جست‌وجوی استاد و پژوهشگر

Carola-Bibiane Schönliebمشاهده پروفایل

استاد

• Image Analysis
• Image Processing
• Inverse Problems
• +۳ مورد دیگر

Carola-Bibiane Schönlieb is a Professor of Applied Mathematics and head of the Cambridge Image Analysis (CIA) group at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge. She concurrently serves as co-director of the Cambridge Mathematics of Information in Healthcare (CMIH) Hub, leading interdisciplinary initiatives at the intersection of mathematics, healthcare, and data science. Her research centers on variational methods, partial differential equations, and machine learning for image analysis, processing, and inverse problems. She maintains active collaborations with clinicians, biologists, physicists, chemical engineers, plant scientists, artists, and art conservators, driving innovations in biomedical imaging, image sensing, and digital art restoration. This interdisciplinary approach bridges theoretical mathematics with real-world applications across healthcare and cultural heritage domains. Analysis of her recent publications reveals a dominant focus on deep learning applications for medical imaging challenges, particularly in cardiology, oncology, and neuroimaging. Her work consistently addresses inverse problems in reconstruction and segmentation while emphasizing robustness against artifacts, model efficiency, and integration of physical constraints. A clear trend emerges toward foundation models and transfer learning techniques specifically adapted for medical image analysis with limited annotated data. Prof. Schönlieb leads the Cambridge Image Analysis research group and co-directs the CMIH Hub, which unites mathematicians, computer scientists, and clinicians to translate advanced data science into clinical practice through collaborative healthcare innovation.

Jianliang Qianمشاهده پروفایل

استاد

• Inverse Problems
• Scientific Computing
• Numerical Analysis
• +۷ مورد دیگر

Jianliang Qian is a Professor at the Department of Mathematics and Department of Computational Mathematics, Science and Engineering at Michigan State University , East Lansing, MI. He earned his Ph.D. from Rice University and has held academic appointments at MSU since 2005, including roles as Assistant, Associate, and Full Professor. Since 2015, he has served as Director of the Michigan Center for Industrial and Applied Mathematics . Research Interests: His work focuses on big-data analysis via inverse problems , fast algorithms for geophysical and medical imaging , microlocal analysis , numerical methods for PDEs , and optimal control . Key applications include high-frequency wave modeling, seismic inversion, and genome architecture reconstruction. Article Trends: Recent publications emphasize high-frequency wave propagation , level-set inversion techniques , machine learning integration into wave modeling, tensor methods for high-dimensional problems, and adaptive algorithms for Helmholtz and Hamilton-Jacobi equations. Leadership: As Director of the Michigan Center for Industrial and Applied Mathematics, he organizes seminars and workshops, including the Conference on Wave Challenges (2015) and MCIAM Seminars series.

Kent-Andre Mardalمشاهده پروفایل

استاد

• Computational Mechanics
• Biomechanical Applications in Medicine
• Brain Clearance During Sleep
• +۵ مورد دیگر

Kent-Andre Mardal is a Professor at the Department of Mathematics , University of Oslo . He specializes in computational mechanics with a strong focus on biomechanical applications in medicine , particularly in modeling brain clearance mechanisms during sleep. His work integrates multi-physics modeling , fluid-structure interaction , and poroelastic couplings to advance understanding of the glymphatic system and cerebrospinal fluid dynamics. Education: PhD (2002) – Simula Research Laboratory Research Interests: Mardal's research spans a wide range of disciplines including: Computational Mechanics – developing robust numerical algorithms for complex physical systems Biomechanical Applications – particularly in neuroscience and medical imaging Brain Clearance During Sleep – modeling the glymphatic system and CSF flow dynamics Multi-Physics Modeling – integrating fluid dynamics, elasticity, and neural networks Finite Element Methods – for accurate and efficient simulations Neural Networks in Scientific Computing – exploring physics-informed neural networks Research Trends from Publications: Mardal's recent publications (2022–2025) demonstrate a clear focus on brain fluid dynamics , particularly the glymphatic system , CSF circulation , and neurodegenerative disease modeling . He employs advanced numerical techniques such as isogeometric analysis , physics-informed neural networks , and parameter-robust preconditioning to solve complex multi-physics problems. His work bridges medical imaging (MRI) with computational modeling to provide insights into brain clearance mechanisms and their impairment in diseases like Alzheimer's. Scientific Awards: No specific awards are mentioned in the provided text. Grants and Projects: Currently, Mardal is the Principal Investigator (PI) of three active research projects: Alzheimer's Physics – exploring the role of fluid dynamics in neurodegeneration Scientific Machine Learning – advancing numerical methods with AI Computational Hydrology – modeling subsurface fluid flow Affiliations and Teams: Mardal was previously a group leader at the Centre of Excellence “Biomedical Computing” at the Simula Research Laboratory. He has authored over 100 papers and several books, and his research homepage is available at https://kent-and.github.io/ .

David Coeurjollyمشاهده پروفایل

استاد پژوهشی

• Digital Geometry
• Geometry Processing
• Point Sampling
• +۴ مورد دیگر

David Coeurjolly is a Research Director at the French National Center for Scientific Research (CNRS) affiliated with LIRIS laboratory at Claude Bernard University Lyon 1. He leads the Origami research team and holds several leadership positions including Director of GdR Informatique Géométrique et Graphique and Co-lead of PEPR ICCARE. As co-founder of the DGtal library and General Chair of the Graphics Replicability Stamp Initiative, he significantly influences computational geometry research. His research spans digital geometry, discrete algorithms, Monte Carlo rendering, and geometry processing. Core innovations include work on optimal transport, low-discrepancy sampling, digital surface regularization, and curvature estimation methods. His approaches combine theoretical mathematics with practical implementations for computer graphics and scientific computing applications. Recent publications demonstrate strong focus on: Efficient transport algorithms (BSP-OT, Rectified Flows) Sampling theory innovations (Sobol' sequences, Owen scrambling) Digital geometry foundations (Gauss digitization, Laplace-Beltrami operators) Geometric transformations (bijective rotations, plane probing) Awards include: 🎫 Best Paper Award, SIGGRAPH Asia 2024 🎫 SGP Software Award 2016 He leads multiple ANR grants including SSLAM (point cloud ML), StableProxies (geometry processing), and MoCaMed (medical physics). His lab develops open-source tools like DGtal and maintains active collaborations through international initiatives like the Graphics Replicability Stamp.