جست‌وجوی استاد و پژوهشگر

Damek Davisمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Damek Davis is an Assistant Professor in the Department of Mathematics at Cornell University, affiliated with both the College of Engineering and the College of Arts and Sciences. He holds a Ph.D. in Mathematics from the University of California, Los Angeles (2015). His research focuses on developing and analyzing optimization algorithms for large-scale, nonconvex, and nonsmooth problems arising in machine learning and signal processing. He emphasizes theoretical guarantees on algorithm performance and practical implementations that leverage modern computing architectures. His work spans stochastic optimization, subgradient methods, and convergence analysis, with applications to computational microscopy, phase retrieval, and decentralized systems. Recent contributions include advancements in adaptive stepsize methods, sharpness-aware optimization, and global optimality conditions for mixture models. Davis collaborates on theoretical foundations and algorithmic innovations in high-dimensional statistics, nonlinear optimization, and parallel computing. His scholarly output includes over 50 peer-reviewed articles, with recent highlights in top venues such as SIAM Journal on Optimization, Mathematical Programming, and IEEE Transactions on Signal Processing. His research is supported by grants from NSF and AFOSR, focusing on topics like robust statistical estimation and algorithmic scalability.

Longxiu Huangمشاهده پروفایل

استادیار

• Data/Signal Processing
• Applied Harmonic Analysis
• Machine Learning
• +۴ مورد دیگر

Longxiu Huang is an Assistant Professor at Michigan State University (MSU), holding dual appointments in the Department of Computational Mathematics, Science and Engineering (CMSE) and the Department of Mathematics. Prior to this, she was an Assistant Adjunct Professor at UCLA's Department of Mathematics. Her research focuses on Data/Signal Processing, Applied Harmonic Analysis, Machine Learning, Random Matrix/Tensor Analysis, Dynamical Sampling, and Optimization. Education : Ph.D. in Mathematics, Vanderbilt University (2014-2019) M.S. in Mathematics, Fudan University (2012-2014) B.S. in Mathematics, Sun Yat-Sen University (2009-2012) B.S. in Software Engineering, Sun Yat-Sen University (2008-2009) Research Interests : Huang's work bridges theoretical mathematics and applied computational methods. She explores signal reconstruction in dynamical systems, tensor decompositions for high-dimensional data, robust matrix/tensor completion, and optimization algorithms for ill-conditioned problems. Her contributions include CUR-based methods for low-rank approximations and sampling strategies for sparse signals on graphs. Professional Activities : She co-organizes the One World MINDS Seminar, a global initiative in mathematical data science. Her research has been published in top journals like SIAM Journal on Imaging Sciences and Applied and Computational Harmonic Analysis, with a focus on theoretical guarantees and practical applications in imaging and machine learning. Lab/Teams : Active collaborator in interdisciplinary projects at MSU, particularly in computational mathematics and machine learning applications.

Melina Freitagمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Data Assimilation
• Inverse Problems
• +۶ مورد دیگر

Melina Freitag is Professor for Data Assimilation at the Institute for Mathematics, University of Potsdam. Her research spans numerical linear algebra, inverse problems, and model order reduction, with applications in geophysics, image processing, and machine learning. She leads the Data Assimilation Group and contributes to the SFB 1294 Collaborative Research Center. Education : Prof. Freitag earned her Diplom in Mathematics at TU Chemnitz (2004) and PhD in Mathematical Sciences from University of Bath (2007). Research Themes : Her work focuses on Krylov subspace methods, low-rank approximations, and preconditioning for large-scale systems. She bridges classical numerical analysis with modern data assimilation, addressing challenges in: Bayesian inverse problems with unstable systems Optimized spectral sampling in X-ray imaging Physics-informed neural networks for Navier-Stokes inversion Parameter-dependent eigenvalue analysis Collaborations & Grants : She collaborates with institutions like KTH Stockholm and Arizona State University. Her group secures funding through SFB 1294 and participates in INI Cambridge networks. Leadership : Co-Chair of GAMM Activity Group on Applied Numerical Linear Algebra SIAM Activity Group on Linear Algebra Chair (2022) Teaching : Delivers courses on numerical optimization, matrix methods in data science, and inverse problems, integrating computational theory with real-world applications.

Mykhailo Kuianمشاهده پروفایل

مدرس

• Applied Mathematics
• Numerical Analysis
• Linear Algebra
• +۳ مورد دیگر

Dr. Mykhailo Kuian is a Full-time Lecturer in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University , located in Cleveland, Ohio. He holds a Ph.D. (institution unspecified in provided data). His work bridges theoretical mathematics with practical engineering and computational applications. Education: Ph.D. (details not specified). Research Interests: Dr. Kuian’s research focuses on numerical analysis, applied mathematics, and optimization. Key areas include solving linear ill-posed operator equations, developing efficient matrix factorization algorithms for Vandermonde matrices, and enhancing multiparameter measurement systems. His work extends to electromagnetic design, incorporating uncertainty quantification and shape control in reflector systems. Recent Research Trends: His publications (2018–2025) emphasize numerical methods for inverse problems, matrix conditioning optimization, and interdisciplinary applications in optics and engineering. Notable contributions include fast computational techniques for Vandermonde-like matrices and accuracy improvements in polarized light microscopy systems. Awards/Grants: No awards or grants are mentioned in the provided information. Advising & Labs: No advisees, grants, or affiliated laboratories/teams are listed in the available data.

Manfred Trummerمشاهده پروفایل

استاد

• Applied Mathematics
• Computational Methods in Medical Imaging
• Numerical Methods for Differential Equations
• +۳ مورد دیگر

Manfred Trummer is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. His research focuses on computational methods in medical imaging, particularly dynamic single photon emission computed tomography (SPECT), and numerical methods for differential equations, including spectral collocation and radial basis function techniques. He emphasizes solving ill-posed problems and improving stability, accuracy, and adaptivity in numerical solutions. Trummer holds a Ph.D. in Mathematics from ETH Zurich (1983). His work bridges applied mathematics and computational science, addressing challenges in medical imaging reconstruction and high-order numerical methods for differential equations. Notable contributions include advancements in spectral methods, iterative reconstruction techniques for SPECT, and preconditioning strategies for ill-conditioned systems. His research spans dynamic imaging applications, such as kidney imaging and 4D SPECT reconstruction, alongside foundational numerical analysis topics like matrix factorization, boundary layer resolution, and RBF stability. Recent publications highlight innovations in spectral collocation for mixed functional differential equations and conformal mapping using Szegő kernels. Trummer teaches graduate courses such as APMA 923 (Numerical Methods in Continuous Optimization). While no specific grants or labs are detailed in the text, his work reflects sustained engagement with interdisciplinary computational challenges in mathematics and medical imaging.

• Inverse Problems
• Data Assimilation
• Applied Mathematics
• +۷ مورد دیگر

Roland Potthast is a Professor specializing in applied mathematics and inverse problems with significant contributions to data assimilation and meteorological modeling. His work bridges mathematical theory and atmospheric sciences, with applications in weather prediction and geophysical systems. His research interests include inverse problems, data assimilation, and mathematical modeling of atmospheric dynamics. He has developed and refined methods such as the range test, no-response test, and particle filters for use in both theoretical and operational contexts. His work often involves solving ill-posed problems and improving the accuracy of numerical weather prediction through advanced statistical and filtering techniques. The 15 most recent publications reveal a consistent focus on data assimilation techniques, particularly particle and ensemble filters, applied to meteorological models and inverse scattering problems. There is a strong trend toward nonlinear and non-Gaussian methods, localization strategies, and the integration of remote sensing data into forecasting systems. His work spans both theoretical developments in applied mathematics and practical implementations in operational weather models. The following scientific awards have not been explicitly mentioned in the provided text. Roland Potthast has collaborated extensively with researchers across institutions on data assimilation and inverse problems, though specific details about student advising or grant funding are not available in the current dataset. His publications suggest leadership in developing novel mathematical frameworks for environmental modeling and remote sensing applications. There is no explicit mention of specific laboratories or research teams in the provided text, though his frequent collaborations with institutions such as DWD (German Weather Service) and involvement in operational NWP frameworks suggest integration within large-scale meteorological research consortia.

• Data Assimilation
• Numerical Weather Prediction
• Observation Error Covariance
• +۷ مورد دیگر

Jemima M. Tabeart is a researcher affiliated with the School of Mathematics at the University of Edinburgh, UK, and previously with the School of Mathematical, Physical and Computational Sciences at the University of Reading. Her work lies at the intersection of applied mathematics and meteorology, focusing on data assimilation systems used in numerical weather prediction. Her research interests include: Data Assimilation Observation Error Covariance Modeling Numerical Linear Algebra in Geophysical Systems Optimization and Conditioning of Inverse Problems Statistical Methods for Atmospheric Science Her recent publications analyze the impact of correlated observation errors in data assimilation, particularly in the Met Office 1D-Var system, with a focus on improving matrix conditioning using reconditioning techniques like ridge regression. These works demonstrate a strong trend toward enhancing computational efficiency and numerical stability in weather forecasting models. She has contributed to key journals such as the Quarterly Journal of the Royal Meteorological Society , Tellus A , and Numerical Linear Algebra with Applications . She has collaborated extensively with researchers from the University of Reading and the Met Office, indicating active engagement in the UK meteorological research community. Jemima M. Tabeart has not been mentioned to have supervised any students or received scientific awards in the provided text. She is involved in research supported by major UK science funding bodies such as EPSRC and NERC.

Miao-Jung Yvonne Ouمشاهده پروفایل

استاد

• Inverse Scattering Theory
• Poroelastic Materials
• Partial Differential Equations
• +۱۲ مورد دیگر

Miao-Jung Yvonne Ou is a Professor in the Department of Mathematical Sciences at the University of Delaware, with affiliated faculty status in the Department of Biomedical Engineering. She is based in 508 Ewing Hall, Newark, DE, and actively contributes to research and graduate education in applied mathematics. Research Interests: Her work centers on inverse and direct scattering in composite and poroelastic materials, partial and integral differential equations, homogenization theory, and the application of Herglotz-Nevanlinna functions in systems with memory. She applies these to material sciences, particularly in modeling wave propagation and effective properties in complex media such as bones and porous composites. Her recent publications (2023–2015) show a strong trend in numerical methods (e.g., discontinuous Galerkin, RKHS regularization), wave modeling in poroelastic and dispersive media, and inverse problems in biomedical and geophysical contexts. She frequently employs advanced mathematical tools like fractional calculus, spectral analysis, and integral representations to address real-world physical systems. Scientific Engagement: She is currently part of the EU RISE EXPOWER project (2021–2025), promoting international research exchange in mathematics. She teaches graduate courses such as Math 508: Introduction to Complex Variables and Applications. Advising and Grants: She supervises graduate student research projects in areas like efficient quadrature for fractional derivatives, RKHS frameworks, spectral analysis of poroelastic waves, and NMR imaging. Funding includes the EU RISE grant, supporting collaborative research and student involvement. Laboratories and Teams: She collaborates with interdisciplinary teams in applied mathematics, biomedical engineering, and computational science. Her work with the MADNESS project and poroelastic wave modeling indicates active participation in high-performance scientific computing and modeling environments.

Narek Hovsepyanمشاهده پروفایل

استادیار

• Applied Mathematics
• Computational Mathematics
• Mathematical Physics
• +۵ مورد دیگر

Narek Hovsepyan is a Hill Assistant Professor in the Department of Mathematics at Rutgers University within the School of Arts and Sciences. His research spans applied mathematics, mathematical physics, and computational analysis, focusing on wave propagation, inverse problems, and analytic continuation techniques. Education: PhD from Temple University (advisor: Yury Grabovsky), MSc from University of Bonn (advisor: Juan J. L. Velázquez), BSc from Yerevan State University (advisor: Hakop Hakopian) Research: Scattering theory, nonlinear optical phenomena, quasiconvex analysis, and spectral methods Activities: Co-organizer of the Applied and Computational Mathematics Seminar since 2023, visiting researcher at Institut Fourier (Université Grenoble Alpes) in 2024 Contact: nh507@rutgers.edu, narek.hovsepyan@rutgers.edu

Wolfgang Ringمشاهده پروفایل

استاد

• Inverse Problems
• Partial Differential Equations
• Shape Optimization
• +۳ مورد دیگر

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

Elena Resmeritaمشاهده پروفایل

دانشیار

• Inverse Problems
• Regularization Methods
• Image Restoration
• +۳ مورد دیگر

Elena Resmerita is an Associate Professor at the Institute of Mathematics , Alpen-Adria University of Klagenfurt, Austria. She holds a Habilitation in Mathematics (2013) and a PhD in Mathematics (2003) from the University of Klagenfurt and Haifa respectively. 2013: Habilitation, University of Klagenfurt 2003: PhD, University of Haifa 1997: MSc, Al. I. Cuza University of Iasi Her research focuses on inverse problems , regularization methods for ill-posed problems , image restoration , continuous optimization , convex analysis , and iterative methods for convex feasibility problems . Her recent publications highlight advancements in regularization theory, multiscale decomposition, and sparsity-driven optimization. Key contributions include: Nonconvex regularization techniques Entropic Landweber methods Bregman iterations for image processing Convergence analysis of stochastic algorithms She has received prestigious scientific awards : FWF Elise Richter Fellowship IPAM Fellow at UCLA Active in academic service, she co-organizes workshops like the Alps-Adriatic Workshop in Inverse Problems and contributes to the European Women in Mathematics (EWM) as Deputy Convenor and committee member. Her teaching spans Analysis, Calculus, and specialized inverse problem courses.

• Spectral Analysis
• Integral Operators
• Boundary Value Problems
• +۴ مورد دیگر

Dr. Daisuke Kawagoe is an Assistant Professor at the Graduate School of Informatics, Kyoto University, specializing in applied mathematics and mathematical analysis. His research focuses on spectral analysis, boundary value problems, and inverse problems, with applications in tomography and kinetic theory. He holds a Ph.D. in Informatics from Kyoto University. Current Position: Assistant Professor (2019–present) Previous: Postdoctoral Researcher at Inha University (2018–2019) Research Interests: Spectral analysis of integral operators, boundary value problems for PDEs, and mathematical foundations of tomographic imaging. He has contributed to understanding discontinuities in radiative transport equations and their implications for optical tomography. Key Projects: Leading a JSPS-funded project on tomography-based mathematical analysis (2024–2028) and co-leading studies on fluid dynamics and kinetic theory. His work integrates theoretical analysis with numerical methods, addressing ill-posed problems in applied mathematics. Awards: 2022 Prize for Excellent Young Applied Mathematicians (Mathematical Society of Japan) for groundbreaking work on radiative transport equation discontinuities.

Jan Modersitzkiمشاهده پروفایل

استاد

• Image Registration
• Variational Methods
• Medical Imaging
• +۲ مورد دیگر

Jan Modersitzki is a Full Professor of Applied Mathematics at the University of Lübeck, where he leads research at the Institute of Mathematics and Image Computing. He holds a secondary affiliation with Fraunhofer MEVIS, focusing on image registration projects. His academic journey includes a Diploma (1990) and PhD (1995) from the University of Hamburg, followed by a Habilitation (2003) from the University of Lübeck. Career milestones include positions at Emory University, McMaster University, and CAU Kiel. Modersitzki's research centers on computational methods for medical imaging , with emphasis on variational techniques, optimization, and deformable registration. He developed the widely used FAIR toolbox (Flexible Algorithms for Image Registration) and has contributed to foundational theories in image alignment. His work bridges mathematical rigor with clinical applications in oncology, ophthalmology, and pulmonology. Analysis of his 15 most recent publications (2017–2025) reveals dominant themes: multiscale registration , discontinuous deformation modeling , and high-performance computing solutions for medical images. Trends include GPU acceleration, inverse problem optimization, and integration of machine learning with traditional variational frameworks. He actively contributes to academic societies including GAMM, MICCAI, and SIAM. No awards, grants, or supervised students are detailed in available materials.

• Partial Differential Equations
• Integral Equations
• Boundary Value Problems
• +۵ مورد دیگر

Vasilios Sevroglou is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Piraeus, where he has been serving since 2012. Previously, he held positions as Lecturer (2007-2011) at the same department and as Visiting Professor at the University of Ioannina (2002-2006). His academic foundation includes a Doctorate in Applied Mathematics and Physical Sciences from NTUA (2001) with research focused on partial differential equations, specifically "The Herglotz Functions in Two-Dimensional Elasticity Applications to the Inverse Scattering Problem," and a Bachelor's degree in Mathematics from the National & Kapodistrian University of Athens (1995). University: University of Piraeus School: School of Finance and Statistics Department: Department of Statistics and Actuarial Science Academic Rank: Assistant Professor Dr. Sevroglou's research spans multiple areas of applied mathematics with a strong focus on Partial Differential Equations , Integral Equations , and Wave Scattering phenomena. His work particularly addresses boundary value problems in unbounded domains, scattering of acoustic and elastic wave fields, and electromagnetic field propagation in complex media. His research integrates theoretical mathematical approaches with practical applications in elasticity theory and inverse scattering problems. The interdisciplinary nature of his work bridges pure mathematics with physics and engineering applications, particularly in the areas of material science and wave propagation. Analysis of his publication record from 2005-2012 reveals a consistent research trajectory focused on elastic and acoustic scattering problems. His work demonstrates progression from foundational theoretical investigations to increasingly complex applications involving penetrable obstacles, mixed impedance problems, and chiral media. The majority of his publications address inverse problems in elasticity, with particular emphasis on developing and refining mathematical methods for shape reconstruction and parameter estimation. His collaborations with researchers like G. Pelekanos, C. Athanasiadis, and I.G. Stratis show an established research network focused on advancing mathematical approaches to scattering theory. Dr. Sevroglou maintains an active teaching schedule across both undergraduate and postgraduate programs. His undergraduate teaching includes core mathematics courses such as Infinite Calculus, Differential Equations, Applied Linear Algebra, and Algebra. At the postgraduate level, he teaches specialized courses including Principles of Economics and Financial Mathematics, Stochastic Financial Models, and Investment Portfolio Management. His teaching portfolio demonstrates the integration of his mathematical expertise with financial applications, reflecting the interdisciplinary nature of the Statistics and Actuarial Science department.

• Coefficient inverse problems (CIPs)
• Adaptive finite-element methods
• Globally convergent numerical methods
• +۵ مورد دیگر

Larisa Beilina is Full Professor at the Department of Applied Mathematics and Statistics, Chalmers University of Technology, Sweden. Her work sits at the intersection of numerical analysis, inverse problems, and computational electromagnetics, with recent extensions to biomedical modeling. Education and Career Milestones: PhD awarded in 2003 for pioneering an adaptive finite-element method for hyperbolic coefficient inverse problems. In 2008, co-developed—with Prof. M. V. Klibanov (UNC Charlotte)—a globally convergent numerical method for coefficient inverse problems. 2011: experimentally validated the globally convergent algorithm using blind field data from the US Army Research Laboratory’s Forward-Looking Radar. Research Interests Beilina’s research focuses on ill-posed and coefficient inverse problems governed by PDEs. She designs adaptive finite-element algorithms that deliver reliable reconstructions from limited, noisy data, and couples these with globally convergent optimization techniques to escape local minima. Application domains span microwave thermometry for hyperthermia monitoring , early-stage melanoma detection via RF/microwave imaging , subsurface imaging using ground-penetrating radar , and parameter identification in HIV dynamics models . Recent Publication Trends Over 2022-2024 her articles emphasize four themes: (1) stabilized and explicit finite-element schemes for Maxwell’s equations and coupled wave systems; (2) hybrid FEM/FDM domain-decomposition solvers for large-scale 3D electromagnetic inverse problems; (3) adaptive error control and a posteriori estimates ensuring quantitative accuracy in conductivity and permittivity reconstruction; and (4) biomedical applications —ranging from microwave melanoma detection to time-adaptive determination of drug efficacy in HIV infection models. Scientific Awards & Recognition Keynote speaker and session chair at multiple international conferences on inverse problems and computational electromagnetics. Serves on editorial boards and as guest editor for proceedings volumes such as Springer Proceedings in Mathematics & Statistics and Journal of Inverse and Ill-Posed Problems . Research Teams & Grants Leads a multidisciplinary group comprising graduate researchers and post-docs collaborating with partners including the US Army Research Laboratory, University of North Carolina at Charlotte, and medical institutions in Sweden. Funding sources encompass Swedish Research Council grants, EU Horizon programs, and industry contracts focused on microwave diagnostics and multiphase flow measurements. Laboratory & Infrastructure Heads the Computational Inverse Problems Laboratory at Chalmers, equipped with high-performance GPU clusters, anechoic chamber facilities for microwave experiments, and access to clinical data sets for melanoma and HIV projects.