Find Academic Supervisors

• Finite Element Methods
• Numerical Analysis
• Linear Elasticity
• +11 more

Carsten Carstensen is a Professor at the Humboldt University of Berlin, affiliated with the Faculty of Mathematics and Natural Sciences and the Institute of Mathematics. His research focuses on numerical analysis, finite element methods, computational mechanics, and adaptive algorithms. He has contributed significantly to the development and theoretical analysis of numerical techniques for solving complex mathematical models in solid and fluid mechanics, including studies on linear elasticity, Stokes equations, and non-Newtonian fluids. His work emphasizes error estimation, method robustness, and optimal convergence rates. His research interests include the application of advanced finite element methodologies such as hybrid high-order (HHO) methods, virtual elements, and mixed formulations. He also explores eigenvalue problems, nonlinear partial differential equations, and plasticity models, with a particular attention to the interplay between mathematical theory and computational implementation. Recent studies highlight his efforts to unify error analysis frameworks and improve adaptivity in numerical simulations. While no scientific awards are explicitly listed, his extensive publication record reflects a deep engagement with foundational and applied aspects of computational mathematics. His advising and grants activities are not detailed in the text, but his research spans collaborations on topics such as microstructure modeling and numerical algorithms for elastoplasticity. He is based at the Institute of Mathematics, contributing to its research on differential equations and computational engineering.

Gabriela ApreuteseiView profile

Lecturer

• Convex Analysis
• Variational Calculus
• Functional Analysis
• +1 more

Roles & Affiliations: Gabriela Apreutesei is a Lecturer (PhD) at the Department of Mathematics, Faculty of Mathematics, Alexandru Ioan Cuza University of Iași, Romania. She specializes in Convex Analysis, Variational Calculus, Functional Analysis, and Topology. Her work focuses on set-valued analysis, hypertopologies, and nonlinear functional spaces. Education: PhD in Mathematics (2000) from Alexandru Ioan Cuza University of Iași, with a thesis on topological structures in linear spaces. She holds a Master's and Bachelor's degree from the same institution. Research Interests: Her research spans convex analysis, variational calculus, and topology, particularly in hypertopologies, set-convergence, and fuzzy multimeasures. She explores semi-linear spaces and their applications in optimization and control theory. Recent work includes studies on C-metric spaces, Cauchy conditions in topological frameworks, and continuous dependence in difference equations. Key Contributions: Notable contributions include developing topological properties for non-linear spaces and proving results on continuous dependence in second-order systems. Her work on Darboux properties of fuzzy multimeasures earned a Best Paper Award at the 2009 WSEAS Conference. Awards & Grants: Recipient of the 2005 Act of Honour for contributions to the American Romanian Academy Congress. She led or participated in multiple research grants, including CNCSIS-funded projects on set-valued analysis and variational methods. Recent grants include WSEAS-funded projects on fuzzy mathematics and set-valued analysis. Teaching & Service: Teaches complex analysis and related courses. Served as a reviewer for An. St. Univ. 'Al. I. Cuza' Iasi and Mathematical Reviews . Actively participates in seminars on measure theory and optimization, presenting on topics like hypertopologies and bounded conditions.

• Partial Differential Equations
• Nonlinear Analysis
• Elliptic Equations
• +4 more

Marta Latorre Balado is a mathematical researcher affiliated with the Universitat de València, specifically within the Department of Mathematical Analysis. She is associated with the research group EDPNOL (Nonlinear Partial Differential Equations), focusing on advanced topics in PDE theory. She earned her PhD in 2018 with a dissertation on elliptic equations involving the 1-Laplacian operator, supervised by Dr. Sergio Segura de León. Her research lies at the intersection of nonlinear analysis and partial differential equations, particularly those with singularities, gradient dependencies, and variational structures. Her publications reveal a strong focus on existence, uniqueness, and regularity of solutions for equations involving the 1-Laplacian and related operators. Key themes include singular terms, L^1 and Marcinkiewicz data, dynamical boundary conditions, and blow-up behavior in reaction-diffusion systems. Her work contributes to the theoretical foundations of nonlinear PDEs with applications in mathematical physics and image processing. Notable trends in her research include the use of BV (bounded variation) spaces, comparison principles, and self-similar solutions. She frequently collaborates with experts such as Sergio Segura de León, Razvan Iagar, and Ariel Sánchez. Marta has not been awarded any listed scientific prizes in the provided data and has no known advisees. She has co-authored 11 publications since 2017, primarily in reputable journals like Journal of Mathematical Analysis and Applications and Nonlinearity . She is part of an active research team in nonlinear PDEs at her institution, contributing to both theoretical developments and collaborative projects in mathematical analysis.

Jose Julian Toledo MeleroView profile

Professor

• Partial Differential Equations
• Nonlocal Diffusion
• Calculus of Variations
• +7 more

Jose Julian Toledo Melero is a Professor in the Department of Mathematical Analysis at the Faculty of Mathematics, Universitat de València. He is a leading researcher in nonlinear partial differential equations, with a focus on nonlocal diffusion, evolution equations, and optimal transport in metric random walk spaces. His work bridges pure and applied analysis, with strong collaborations with mathematicians such as José M. Mazón and Julio D. Rossi. His research interests include: Nonlinear Partial Differential Equations Nonlocal Diffusion and Operators Calculus of Variations and Optimal Transport Evolution Problems in Metric Spaces Geometric Analysis and Minimal Surfaces Doubly Nonlinear and Degenerate Equations The recent articles show a consistent focus on analytical and geometric properties of nonlocal models, particularly in discrete and metric random walk settings. His work frequently appears in top journals such as Calculus of Variations , SIAM Journal on Mathematical Analysis , and Journal of Evolution Equations , often in collaboration with leading experts. The publications reveal a deep engagement with both theoretical foundations and structural analysis of solutions. He has co-authored influential books such as Nonlocal Diffusion Problems (AMS, 2010) and Variational and Diffusion Problems in Random Walk Spaces (Birkhäuser, 2023), which are key references in the field. His research is highly cited, indicating significant impact in the mathematical community. He advises no publicly listed students in the provided data. There is no mention of specific grants or teaching roles, but his extensive publication record and book authorship reflect a sustained and high-level research career. He maintains an active research profile with upcoming events such as the 2025 CIMPA-UCA School on optimal transport and PDEs, suggesting ongoing leadership and contribution to the international mathematical community.

Jan LellmannView profile

Professor

• Variational Image Processing
• Optimization on Manifolds
• Image Registration
• +7 more

Jan Lellmann is a Professor at the Institute of Mathematics and Image Computing of the University of Lübeck, with affiliations to Fraunhofer MEVIS . His research focuses on variational image processing , emphasizing the systematic formulation of prior knowledge into energy functions for improved accuracy and data efficiency. Applications span medical imaging , earth sciences , and biological data analysis . He develops non-smooth optimization methods for problems with combinatorial aspects like image segmentation . His recent work includes manifold-constrained optimization and quantum algorithms for imaging tasks. He has contributed software libraries like MFOPT (for manifold optimization) and COAL (for convex energy minimization). Scientific Awards: Best Student Paper Award at SSVM 2021 Honorable Mention at CVPR 2016 Recent Research Trends: Integration of quantum computing with classical image registration. Advancements in Riemannian geometry for protein dynamics and cryo-EM. Development of meta-learning frameworks for adaptive image alignment. Focus on non-smooth and higher-order regularization for sparse data reconstruction.

• Signal Processing
• Biomedical Engineering
• Image Processing
• +3 more

Prof. Dr. Mustafa Ersel Kamaşak is a Professor at the Department of Computer Engineering , Istanbul Technical University . His academic journey includes a PhD in Electrical and Computer Engineering from Purdue University and a MA in Electrical and Electronic Engineering from Bogazici University . His research interests span Signal Processing , Biomedical Engineering , Image Processing , Machine Learning , and Total Variation Regularization , with a focus on applications in Medical Imaging , Geospatial Analysis , and Logistics Optimization . His recent work includes 2024 studies on barcode detection and mandibular morphology analysis , 2023 research on Antarctic sea level monitoring , and 2022 projects on fluorescence microscopy restoration . Notable projects led by him include Automatic Detection and Transfer of Document Types (TTO, 2021), Telehealth Equipment Development (TTO, 2019-2021), and Directional Total Variation Model (TUBITAK, 2016-2017). His work has been cited 18 times (Scopus h-index: 22).

Yiqiu DongView profile

Associate Professor

• Mathematical Imaging
• Inverse Problems
• Variational Methods
• +4 more

Yiqiu Dong is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where they have been employed since 2010. Currently, they are working on the ERC-project "High-Definition Tomography" and teach courses including Optimization and Data Fitting, Computational Science in Imaging, and Discrete Inverse Problems. Dr. Dong received their Ph.D. in Computational Mathematics from Peking University in 2007 through a joint program with The Chinese University of Hong Kong. Their academic journey includes postdoctoral research at Karl-Franzens University of Graz in Austria and a scientific researcher position at Helmholtz Zentrum Muenchen in Germany. Dr. Dong's research focuses on mathematical imaging, inverse problems, and variational methods. Their work spans multiple areas including image restoration, tomographic reconstruction, and optimization methods for solving complex imaging problems. They have developed innovative approaches for handling various types of image noise including multiplicative noise, impulse noise, and Cauchy noise. Analysis of Dr. Dong's publication record reveals a strong focus on image restoration techniques using variational methods and total variation regularization. Their work demonstrates progression from basic noise removal techniques to more sophisticated multi-scale and vectorial approaches for color image processing. Recent publications show increasing focus on tomographic reconstruction and medical imaging applications. Dr. Dong has collaborated extensively with researchers across Europe and Asia, including institutions such as Helmholtz Zentrum Muenchen, Karl-Franzens University of Graz, The Chinese University of Hong Kong, and Peking University. Their work on the ERC-project "High-Definition Tomography" represents a significant research initiative in their field. Teaching responsibilities include advanced courses in optimization, computational imaging, and inverse problems, reflecting their expertise in mathematical methods for image processing and analysis.

Wolfgang RingView profile

Professor

• Inverse Problems
• Partial Differential Equations
• Shape Optimization
• +3 more

Wolfgang Ring is a professor at the University of Graz , affiliated with the Institute of Mathematics . His work bridges applied mathematics and engineering disciplines. Research Interests Specializing in inverse problems , his research spans: Partial differential equations and variational inequalities Shape and structural optimization Nonsmooth optimization methods Ill-posed Volterra equations Image processing and analysis His mathematical techniques address challenges in electromagnet design , mechanical systems , and medical imaging applications. Academic Contributions Key publication trends include: Development of regularization methods for ill-posed problems Advancements in predictor-corrector algorithms for Volterra equations Innovations in shape optimization using finite element techniques Structural analysis of total variation regularization solutions Education Dr. techn. in Mathematics from Technical University Graz (1993) Teaching Has taught graduate-level courses on: Ordinary differential equations Complex analysis Heat and mass transfer

• Multi-Dimensional Signal Processing
• System Modelling and Identification
• Image and Video Processing
• +5 more

Mårten Sjöström is a Professor in Signal Processing at Mid Sweden University, where he serves as the highest representative of the research subject Computer and System Sciences and is part of the managerial group of the Department of Information and Communication Systems (IKS). He leads the Realistic 3D research group and has extensive experience in both academic and industrial settings. His educational background includes a Master of Science from Linköping University (Applied Physics and Electrical Engineering, 1992), a Technical Licentiate degree from the Royal Institute of Technology, Stockholm (Signal Processing, 1998), and a PhD from Ecole Polytechnique Federale de Lausanne (Modelling of Non-linear Systems, 2001). He obtained his Docent degree (Associate Professor) in 2008 and Professor's degree in Signal Processing in 2013. His primary research focuses on Multi-Dimensional Signal Processing with emphasis on System Modelling and Identification. He has successfully applied these techniques to Image and Video Processing, Multi-media Communications, and currently specializes in Multi-Scopic 3D and Light Field Technology including capture, processing, coding, and presentation/visualization. His work spans theoretical foundations to practical implementations across various application domains. His recent publication record demonstrates a clear trajectory toward advanced light field and 3D imaging technologies, with significant contributions to compression algorithms, depth estimation techniques, quality assessment metrics, and telepresence applications. His research bridges theoretical signal processing with practical industrial implementations, particularly in remote operation, mining applications, and immersive visualization systems. Best Paper Award at MMEDIA 2013 Quality Reviewer Award at ICME 2013 Professor Sjöström has supervised an extensive number of doctoral and licentiate students, with numerous current PhD candidates expected to complete their degrees in 2025. His teaching portfolio covers a wide range of subjects including Applied Signal Processing, Automatic Control, Computer Hardware and Architecture, and specialized PhD courses in Video Processing and Realistic 3D. He has led numerous research projects both current and completed, including IMMERSE, PLENOPTIMA, and various initiatives in 3D video technology and visualization. As founder and head of the Realistic 3D research group, he directs activities focused on synthesis and capture of 3D images and video, rendering techniques for virtual perspective views, system modeling for 3D capture and presentation, coding of 3D content, quality metrics and assessments, and remote control and measurement systems. The group maintains strong industrial collaborations across multiple sectors.

• Signal Processing
• Optimization
• Sensitivity Analysis
• +3 more

Hugo Raguet serves as an Associate Professor at INSA Centre-Val de Loire in Blois, France, with research affiliations at the Fundamental and Applied Computer Science Laboratory (jointly associated with University of Tours). His work bridges theoretical computer science and applied mathematics, focusing on algorithmic solutions for real-world computational challenges. His research expertise spans: Signal Processing: Developing efficient algorithms for real-time applications and medical imaging Optimization: Advancing convex and non-smooth optimization techniques with graph-based regularization Sensitivity Analysis: Pioneering targeted methods using dependence measures for domain-specific phenomena Image Processing: Extending raster-to-vector conversion to multicolor systems Publication analysis reveals consistent innovation in splitting methods (Douglas-Rachford, forward-backward) and preconditioning techniques, with strong emphasis on computational efficiency for graph-structured problems. His work frequently bridges mathematical theory with practical implementations in signal processing and machine learning. No scientific awards are documented in available sources. Student advising and grant information remain unspecified. Raguet actively contributes to the Fundamental and Applied Computer Science Laboratory, where he develops open-source tools for academic collaboration including live conferencing software and LaTeX templates for scientific communication.

• Inverse Problems
• Mathematical Imaging
• Computational Mathematics
• +3 more

Prof. Otmar Scherzer is a distinguished academic at the Faculty of Mathematics, Department of Mathematics, University of Vienna . His work spans Inverse Problems, Mathematical Imaging, Computational Mathematics, and Applied Mathematics , with a focus on tomography, neural networks, and medical imaging. Over nearly two decades, he has published extensively in journals like Inverse Problems , SIAM Review , and IEEE Transactions . 2025 : Advanced data-driven regularization and neural network applications in inverse problems. 2024 : Explored diffraction tomography, Hilbert space approaches to scattering, and motion detection algorithms. 2023 : Addressed uncertainty quantification, visco-acoustic imaging, and Doppler ultrasound interpolation. His recent research emphasizes uncertainty-aware blob detection, strain reconstruction in elastography , and Fourier-based tomography . Publications frequently integrate numerical experiments and computational inverse methods , reflecting a blend of theoretical and applied mathematics. Prof. Scherzer collaborates with institutions like RICAM (Johann Radon Institute) and contributes to hybrid imaging modalities such as quantitative optical coherence elastography and photoacoustic tomography . He has supervised projects in 3D shape reconstruction and non-convex regularization , though specific student names are not listed in the provided data.

Pavan AduriView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +4 more

Pavan Aduri is a Professor and Interim Department Chair at Iowa State University's Department of Computer Science. His research focuses on theoretical computer science, computational complexity, algorithms, and machine learning. Key interests include statistical similarity estimation, submodular optimization, and probabilistic inference. He has contributed significantly to topics like #P-completeness, data stream processing, and fairness in optimization. His work spans algorithm design, computational hardness analysis, and applications in data science. Notable publications address total variation distance estimation, forgetting mechanisms in data streams, and monotone k-submodular maximization. He has explored connections between model counting and distinct element estimation in streams. Aduri's recent work (2023-2025) emphasizes geometric rounding techniques, Sperner's lemma variants, and algorithmic approaches to probabilistic inference. His research often bridges foundational theory with practical algorithmic challenges in machine learning and big data analysis. No scientific awards are explicitly listed, but his extensive publication record reflects sustained academic contributions.

• Uncertainty Quantification
• Ensemble Kalman Methods
• Bayesian Inverse Problems
• +3 more

Xin T. Tong is Associate Professor in the Department of Mathematics at the National University of Singapore, specializing in uncertainty quantification, machine learning, and operations research. His research develops theoretical foundations and methodologies for structured problem-solving in high-dimensional settings. Current investigations focus on ensemble Kalman methods, Bayesian inverse problems, sampling algorithms, and stochastic optimization with non-i.i.d. data. Research emphasizes mathematical analysis of algorithm efficiency and structure-aware computational methods. Professional background includes postdoctoral work at NYU's Courant Institute and Ph.D. from Princeton University.

Valery VishnevskiyView profile

Lecturer

• Biomedical Imaging
• Magnetic Resonance Imaging (MRI)
• Ultrasound Imaging
• +3 more

Dr. Valery Vishnevskiy is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich , affiliated with the Biomedical Imaging Group . His research focuses on advanced medical imaging techniques, particularly in cardiac MRI , ultrasound , and machine learning applications . He contributes to projects involving 4D flow MRI reconstruction , speed-of-sound imaging , and motion-corrected diffusion tensor analysis . Primary Affiliation : ETH Zürich, Department of Information Technology and Electrical Engineering Research Interests : Biomedical Imaging, MRI Reconstruction, Ultrasound Physics, Cardiac Imaging, Deep Learning His recent work emphasizes self-supervised learning for medical imaging, including the development of FlowMRI-Net for accelerated 4D flow MRI and Speed-of-Sound Imaging using diverging waves. Publications span hyperpolarized 13C metabolic imaging , viscoelasticity reconstruction , and probabilistic sampling optimization . Dr. Vishnevskiy's contributions to deformable image registration include methods for handling sliding interfaces in abdominal and cardiac imaging, with applications in respiratory motion compensation and tissue ablation monitoring . He also explores mathematical models for enzyme activity analysis and behavioral pattern detection.

Fred ParkView profile

Assistant Professor

• Mathematical Image Processing
• Computer Vision
• Numerical Partial Differential Equations
• +3 more

Fred Park serves as Assistant Professor of Mathematics in the Department of Mathematics & Computer Science at Whittier College. His research bridges mathematical theory with practical applications in image processing and computer vision, maintaining active collaborations across computational disciplines. Education: Ph.D. in Mathematics, University of California, Los Angeles M.A. in Mathematics, University of California, Los Angeles Professor Park's research focuses on mathematical image processing with emphasis on total variation methods, blind deconvolution, and shape modeling. His work spans computer vision applications including 3D surface reconstruction, texture extraction, and medical image analysis. Current projects explore non-convex optimization for deep learning compression and scientific computing implementations for real-world problem solving. His teaching philosophy integrates real-world mathematical problems to inspire undergraduate learning. Recent publications demonstrate strong trends in image segmentation frameworks combining anisotropic-isotropic total variation, network slimming for CNN efficiency, and medical imaging applications such as COVID-19 lung scan clustering. His work consistently bridges theoretical mathematics with computational implementation. Professor Park actively seeks to incorporate research into undergraduate education, developing methodologies to motivate learning through practical applications. His teaching experience spans undergraduate lower and upper division courses at UCLA, UC Irvine, and University of Michigan during postdoctoral positions. Research facilities include computational resources at Whittier College's Science & Learning Center (SLC 218), supporting work in image processing, shape modeling, and 3D reconstruction from point cloud data.