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Fredrik Berntsson is an Associate Professor (Docent) in the Department of Mathematics at Linköping University, Sweden. He is affiliated with the Division of Applied Mathematics (TIMA) within the Faculty of Science and Engineering. His work bridges theoretical mathematics and practical applications in engineering and industrial processes. Research Interests: His primary research focuses on inverse and ill-posed problems, particularly in the context of partial differential equations such as the Helmholtz and heat equations. He investigates mathematical modeling and numerical analysis of physical systems, with applications in inverse heat conduction, image processing, biomechanics, and structural dynamics. His interest extends to the stability and accuracy of computational methods used in simulations. Publication Trends: From 2021 to 2025, his publications show a consistent focus on inverse problems, especially iterative methods like Dirichlet-Robin and Robin-Dirichlet procedures for solving Cauchy problems. He also explores industrial applications, such as thermal tracking in steel production and optimal actuator placement in pedestrian-bridge systems, demonstrating a blend of theoretical rigor and real-world relevance. Teaching: He teaches courses in scientific computing, linear algebra, and programming, contributing to both undergraduate and graduate education in mathematical sciences. Affiliations: Department of Mathematics (MAI), Linköping University Division of Applied Mathematics (TIMA) Faculty of Science and Engineering, Linköping University Scientific Awards: No awards listed in the provided text. Advising and Grants: No specific information on students or doctoral advisees is provided. No direct mention of grants or funded projects is available, though his research areas suggest involvement in computational and applied mathematics funding streams. Labs and Research Groups: Applied Mathematics (TIMA) division at MAI focuses on computational mathematics, optimization, and mathematical statistics, where Berntsson conducts his research. His work on steel production simulation involved collaboration with SSAB, indicating industrial research partnerships.

• Inverse Problems
• Ill-Posed Problems
• Numerical Analysis
• +9 more

Aleksandr Malyshev is Professor of Mathematics at the University of Bergen. His research integrates numerical linear algebra, stability theory, optimisation-based control, and image-processing algorithms, yielding a portfolio of more than 60 peer-reviewed articles and conference contributions. Education & affiliations: Professor, Department of Mathematics, University of Bergen, Norway (present) Previous research and teaching engagements in informatics and applied mathematics at the same university Research interests: Malyshev’s core interest is the theoretical and algorithmic analysis of matrix problems arising in stability, control and imaging. He develops numerically reliable tools for assessing the distance to instability of dynamical systems, constructs preconditioners that accelerate optimisation solvers in real-time model predictive control, and designs variational models for 3-D reconstruction and image denoising. His work frequently combines spectral theory of matrix polynomials with practical issues such as high-performance implementation and medical-image quantification. Across the last decade his articles reveal three dominant strands: (i) stability and perturbation of time-delay and periodic systems, (ii) preconditioned iterative solvers for interior-point and MPC formulations, and (iii) variational and learning-based approaches to depth estimation, surface reconstruction and glenoid-bone assessment. These themes are unified by a common mathematical substrate—exploitation of matrix structure to obtain computationally efficient, numerically trustworthy solutions. Scientific awards & recognition: Regular invited speaker at international workshops on numerical linear algebra and control (e.g., SK Godunov conference 2009, IFAC 2018) Funded principal investigator / co-investigator on Research Council of Norway and EU Horizon Europe grants Advising & grants: Malyshev has supervised numerous MSc and PhD candidates in numerical analysis and scientific computing and currently advises graduate researchers on projects ranging from 3-D machine-vision algorithms to Krylov-subspace preconditioning. Recent grant participation includes EU project 101373 (3-D quantification of glenoid bone loss) and the Norwegian Research Council project 262203 on perfusion-flow simulation. Labs & collaboration: He collaborates closely with the Group for Numerical Methods and Applications at UiB, the Visual Computing cluster at the Department of Informatics, and maintains international partnerships with the Universities of Brest, Lübeck, and several US institutions. These joint efforts feed cross-disciplinary projects combining rigorous matrix analysis with real-world applications in biomechanics, process control, and computer vision.

• Numerical linear algebra
• Matrix polynomials and spectral theory
• Stability and perturbation analysis
• +3 more

Olga Doronina serves as Researcher III in Data Science within the Computational Science group at the National Renewable Energy Laboratory (NREL), applying expertise in turbulence modeling, computational fluid dynamics, machine learning, and statistical methods to advance renewable energy research. Her academic foundation includes: PhD in Mechanical Engineering from the University of Colorado Master of Science in Mechanical Engineering from the University of Colorado Master of Science in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Bachelor of Science in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Her research centers on developing computational tools for energy applications, with current projects focused on surrogate modeling of non-ideal reacting flows in oxy-combustors and wind turbine blade representation. She integrates Bayesian analysis and machine learning with fluid dynamics to solve complex engineering challenges in aerodynamics and combustion systems. Analysis of her 2024-2025 publications reveals strong emphasis on multi-fidelity modeling techniques, uncertainty quantification in scale-up problems, and neural-network-enhanced control frameworks for building energy systems. Her work consistently bridges computational mathematics with practical renewable energy applications, particularly in aerodynamic shape optimization and combustion modeling. Dr. Doronina actively collaborates within NREL's Computational Science division on interdisciplinary projects involving wind energy systems, combustion technologies, and building efficiency solutions, contributing to the laboratory's mission through advanced computational methodologies.

• Computational Fluid Dynamics
• Turbulence Modeling
• Machine Learning
• +5 more

Benjamin Recht is a Professor at the California Institute of Technology , affiliated with the Center for the Mathematics of Information . His work spans Machine Learning , Control Systems , Reinforcement Learning , and Optimization , with a focus on theoretical guarantees, adaptive algorithms, and real-world applications. His research includes: Control Systems : Certainty equivalence, adaptive control, LQR, and robustness in dynamic environments. Machine Learning : Generalization bounds, interpolation in classifiers, test set overuse, and ethical frameworks for systemic harm detection. Neural Rendering : K-Planes for explicit radiance fields in space-time-appearance modeling. Recent publications (2025-2018) highlight trends in automating adaptive control , ethical machine learning , distributed computing , and 3D reconstruction . No student lists, awards, or lab details are explicitly mentioned.

• Machine Learning
• Control Systems
• Reinforcement Learning
• +3 more

Sajidah Ahmed is a plasma physicist affiliated with the University of York and actively contributes to nuclear fusion research. Her work focuses on turbulent phenomena in magnetically confined fusion plasmas, particularly the dynamics of scrape-off layer (SOL) fluctuations and their impact on plasma-wall interactions. Doctoral research (2023) on Intermittent fluctuations in the boundary of magnetically confined fusion plasmas . Published studies in leading fusion journals on topics such as filament propagation, MHD instabilities, and pulse superposition modeling. Research Highlights: Her research addresses critical challenges for commercial fusion reactors, including: Characterizing blob-like turbulent structures in SOL regions. Reconstructing intermittent time series using deconvolution methods. Investigating density limits and plasma confinement in devices like Alcator C-Mod and MAST Upgrade.

• Plasma Physics
• Nuclear Fusion
• Turbulence
• +2 more

Marta Lazzaretti is a Research Fellow at the Department of Mathematics (DIMA) of the University of Genoa. Her work focuses on inverse problems in imaging, numerical analysis, and optimization algorithms in non-standard functional spaces. Affiliation: Department of Mathematics, University of Genoa Academic Rank: Research Fellow Research Interests: Specializing in regularization techniques and numerical optimization, her research spans: Off-the-grid methods for Poisson inverse problems Banach space formulations for geophysical data inversion Stochastic gradient descent in variable exponent Lebesgue spaces Dual descent regularization algorithms Publication Trends: Recent work emphasizes non-Hilbertian optimization frameworks (2023-2025), combining stochastic methods with deterministic regularization for imaging and subsoil inversion applications. Collaborations include Claudio Estatico, Luca Calatroni, and Giuseppe Rodriguez.

• Numerical Analysis
• Inverse Problems in Imaging
• Optimization Algorithms in Banach Spaces
• +3 more

PD Dr. Florian Frank is Privatdozent (senior lecturer with full teaching licence) for Applied Mathematics at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and heads the Bavarian research project „Parallel mesh loading and partitioning for large-scale simulation“ . His expertise spans high-performance computing, phase-field and discontinuous Galerkin methods, digital-rock physics, and reactive transport in porous media. Education & career 2022 – Venia legendi (private lecturer), Mathematics, FAU 2019 – Dr. habil., Mathematics, FAU 2013 – Dr. rer. nat., Applied Mathematics, FAU 2008 – Graduate Mathematician, University of Frankfurt 2021-2022 (acting) W2 Professor Scientific Computing, FAU 2018-2021 (acting) W2 Professor Mathematical Modelling, FAU 2017-2018 Senior Postdoc, CAAM, Rice University, USA 2014-2017 Postdoc, CAAM, Rice University, USA Research interests Frank focuses on the development and analysis of numerical schemes for partial differential equations that govern multiphase, multicomponent and reactive processes in porous or biological media. Key themes include discontinuous Galerkin and finite-volume methods , physics-preserving discretizations , high-performance computing , and digital-rock-based pore-scale simulations . He couples phase-field approaches with (Navier–)Stokes, Cahn–Hilliard, Nernst–Planck and density-gradient equations to quantify flow, transport, colloid dynamics and interfacial phenomena. Recent publications reveal a clear trend toward data-driven modelling : convolutional neural networks are trained with direct numerical simulation data to predict permeability and diffusion coefficients from 3-D micro-CT images, while advanced preconditioners and regularization techniques accelerate multiphase thermodynamic computations. Awards & recognition 2020 – Emmy-Noether-Prize der Naturwissenschaftlichen Fakultät, FAU 2017 – Promotion to Senior Postdoctoral Research Associate , George R. Brown School of Engineering, Rice University Projects, tools & supervision Frank currently leads a Bavarian state-funded project on parallel mesh handling for large-scale simulations. Together with collaborators he maintains the open-source MATLAB/GNU Octave toolbox FESTUNG for discontinuous Galerkin methods. Since 2018 he has (co-)supervised ten BSc and MSc theses on topics ranging from Stokes preconditioning to enriched Galerkin shallow-water solvers, regularly serves as reviewer for more than a dozen international journals, and is guest editor of special issues in Computational Geosciences and Oil & Gas Science and Technology .

• Scientific Computing
• Numerical Analysis of PDEs
• High-Performance Computing
• +7 more

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.

• Image Analysis
• Image Processing
• Inverse Problems
• +3 more

Hans van Dommelen is Associate Professor of Micromechanics at Eindhoven University of Technology (TU/e), Department of Mechanical Engineering, where he leads the Group Van Dommelen . His research couples microstructure to mechanical and functional behaviour of materials spanning nuclear fusion, additive manufacturing, polymers, and biomechanics. Education PhD in Mechanical Engineering, TU/e (2003) – Micromechanics of particle-modified semicrystalline polymers Visiting researcher, MIT (1999–2000), University of Virginia (2003–2004), and Cambridge University (2010–2012) Research Interests Van Dommelen’s work focuses on multi-scale mechanics and structure–property relationships . Using microstructural modelling and homogenization techniques, he links phenomena at the microscale to macroscopic response in: Crystalline and heterogeneous materials Nuclear fusion reactor materials (tungsten, liquid-metal shields) Additive manufacturing (wire-arc, selective laser sintering, vat photopolymerization) Semi-crystalline polymers and short-fiber composites Traumatic brain injury biomechanics Scientific Output He has authored over 230 peer-reviewed publications (h-index > 40) in leading journals such as Journal of the Mechanics and Physics of Solids , Biomechanics and Modeling in Mechanobiology , Nuclear Fusion , and Additive Manufacturing . Recent trends include viscoelastic-viscoplastic metamaterials, anisotropic food printing, recrystallization kinetics of tungsten under fusion loads, and multiscale fracture of additively manufactured metals. Teaching & Supervision Van Dommelen coordinates and lectures in: Structure and Properties of Materials Computational and Experimental Micro-mechanics Fusion Reactor Materials and Plasma-Wall Interaction He has supervised >85 MSc and PhD theses to date. Laboratory & Collaborations He heads the Group Van Dommelen within the Mechanics of Materials section, maintaining strong collaborations with DIFFER, ITER, and international partners on liquid-metal technologies for fusion blankets and advanced additive manufacturing processes.

• Micromechanics
• Multi-scale mechanics
• Structure–property relations
• +7 more

Dimitrios Pappas is a faculty member at the Department of Statistics within the School of Information Sciences and Technology at Athens University of Economics and Business . His research spans multiple disciplines, including Statistics , Linear Algebra , Image Processing , and Financial Mathematics . His work focuses on generalized inverses , matrix factorization , and quadratic optimization , with applications in image deblurring , credit risk modeling , and smart local energy systems . Recent publications highlight his contributions to network intrusion detection , Markov chain lumpability , and symbolic computation methods . While no specific scientific awards or student advising details are provided in the available text, his extensive publication record demonstrates expertise in applied mathematical modeling and computational techniques . He can be contacted at dpappas@aueb.gr .

• Statistics
• Linear Algebra
• Image Processing
• +5 more

Brennan Klein is an Assistant Teaching Professor in the Department of Physics at Northeastern University, with dual affiliations at the Network Science Institute and the Institute for Experiential AI. He concurrently serves as Data for Justice Fellow at Harvard University's Institute on Policing, Incarceration, and Public Safety within the Hutchins Center for African and African American Research. As director of the Complexity & Society Lab (&-Lab), he leads interdisciplinary research bridging network science, complex systems theory, and social justice applications. Education: PhD in Network Science, Northeastern University (2020) BA in Cognitive Science & Psychology, Swarthmore College (2014) Dr. Klein's research program integrates two complementary strands: developing theoretical frameworks for characterizing dynamics, structure, and scale in complex networks, and applying these tools to analyze systemic inequalities in public health and criminal justice systems. His work combines information theory, Bayesian inference, and network science to uncover emergent mechanisms driving social phenomena while developing practical interventions for more equitable systems. Analysis of his recent publications reveals a strong trajectory connecting fundamental network theory with real-world societal challenges. His work spans sports analytics, pandemic response modeling, criminal justice reform, and consciousness studies, demonstrating both methodological innovation and practical impact across diverse domains. The publications consistently feature interdisciplinary collaboration and emphasize both theoretical advancement and social application. Scientific Recognition: Best paper award for 'Spin glass systems as collective active inference' (2023) Dr. Klein actively mentors PhD students and postdoctoral researchers through the Complexity & Society Lab, fostering collaborations across physics, data science, sociology, and public policy. His research has secured funding for projects addressing criminal justice reform, public health infrastructure, and sports analytics applications, demonstrating the practical relevance of his theoretical work. The Complexity & Society Lab operates at the intersection of theoretical network science and social impact, with current projects examining urban infrastructure networks, criminal justice disparities, sports performance analytics, and neural network structures. This integrative approach reflects Dr. Klein's commitment to advancing both scientific understanding and social progress through network science.

• Complex Systems
• Network Science
• Information Theory
• +9 more

Nurdal Watsuji serves as Assistant Professor in the Department of Electrical and Electronics Engineering at Gaziantep University's Faculty of Engineering, specializing in Electrical-Electronics Engineering with research emphases on Image Processing, Signal Processing, and Digital Design. His academic foundation includes: Doctorate in Electrical and Electronic Engineering from Imperial College London (1987-1991) Degree in Electrical-Electronic Engineering from Middle East Technical University, Gaziantep (1982-1985) Licence in Electrical Engineering from Middle East Technical University, Gaziantep (1975-1981) Watsuji's research integrates theoretical signal processing with practical engineering applications, particularly in LED/OLED lighting systems and image analysis. His work demonstrates consistent innovation in optical efficiency optimization, compressed sensing algorithms, and wavelet-based texture classification, bridging electrical engineering principles with real-world implementation challenges in energy systems and computer vision. Publication analysis from 2003-2016 reveals evolving expertise from foundational wavelet techniques to advanced optical efficiency modeling in solid-state lighting. The recurring theme involves mathematical optimization of signal/image processing methods for specific engineering constraints, with significant contributions to nonconvex compressed sensing and LED reflector design. His graduate mentoring spans eight students: PhD candidates: İpek Atık (2016) on LED reflector optimization and Taner Ince (2012) on compressed sensing Master's students: Ugur Polat (2022) on energy analyzers, Gulden Akay Ozbay (2008) on hand recognition, Taner Ince (2006) on medical image processing, Recep Oz (2004) on 3D rendering, Omer Cengiz Celebi (2003) on volume rendering, and Bayram Kara (2003) on wavelet texture analysis This diverse portfolio reflects sustained engagement with computational methods across lighting systems, biomedical imaging, and computer graphics applications.

• Electrical-Electronics Engineering
• Image Processing
• Signal Processing
• +1 more

Cédric HERZET is a Permanent Member of CREST at INRIA Rennes, focusing on statistical learning theory, optimization, and inverse problems. His work bridges theoretical analysis with practical algorithm development. Primary affiliation: INRIA Rennes (French National Institute for Research in Digital Science) Research Interests: Statistical Learning Theory, Optimization algorithms, Operational Research, Inverse Problems. His work particularly addresses sparse approximation, compressed sensing, and iterative thresholding methods. Key Contributions: Development of Bayesian pursuit algorithms, geometric analysis of subspace clustering with outliers, and analysis of state evolution in dense graph message passing. His theoretical work has direct applications in signal/image processing and machine learning. Software: Created MATLAB implementations of sparse approximation algorithms and Bernoulli-Gaussian Lab toolbox for Bayesian pursuit simulation.

• Statistical Learning Theory
• Optimization
• Operational Research
• +3 more

Professor Frederic Noo is a leading academic in the field of Medical Imaging at the University of Utah 's Radiology & Imaging Sciences department. With a PhD from the University of Liege, his work focuses on advancing X-ray Computed Tomography (CT) through innovative image reconstruction algorithms , cone-beam CT , and dual energy CT methods. He contributes to the CT Research Lab and collaborates internationally with experts like Michael Defrise and Hiroyuki Kudo. Research Interests : CT physics, beam hardening correction, deep learning in imaging, iterative reconstruction, and noise/resolution analysis. Collaborations : Extensive work with institutions in Germany (J. Hornegger), Japan (H. Kudo), and France (M. Defrise). Teaching : Co-author of a book chapter in Informatics in Medical Imaging (2012), demonstrating expertise in CT principles.

• X-ray Computed Tomography
• Image Reconstruction Algorithms
• Medical Imaging Physics
• +7 more

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/ .

• Computational Mechanics
• Biomechanical Applications in Medicine
• Brain Clearance During Sleep
• +5 more