Find Academic Supervisors

• Partial Differential Equations
• Calculus of Variations and Optimal Control
• Geometric Analysis
• +5 more

Jose Salvador Moll Cebolla is an Associate Professor in the Department of Mathematical Analysis at the Faculty of Mathematics, University of Valencia. He is actively engaged in research within the Geometric Analysis Group U.V. (GAGUV) and the Nonlinear Partial Differential Equations (EDPNOL) research group. PhD, University of Valencia, 2005 His research focuses on nonlinear partial differential equations, calculus of variations, and geometric analysis. Key areas include total variation methods, 1-harmonic flows, flux-saturated diffusion, and phase-field models for materials science. His work bridges pure analysis with applications in image processing and material modeling. The recent publications highlight a strong trend in nonlinear diffusion equations, particularly those with singular or flux-limited behavior, as well as variational methods in imaging and geometric evolution equations. His work frequently involves existence, regularity, and qualitative behavior of solutions to highly nonlinear PDEs. No scientific awards are mentioned in the provided texts. He has collaborated extensively with researchers such as Lorenzo Giacomelli, José M. Mazón, Vicent Caselles, Fuensanta Andreu, Ken Shirakawa, and Francesco Petitta. There is no mention of grants or student advising in the available information. He is a member of the research groups GAGUV (Geometric Analysis Group U.V.) and EDPNOL (Nonlinear Partial Differential Equations), which focus on geometric PDEs and nonlinear evolution equations, respectively.

Bastian GoldlückeView profile

Associate Professor

• Computer Vision
• Variational Methods
• Light Field Analysis
• +3 more

Bastian Goldlücke serves as an Associate Professor in the Department of Computer Science at the University of Konstanz since 2014. His academic journey includes postdoctoral positions at the University of Heidelberg (2011), TU Munich (2010), and the University of Bonn (2009), following a Ph.D. from the Max Planck Institute for Computer Science in Saarbrücken (2006). His research centers on variational methods and their applications in computer vision, with specialized focus on light field analysis and inverse problems . Key contributions include novel frameworks for light field decomposition, real-time 3D reconstruction, and attention-based neural representations. His work bridges mathematical optimization with practical computer vision challenges in autonomous systems and 3D imaging. Notable scientific recognition includes: ERC Starting Grant 'Light Field Imaging and Analysis' (2013) Funding through SFB Transregio 'Quantitative Methods for Visual Computing' Prof. Goldlücke actively contributes to the academic community as a reviewer for major conferences and journals, co-organizer of the 'Light Fields for Computer Vision' workshops at ECCV 2014 and CVPR 2017, and area chair for ACCV 2014 and 3DV 2016. His current research is supported by significant European and German research grants focusing on computational imaging and visual analysis.

Julien FageotView profile

Researcher

• Sparsity
• Random Processes
• Approximation Theory
• +7 more

Julien Fageot is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in the AudioVisual Communications Laboratory within the School of Computer and Communication Sciences. He previously held postdoctoral positions at Harvard University, McGill University, and EPFL. His educational background includes: Ph.D. in Electrical Engineering at EPFL (2012-2017) M.Sc. Mathematics, Vision, and Learning in ENS Paris-Saclay, France (2011) M.Sc. in Probability and Statistics at Université Paris Orsay, France (2009) École Normale Supérieure, Section Mathématiques, Paris, France (2007-2012) Dr. Fageot's research lies at the intersection of high-level mathematics and data sciences, focusing on mathematical properties of advanced processing tools for sparse signal reconstruction and synthesis. His expertise spans sparsity, random processes, approximation theory, splines, convex optimization, functional analysis, and signal/image processing. He explores probability theory (sparse stochastic processes), optimization theory (sparsity-promoting spline reconstruction), and applications in signal processing (inverse problems, segmentation, detection, CNNs). His publication record demonstrates a clear progression from theoretical foundations of stochastic processes to practical applications in biomedical imaging. Recent work shows increasing focus on machine learning applications while maintaining strong mathematical rigor, particularly in developing sparse representations for medical image analysis. His scientific achievements have been recognized with: Best Paper Award at the MIDL Conference (2019) EPFL Best Doctorate Award (2018) Outstanding PhD Thesis Distinction in Electrical Engineering, EPFL (2017) Education Award from the Life Science Department, EPFL (2013) Dr. Fageot actively mentors students, currently supervising PhD candidate Adrian Jarret and having previously guided Thomas Debarre and Shayan Aziznejad to completion. He has supervised numerous master's theses on spline-based reconstruction and biomedical image analysis. His research is supported by Swiss National Science Foundation grants including the Postdoc.Mobility fellowship for 'Mathematical Models for Analog Data Sciences: the Continuous Way' (2020) and the Early Postdoc.Mobility fellowship for 'Probabilistic and Variational Methods for Sparse Signals' (2018). As a key member of EPFL's AudioVisual Communications Laboratory, he collaborates with Prof. Martin Vetterli, Prof. Michael Unser, and Prof. Christian Genest, bridging theoretical mathematics with practical applications in signal processing and data science.

George BaravdishView profile

Associate Professor

• Inverse Problems
• Machine Learning
• Medical Imaging
• +6 more

George Baravdish is a Senior Associate Professor and Head of Unit at the Department of Science and Technology (ITN), Linköping University. He leads the Physics, Electronics and Mathematics (FEM) unit and holds a PhD in Applied Mathematics from Linköping University (1995), focusing on Ill-posed and Inverse Problems under Prof. V. G. Maz'ya. His research spans Inverse Problems , Machine Learning , and Mathematical Oncology , with applications in medical imaging, signal processing, and telecommunications. Notable projects include Mathematics for Machine Learning (focusing on neural ODEs) and Mathematical Oncology (modeling brain tumor growth). Baravdish has supervised two doctoral students and co-supervised two more. He has secured funding as a main or co-applicant for 15 research projects. His work bridges theoretical mathematics and practical applications in healthcare and technology. Current research themes include: Image Processing : Generalizations of the p-Laplace operator for noise reduction Telecommunications : 3D object reconstruction using Wi-Fi signals Inverse Problems : Theoretical advancements and algorithm development He contributes to interdisciplinary collaborations, including AI-driven solutions for healthcare and signal analysis.

• Analysis of PDE
• Numerical Analysis of PDE
• Geometric Analysis
• +3 more

Dr. Heiko Kroener is a Researcher at the University of Duisburg-Essen , affiliated with the Faculty of Mathematics and the Department of Mathematics . Since 2019, he has been conducting postdoctoral research in Professor Paola Pozzi's DFG-funded project, Flow of elastic networks . His research focuses on Analysis of PDE , Numerical Analysis of PDE , and Geometric Analysis , particularly in curvature flows, finite element methods, and geometric evolution equations. He earned his PhD in Mathematics from Heidelberg University in 2012. Prior to his current position, he held postdoctoral roles at the University of Tübingen, WIAS Berlin, the University of Hamburg, and the University of Freiburg. His work has been recognized with awards such as the First Prize in the Bundeswettbewerb Mathematik (high school mathematics competition), a scholarship from the German National Academic Foundation (2005–2007), and a Postdoctoral Fellowship at WIAS Berlin (2014). His research explores topics like anisotropic curvature flows, inverse mean curvature flow, and numerical methods for PDEs on surfaces. Key contributions include studies on error estimates for finite element approximations and gradient estimates for curvature flows in curved manifolds. Key Research Themes : Curvature flows, geometric PDEs, numerical analysis, and surface PDEs. Selected Grants : Participates in a DFG project on elastic network flows. Professional Activities : Invited speaker at workshops on curvature energies and interfaces (2016–2017).

• Optimization
• Convex Optimization
• Nonconvex Optimization
• +7 more

Wotao Yin is a Professor of Mathematics at the University of California, Los Angeles, with a distinguished research career spanning over two decades in optimization theory and its applications. His work bridges theoretical mathematics with practical applications in machine learning, image processing, and signal analysis. As a leading researcher in optimization algorithms, he has made significant contributions to the development of methods like ADMM (Alternating Direction Method of Multipliers), proximal algorithms, and decentralized optimization techniques. Department: Department of Mathematics School: College of Letters and Science University: University of California, Los Angeles Yin's research focuses on developing efficient algorithms for large-scale optimization problems, with particular expertise in convex and nonconvex optimization, distributed and decentralized optimization, and mathematical foundations of machine learning. His work has profound implications for image reconstruction, signal processing, and modern machine learning systems. He has pioneered methods for handling sparse data, non-smooth objectives, and constrained optimization problems that arise in real-world applications. An analysis of his recent publications reveals a strong trend toward addressing optimization challenges in machine learning, particularly in federated learning, attention mechanisms, and nonconvex problem structures. His work demonstrates a consistent pattern of bridging theoretical optimization with practical machine learning applications, developing algorithms that balance computational efficiency with theoretical guarantees. Recent papers show increasing focus on heterogeneous data settings, large language model optimization, and fundamental limitations of optimization methods in complex learning scenarios. Throughout his career, Professor Yin has mentored numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. His collaborative network spans multiple institutions worldwide, with particularly strong connections to researchers in China and across the United States. His work has been supported by various funding agencies recognizing the fundamental importance of optimization theory for advancing computational science. Professor Yin leads a vibrant research group focused on mathematical optimization and its applications, where students and collaborators work on cutting-edge problems at the intersection of mathematics, computer science, and engineering. The group maintains strong connections with both theoretical and applied research communities, participating in major conferences across optimization, machine learning, and computational mathematics.

• Image Processing
• Computer Science
• Mathematics
• +7 more

Carole Le Guyader is an Associate Professor at the Department of Mathematical and Software Engineering at the National Institute of Applied Sciences of Rouen (INSA Rouen). Her research focuses on hybrid variational/deep learning methods under topological and geometric constraints, with applications in biomedical imaging, geophysics, and high-performance computing (HPC). She supervises Ph.D. students and collaborates with institutions such as UCLA , Cambridge University , and University of Pau . Teaching: Mathematical Engineering (PDEs, CAGD in M1; Variational Methods in Image Processing in M2), L1/L2 STPI courses. Administrative Roles: LMI correspondent for GDR MIA (since 2021), Head of Mathematics Teaching at STPI INSA Rouen (since 2019), Deputy Director of GM Department (2018-2020). Her recent work emphasizes integrating deep learning with variational methods for tasks like semantic segmentation in medical imaging and nonlinear elasticity models for image registration. She has contributed to parallel computing implementations and industrial partnerships with Siemens-Gamesa and Dassault Systèmes .

• Numerical Probability
• Stochastic PDEs
• Multiscale Modeling
• +2 more

Charles-Edouard Bréhier is a Professor at the University of Pau and the Pays de l'Adour, specializing in numerical methods for stochastic systems. His research spans stochastic PDEs, multiscale modeling, metastable processes, and geometric numerical integration. He leads investigations into error analysis and computational efficiency for complex stochastic systems. Bréhier's work develops advanced numerical schemes for high-dimensional problems, including splitting integrators, Galerkin approximations, and structure-preserving algorithms. His recent publications focus on error bounds for SPDE discretizations, turbulence modeling, and stochastic geometric mechanics. Articles consistently demonstrate rigorous mathematical foundations for computational methods, with applications spanning fluid dynamics, plasma physics, and optimization. The research provides tools for simulating complex systems with inherent randomness across multiple temporal and spatial scales.

• Calculus of Variations
• Parallel Computing
• Image Processing
• +3 more

William K. Allard is a Professor in the Department of Mathematics at Duke University. He holds a Sc.B. from Villanova University and a Ph.D. from Brown University. His research focuses on Calculus of Variations, Parallel Computing, Image Processing, and Scientific Computing, with notable projects including the KDI initiative and studies on spiral wave dynamics in cardiac systems. He has taught a wide range of courses, including Basic Analysis, Mathematical Logic, and Scientific Computing. His recent work includes contributions to differential geometry, image denoising via total variation regularization, and adaptive mesh refinement algorithms. Personal links highlight his family and residence information, though these are non-academic. Research highlights include geometric analysis, computational methods for image processing, and parallel computing applications. His teaching spans foundational mathematics to specialized topics like elliptic PDEs and motion by mean curvature. Collaborative work with John Trangenstein on distributed computational codes demonstrates interdisciplinary engagement. Despite the page's outdated timestamp (last modified 2003), his academic contributions remain well-documented in these materials.

• Inverse Problems
• Tomography
• Medical Imaging
• +2 more

Alexander Meaney is a Postdoctoral Researcher in the Department of Mathematics and Statistics at the University of Helsinki, Finland. His research centers on inverse problems with applications spanning medical imaging, tomography, and interdisciplinary biological studies. His core research interests include inverse problems, tomography, and computational mathematics, with specific focus on CT reconstruction algorithms, radiation dose reduction, and dynamic imaging techniques. Meaney develops mathematical models for multi-spectral and low-dose CT systems while addressing practical challenges in mammography quality control and plant development imaging. His methodological work emphasizes directional total variation regularization and synergistic reconstruction approaches. Analysis of Meaney's publications reveals a consistent trajectory in advancing tomographic reconstruction methods, particularly through challenge-based frameworks like the Helsinki Tomography Challenge. His work bridges theoretical mathematics with clinical and biological applications, showing increasing emphasis on multi-energy systems and real-world implementation. Key trends include the integration of machine learning in reconstruction pipelines and cross-disciplinary dataset development for benchmarking. Meaney actively participates in Academy of Finland-funded projects, notably the Centre of Excellence of Inverse Modelling and Imaging (2023-2025) and its predecessor (2018-2025), where he collaborates on grant-supported research in inverse modeling. These projects involve substantial computational resources and international partnerships. His collaborative network includes the Inverse Problems group at Helsinki, with documented academic visits to institutions like CWI Amsterdam. Meaney contributes to open science through publicly available datasets on Zenodo for dynamic X-ray tomography, supporting reproducibility in imaging research.

• Machine Learning
• Computer Vision
• Image Processing
• +2 more

Dr. Stephen Tierney is a Senior Lecturer in Business Analytics at the Sydney Business School, University of Sydney . His research focuses on machine learning , computer vision , image processing , and recommendation systems , with contributions to subspace clustering, data visualization, and image restoration. He advises two current PhD students: Pengqing SHI (on scalable graph neural networks) and Widhiyo SUDIYONO (on stock market prediction via deep learning). Recent work includes policy-relevant studies on NSW teachers' pay trends and labor market skill dynamics. His publications span top-tier journals like Signal Processing and conferences such as IEEE CVPR, covering topics ranging from efficient subspace clustering algorithms to image fusion techniques in remote sensing. Key contributions include robust functional manifold clustering (2021) and low-rank sequential subspace clustering (2015). Interdisciplinary projects merge business analytics with technical domains like computer vision and signal processing.

Daniel WachsmuthView profile

Professor

• Optimal Control Theory
• Partial Differential Equations
• Nonsmooth Optimization
• +4 more

Professor Daniel Wachsmuth holds the Chair of Mathematics VII (Optimal Control) at the University of Würzburg, where he has been a professor since 2012. He is affiliated with the Faculty of Mathematics and Computer Science and maintains his office in Building 30 (Mathematics West), Room 02.011. Prior to his current position, he completed his academic training with positions as a postdoc at RICAM in Linz, Austria (2008-2012) and as a research assistant at TU Berlin (2002-2008). Wachsmuth's research focuses on optimal control theory , particularly concerning partial differential equations, nonsmooth optimization problems, and regularization of problems with bang-bang control. His work bridges theoretical mathematical analysis with practical numerical methods for solving complex control problems. He has made significant contributions to the understanding of sparse optimization, topological derivatives, and non-convex optimization problems in function spaces. His recent publications demonstrate a strong trend toward addressing L 0 constraints in optimal control, developing advanced numerical methods for non-smooth problems, and exploring connections between optimal control theory and deep neural networks. His work spans both theoretical developments (such as second-order conditions and stability analysis) and practical algorithmic implementations for solving challenging optimization problems. Among his notable recognitions is the Dimitrie-Pompeiu Preis awarded in 2016. His research has been published in top-tier journals including SIAM Journal on Control and Optimization, Computational Optimization and Applications, and Inverse Problems. Current Position: Professor of Mathematics at University of Würzburg (since 2012) Previous Positions: Postdoc at RICAM, Linz (2008-2012); Research Assistant at TU Berlin (2002-2008) Contact: daniel.wachsmuth@uni-wuerzburg.de; +49 931 31-89071

Jiying ZhaoView profile

Professor

• Image Processing
• Video Processing
• Multimedia Communications
• +3 more

Jiying Zhao is a Professor at the School of Electrical Engineering and Computer Science (EECS) at the University of Ottawa. He holds a Ph.D. in Engineering from Keio University (Japan) and multiple engineering degrees from North China Electric Power University (China). His academic career includes roles as an instructor and associate professor at Thompson Rivers University (Canada) before joining the University of Ottawa in 2001, where he advanced to full professor in 2009. Dr. Zhao's research focuses on image/video processing , multimedia communications , and digital watermarking . Notable contributions include advancements in video super-resolution techniques, perceptual quality assessment of stereoscopic images, and robust watermarking algorithms resistant to geometric attacks and compression. His professional affiliations include IEEE, IEICE, and membership in Professional Engineers Ontario. While no specific awards or grants are listed, his extensive publication record demonstrates significant contributions to multimedia and signal processing fields. Advising and student mentorship details are not provided in the available text. Research interests are centered around 3D imaging , haptic communication , and content security , with applications in telepresence systems and video authentication. He maintains an active research website linked to the School of EECS.

Youssef DiouaneView profile

Associate Professor

• Numerical Optimization
• Surrogate Modeling
• Data Science
• +5 more

Youssef Diouane is an Associate Professor in the Department of Mathematical and Industrial Engineering at Polytechnique Montréal, Canada. Previously, he was a professor in the Department of Complex Systems and Engineering (DISC) at ISAE-SUPAERO in Toulouse, France. He is a member of the Research Group in Decision Analysis (GERAD) and serves as an associate editor for the journal "Computational Optimization and Applications" as well as a guest co-editor for a special issue of "Mathematical Programming" related to the 25th International Symposium on Mathematical Programming. Dr. Diouane's research focuses on numerical optimization and its applications to complex systems, design, and data sciences. His work targets the development of efficient optimization algorithms with optimal guarantees to solve engineering optimization problems. His primary research areas include: Numerical Optimization Surrogate Modeling Data Science and Machine Learning Computational Science and Engineering His recent publication record (57 total publications) demonstrates a strong focus on optimization techniques applicable to aerospace engineering, particularly aircraft design. There's a clear trend toward addressing high-dimensional optimization problems with mixed and categorical variables, often using Bayesian optimization approaches. His work bridges theoretical optimization methods with practical engineering applications, especially in sustainable transport and green aircraft design, reflecting his secondary spheres of excellence in Sustainable Transport and Infrastructures and Industry of the Future and Digital Society. Dr. Diouane has successfully supervised multiple graduate students, including two Master's theses at Polytechnique Montréal completed in 2022 and 2024. His teaching responsibilities include courses on scientific computing for engineers, operational research foundations, and derivative-free optimization, reflecting his commitment to both theoretical and applied aspects of optimization.

Yury KorolevView profile

Lecturer

• Applied Analysis
• Inverse Problems
• Mathematical Imaging
• +3 more

Yury Korolev is a Lecturer in the Department of Mathematical Sciences at the University of Bath. His research focuses on applied analysis, inverse problems, variational methods, mathematical imaging, and machine learning. He actively organizes workshops, including the Second Workshop on Machine Learning in Infinite Dimensions at ETH Zurich (2025), sponsored by ETH Zurich and the EPSRC Hub ProbAI. His work emphasizes infinite-dimensionality in machine learning, operator learning, and neural networks for partial differential equations (PDEs). He leads multiple funded projects from institutions like the Royal Society and EPSRC, addressing operator learning in inverse problems and regularization theory. Korolev is open to supervising PhD students in machine learning theory, inverse problems, and imaging. Key projects include 'Operator Learning in Inverse Problems' (Royal Society, 2024–2026) and 'Regularisation Theory in the Data Driven Setting' (EPSRC, 2022–2024). His research integrates mathematical rigor with applications in imaging, computational methods, and theoretical foundations of AI. Collaborations span academia and industry, emphasizing interdisciplinary problem-solving.