medical diagnostics (particularly Chronic Kidney Disease classification), Welsh language processing (CymruFluency project), and industrial applications. Recent publications demonstrate strong interdisciplinary work bridging computer vision, machine learning, and healthcare applications. Best VAST Paper Award at VIS 2016 Conference for 'Analysis of Machine- and Human-Analytics in Classification' Excellence in Learning and Teaching Award (ELTA) from Swansea University (2018) Dr. Tam actively supervises numerous PhD students across diverse research topics including explainable AI for economic growth, 3D city reconstruction, medical diagnostics, and human-in-the-loop systems. He has secured multiple research grants totaling over £50,000 from sources including CHERISH-DE, EPSRC, and Swansea University funds, supporting projects in medical AI, Welsh language processing, and computational infrastructure. His research group maintains strong collaborations with Cardiff University, National University of Singapore, and various medical research institutions, focusing on applying advanced computational techniques to real-world problems in healthcare, language processing, and industrial applications.

Richard J. SmithView profile

Professor

• Inherited Hearing Impairment
• Genetic Deafness
• Non-syndromic Hearing Loss
• +16 more

Richard J. Smith, MD, is a Professor at the University of Iowa, holding appointments in the Department of Otolaryngology, Internal Medicine, Pediatrics, Molecular Physiology and Biophysics, and Anatomy and Cell Biology within the Carver College of Medicine. He directs the Molecular Otolaryngology and Renal Research Laboratories (MORL) and is a key figure in the Iowa Institute of Human Genetics. His work bridges clinical otolaryngology and molecular genetics. BA in Biochemistry, Rice University MD, Baylor College of Medicine Internship and General Surgery Residency, University of Texas Health Science Center Otolaryngology Residency, Baylor College of Medicine Pediatric Otolaryngology Fellowship, The Hospital for Sick Children, London Postdoctoral Fellowship, Medical Research Council Institute of Hearing Research, Nottingham Dr. Smith’s research centers on the genetics of inherited hearing loss and complement-mediated renal diseases. His lab investigates both syndromic and non-syndromic forms of deafness, employing linkage analysis, mutation screening, and functional studies in mouse models. A major focus is on gene discovery and developing RNAi-based therapies for hearing restoration. Concurrently, his team studies MPGN II (Dense Deposit Disease) and atypical HUS, exploring the role of complement dysregulation and its links to age-related macular degeneration. His work has significant implications for diagnosis, prognosis, and treatment of these rare disorders. The recent publications highlight a strong trend in genetic discovery in hearing loss (e.g., CLRN2 , MYO15A ) and mechanistic insights into complement pathway disorders. Collaborative clinical consensus statements reflect leadership in pediatric otolaryngology and airway management. Research spans molecular genetics, functional genomics, immunology, and translational therapeutics. Dr. Smith is actively involved in mentoring through multiple graduate programs including Biomedical Science, Genetics, Neuroscience, and the Medical Scientist Training Program. He leads a multidisciplinary research team at MORL and has contributed to national and international guidelines. His work is supported by institutional affiliations with the Center for Biocatalysis and Bioprocessing and the Iowa Institute of Human Genetics. Future directions include expanding genetic testing (e.g., PHASE Program), refining RNAi therapies, and developing targeted complement inhibitors. Laboratory: Molecular Otolaryngology and Renal Research Laboratory (MORL)

Justus ThiesView profile

Professor

• Computer Graphics
• Computer Vision
• Machine Learning
• +3 more

Justus Thies is a Professor at the Technical University Darmstadt (since September 2023), leading research at the intersection of computer graphics , computer vision , and machine learning . His work focuses on real-time 3D reconstruction, tracking, and photorealistic rendering of humans and environments using commodity hardware, with applications in AR/VR , movie production , and 3D telepresence for collaborative virtual environments. Current faculty member at Technical University Darmstadt Research themes: 3D reconstruction, neural rendering, hybrid machine learning/Graphics methods Applications: AR/VR, telepresence, human-environment modeling His research emphasizes neural rendering and deep learning approaches for facial and body modeling, including projects like GEM (Gaussian Eigen Models), SynShot (synthetic prior inversion), and TeCH (text-guided clothed human reconstruction). Collaborations include institutions like Max Planck Institute for Intelligent Systems and Google . Key trends in his recent publications include: neural Gaussian splatting for real-time avatars, diffusion models for speech/audio-driven animation, and hybrid approaches that combine classical computer graphics principles with modern machine learning techniques. These methods address challenges in generalizability , controllability , and editability of 3D representations. Notable research contributions since 2014 include advancements in monocular facial reconstruction , real-time expression transfer , and neural rendering techniques that enable 3D scene synthesis from unpaired or sparse data. His work has received significant recognition, including Face2Face featured in Communications of the ACM (2019) and multiple publications at top venues like CVPR , ECCV , and SIGGRAPH .

Hidekata HontaniView profile

Professor

• Image Processing
• Medical Image Analysis
• Computational Anatomy
• +3 more

Hidekata Hontani is a Professor at the Graduate School of Engineering, Nagoya Institute of Technology, specializing in image processing and medical image analysis. His research focuses on computational anatomy, deep learning applications in medical imaging, and pathological image analysis, particularly for cancer diagnosis and treatment. Professor Hontani's research interests center around medical image processing , with specific focus on computational anatomy , deep learning for medical applications , pancreatic cancer analysis , and malignant lymphoma grading . His work bridges computer science and medical diagnostics, developing innovative algorithms for analyzing pathological images and creating 3D models of tumors. His recent publications (2023-2024) show a strong trend toward applying diffusion models and weakly supervised learning techniques to medical image analysis, particularly for cancer diagnosis. Key areas include pathological image analysis , tumor microenvironment modeling , anomaly detection , and multi-modal image registration , with increasing focus on lymphoma grading and pancreatic cancer analysis. Scientific Awards: Multiple Japanese Society of Medical Imaging Technology Conference Encouragement Awards (2012-2024) JAMIT Young Medical Imaging Engineering Symposium Encouragement Awards (2023) Cum Laude Poster Award, SPIE Medical Imaging (2018) Student Award, VISAPP 2017 Professor Hontani has secured multiple significant research grants, including an ongoing Grant-in-Aid for Scientific Research (B) from the Japan Society for the Promotion of Science titled Comprehensive Characterization of All Two Million Nuclei in Pathology Images for Quantitative Assessment of Atypia in Malignant Lymphoma (2025-2028). He serves on key academic committees including as Secretary of the Medical Imaging Research Group at IEICE since 2010 and as a committee member of the Pattern Recognition and Media Understanding Research Group since 2013.

Alexandre CaboussatView profile

Consultant Lecturer

• Numerical Analysis
• Scientific Computing
• Numerical Optimization
• +5 more

Alexandre Caboussat is a Consultant Lecturer at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences in the Mathematics Department. He contributes to the Euler Programme and the SMA - Teaching unit, focusing on advanced mathematical analysis and computational methods. Research Interests : Numerical Analysis, Scientific Computing, Numerical Optimization, Variational Problems, Computational Fluid Dynamics, Free Surface Flows, Air Quality Modeling, and Computational Chemistry. His recent work emphasizes numerical methods for partial differential equations, particularly the Monge-Ampère equation, adaptive finite element techniques, and physics-informed neural networks. Applications span from optimal transport to laser surface melting and environmental flows. Students : Girardin Maude, Peruso Anna, Diserens Léo Aurélio, Gourzoulidis Dimitrios, Landry Chantal, Mrad Arwa. Contact : alexandre.caboussat@epfl.ch

Bruno ValletView profile

Researcher

• Geographic Information Science
• Computer Vision
• Remote Sensing
• +7 more

Bruno Vallet is a Senior Researcher at IGN (French National Institute of Geographic and Forest Information) within the LASTIG lab and leads the ACTE research team since 2019. His work focuses on geospatial data processing, including LiDAR and image registration, 3D urban modeling, and computer vision applications. He contributes to projects like AI4GEO and Time Machine , specializing in large-scale point cloud analysis and structured city reconstruction. Education : Habilitation (HDR) in Geographic Information Science (Univ Paris-Est, 2016), PhD in Computer Science (Institut National Polytechnique de Lorraine, 2008), and Master's in Computer Vision (Telecom ParisTech, 2005). His research integrates surface reconstruction , semantic labelings , and uncertainty propagation , with methodologies applied to autonomous navigation and urban change detection. Recent publications emphasize data fusion, visibility computation, and deep learning for 3D scene analysis. He co-supervises PhD students and leads teaching activities at ENSG (National School of Geographic Sciences), covering image processing and 3D data structures. Bruno Vallet also chairs the ISPRS Working Group II/4 on 3D Scene Reconstruction, demonstrating leadership in photogrammetry and remote sensing communities.

Matej KristanView profile

Professor

• Computer Vision
• Visual Object Tracking
• Detection and Segmentation
• +3 more

Matej Kristan is a Full Professor at the Faculty of Computer and Information Science , University of Ljubljana, where he serves as Vice Chair of the Department of Artificial Intelligence . He leads the Visual Object Tracking (VOT) Initiative , presides over the IAPR Slovenian Pattern Recognition Society , and acts as Associate Editor for IJCV . PhD from Faculty of Electrical Engineering, UL (2008) Co-organized 13+ workshops/conferences Research Interests focus on: Visual Object Tracking : Long-term re-detection, transparent object tracking, segmentation-based methods Autonomous Boats : Obstacle detection, sensor fusion, maritime navigation Physics-Informed Deep Learning : Sea level forecasting (HIDRA models), climate prediction Surface Defect Detection : Real-time deep architectures for cracks/dents Scientific Excellence : 26 Research Excellence Awards (Slovenian Research Agency) 10 Teaching Excellence Awards (UL/FRI) Best Paper Awards: ISPA2015, BMVC2022, Pattern Recognition Journal 2024 Student Mentorship : Supervised 8 PhD students including Alan Lukežič (summa cum laude 2021), Domen Tabernik , and Peter Uršič . His students have won 23 awards including multiple Prešeren Awards and Uroš Seljak distinctions. Laboratory Involvement : Key member of the Visual Cognitive Systems Laboratory , contributing to multimodal perception systems for maritime robotics, educational robotics platforms, and advanced deep learning architectures.

• Positron Emission Tomography (PET)
• Magnetic Resonance Imaging (MRI)
• Biomedical Informatics
• +4 more

Thibault Marin, PhD, is an Assistant Professor at the Yale School of Medicine in both the Department of Radiology & Biomedical Imaging and the Biomedical Informatics & Data Science program. He is affiliated with the Bioimaging Sciences Division , Center for Brain & Mind Health , and Center for Molecular Imaging Technology and Translation (CMITT) . Education: PhD in Electrical Engineering from Illinois Institute of Technology (2010) Key Collaborations: Georges El Fakhri, Chao Ma, Yue Zhuo, Jinsong Ouyang, Paul Han His research focuses on PET/MRI instrumentation , dynamic imaging reconstruction , and motion correction algorithms . He develops diffusion models for tumor segmentation and Bayesian methods for kinetic parameter estimation, contributing to advancements in radiomics-driven radiotherapy and ultra-high resolution PET systems . Recent publications highlight innovations in TOF-DOI panel detectors , linear tangent space alignment (LTSA) for cardiac imaging, and adversarial domain generalization for PET denoising. His work bridges biomedical engineering , machine learning , and clinical imaging applications .

Henry YuenView profile

Assistant Professor

• Quantum Computing
• Computational Complexity Theory
• Cryptography
• +3 more

Henry Yuen is an Assistant Professor of Computer Science at Columbia Engineering, Columbia University. He is affiliated with the Cybersecurity and Foundations of Data Science centers. His research focuses on quantum computing, complexity theory, cryptography, and information theory. Yuen holds a BA in Mathematics from the University of Southern California (2010) and a PhD in Computer Science from MIT (2016). He has received prestigious awards including the NSF CAREER Award and a Sloan Fellowship. His work bridges quantum mechanics and computational complexity, with contributions to quantum non-locality, pseudorandomness, and cryptographic protocols. His recent research explores quantum advantage in algorithms, quantum circuit design, and quantum cryptographic primitives. Notable achievements include advancing the theoretical foundations of quantum pseudorandomness and demonstrating hardness results in quantum learning and state tomography. Key contributions span quantum interactive proofs, error correction, and quantum money schemes. His grants include funding for exploring theoretical foundations of quantum pseudorandom primitives. He collaborates on projects like unitary complexity analysis and superdense coding rigidity, contributing to both algorithmic and foundational aspects of quantum computing.

• Virtual Reality
• Augmented Reality
• Artificial Intelligence
• +3 more

Professor Wen Tang is a leading academic in Digital Games Technology at Bournemouth University, appointed as Professor in 2016. She holds a PhD in Computer Science from the University of Leeds, UK, with prior roles as a Research Fellow at Leeds and Bradford Universities. Her expertise spans Virtual Reality (VR), Augmented Reality (AR), AI, and medical applications, focusing on surgical robotics and endoscopy. She leads major research projects, including the Horizon-MSCA-2024-IP-01-01:3D-Intel Surgery initiative starting in 2025. Her research integrates VR/AR algorithms, AI-driven medical imaging, and robotics. Notable grants include EPSRC, Innovate UK, EU H2020, and Royal Academy of Engineering funding, totaling over £2 million. She serves on the EPSRC Peer Review College and has published extensively in journals like IEEE Transactions on Medical Imaging and Neurocomputing. Key projects include developing interactive surgical simulation tools, point cloud registration methods, and gamified health interventions. Her work aligns with UN SDGs for Quality Education, Decent Work, and Sustainable Innovation. Future work emphasizes 3D medical visualization and healthcare technology integration. Publications highlight advancements in medical image fusion, AI for surgical robotics, and VR safety training. She collaborates widely, mentoring teams in digital health and extended reality applications.

• Robotics and Automation
• Control Systems
• Biomedical Engineering
• +4 more

Dr. Tajdari is a distinguished researcher at Delft University of Technology, specializing in interdisciplinary fields at the intersection of robotics, control systems, and biomedical engineering. His work focuses on advanced control methodologies, non-rigid registration techniques for medical imaging, intelligent transportation systems, and personalized product design through digital fabrication. He has contributed significantly to the development of adaptive controllers for robotics systems, including Stewart platforms and surgical robots, as well as innovative approaches to 3D/4D human mesh registration for applications in healthcare and biomechanics. Key Research Areas: Autonomous Vehicles, Medical Robotics, Intelligent Transportation, 3D Reconstruction, and Human-Motion Analysis His recent research emphasizes integrating machine learning with traditional control theory to address challenges in traffic management, spinal deformity prognosis, and human-centered design. Collaborations include projects on smart infrastructure systems and personalized medical devices. Over 25 peer-reviewed articles demonstrate his expertise in both theoretical and applied engineering domains.