جست‌وجوی استاد و پژوهشگر

• 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.

• Nonsmooth Optimization
• Variational Analysis
• Mathematical Programming
• +۱ مورد دیگر

Nghia T. A. Tran is an Associate Professor in the Department of Mathematics and Statistics at Oakland University, Rochester, MI, USA. His research focuses on mathematical optimization, variational analysis, and inverse problems, with applications spanning signal processing, statistics, and numerical algorithms. He serves as an Associate Editor for the journal Set-Valued and Variational Analysis . Research Interests: Dr. Tran specializes in: Nonsmooth optimization methods and variational inequalities Stability analysis of convex and non-convex optimization problems Regularization techniques for linear inverse problems Convergence properties of iterative algorithms Publication Trends: His 15 most recent publications (2016–2025) demonstrate a consistent focus on: Geometric characterizations of optimization stability (Lipschitz/tilt properties) Solution uniqueness in regularization frameworks (e.g., Lasso, nuclear norm) Convergence analysis of splitting methods and proximal algorithms Applications to matrix completion, inverse problems, and statistical learning Works predominantly appear in premier journals including SIAM Journal on Optimization, Mathematical Programming, and Journal of Optimization Theory and Applications.

Yuejie Chiمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• Signal Processing
• +۴ مورد دیگر

Yuejie Chi is the Sense of Wonder Group Endowed Professor of Electrical and Computer Engineering in AI Systems at Carnegie Mellon University, with affiliations in the Machine Learning Department and CyLab. She holds a Ph.D. from Princeton University and a B.Eng. (Hon.) from Tsinghua University. Her research focuses on theoretical foundations of data science, machine learning, signal processing, and inverse problems, with applications in sensing, imaging, and AI systems. Her work emphasizes interdisciplinary approaches at the intersection of statistics, optimization, and sensing. Notable achievements include contributions to high-dimensional structured signal processing, for which she received the inaugural IEEE Signal Processing Society Early Career Technical Achievement Award (2019) and the PECASE (2019). She is an IEEE Fellow (2023) and has been recognized as a Goldsmith Lecturer (2021) and Distinguished Lecturer (2022). Research interests include generative AI, reinforcement learning, nonconvex optimization, federated learning, and resource-efficient algorithms. Her group collaborates on projects such as data-driven materials research and agile waveform design for communication networks. She has advised numerous PhD students, many of whom have pursued academic and industry roles in leading institutions. Awards include the SIAM Activity Group on Imaging Science Best Paper Prize (2024), NSF/ONR/AFOSR young investigator awards, and leadership roles in top conferences like NeurIPS, ICML, and IEEE workshops.

William Rundellمشاهده پروفایل

استاد

• Partial Differential Equations
• Inverse Problems
• Fractional Calculus
• +۳ مورد دیگر

William Rundell is a Professor of Mathematics at Texas A&M University, affiliated with the Department of Mathematics in the College of Arts & Sciences. He holds a Ph.D. and B.Sc. from Glasgow University (1974 and 1971, respectively). His research focuses on inverse problems for partial differential equations, fractional calculus, and numerical methods for ill-posed systems. Notable interests include parameter identification in elliptic/parabolic equations, inverse scattering, and subdiffusion models. Education: B.Sc. (Hons) Mathematics and Natural Philosophy, Glasgow University, 1971. Ph.D., Glasgow University, 1974. Research Interests: Inverse problems for PDEs and fractional operators. Ill-conditioned problems in integral and differential equations. Fractional diffusion, subdiffusion, and numerical algorithms. Nonlinear inverse problems and iterative methods. Teaching & Mentoring: Offered Honors Calculus and ODE courses (Fall 2022). Guides undergraduate research projects in differential equations and mathematical cryptography. NSF-funded summer research opportunities for students. Labs/Teams: Engages in collaborative research on inverse problems, fractional calculus, and numerical analysis.

• Fractional Calculus
• Numerical Analysis
• Partial Differential Equations
• +۴ مورد دیگر

Professor Suzan Cival Buranay is a faculty member at the Faculty of Arts and Sciences, Eastern Mediterranean University, serving as Department Head in Mathematics. She earned her PhD in Mathematics (2007), MS in Applied Mathematics (1995), and BS in Applied Mathematics (1993) from the same university. Research Interests: Her work focuses on Fractional calculus and its applications to differential equations Numerical analysis of finite difference schemes and hexagonal grids Approximation theory with modified Bernstein-Kantorovich operators Preconditioning techniques for linear systems and ill-posed problems Computational methods for heat equations and Volterra integral equations Recent Contributions: She has developed high-accuracy numerical methods for fractional boundary value problems, Caputo derivatives, and singular integral equations. Her work on hexagonal grid approximations for heat equation derivatives has advanced computational efficiency. Thesis Supervision: She has advised 5 PhD students and 6 MS students, with ongoing supervision of two PhD/MS theses. Her supervised works include collocation methods for fractional equations and implicit schemes for heat equations. Projects: Currently leading a project on collocation methods for generalized Bagley-Torvik equations (2023-2024), and previously worked on block-grid methods for Laplace equations with singularities (2010-2011).

Iveta Hnetynkovaمشاهده پروفایل

دانشیار

• Numerical Mathematics
• Linear Algebra
• Inverse Problems
• +۵ مورد دیگر

Iveta Hnetynkova is an Associate Professor at the Department of Numerical Mathematics, Faculty of Mathematics and Physics, Charles University in Prague. She specializes in numerical linear algebra, inverse problems, and regularization methods, with applications in image processing and scientific computing. Education: Doctor of Natural Sciences (RNDr.) from Charles University (2003) Ph.D. in Scientific Computations from Charles University (2006) Habilitation thesis on error-contaminated linear approximation (2019) Research Interests: Krylov subspace methods Total Least Squares (TLS) formulations Noise revealing in discrete inverse problems Tensor generalizations and structured matrices Applications in image processing and jewelry defect analysis Scientific Awards: Visegrad Group Young Researcher Award (2014) J. Jirsa Prize for textbook excellence (2013) I. Babuska Prize (2nd place) (2007) SVOČ Prize in mathematics (2003)

• Signal Processing
• Machine Learning
• Data Science
• +۷ مورد دیگر

Yuejie Chi is the Sense of Wonder Group Endowed Professor of Electrical and Computer Engineering in AI Systems at Carnegie Mellon University, holding courtesy appointments in the Machine Learning department and CyLab. She earned her Ph.D. and M.A. in Electrical Engineering from Princeton University and B.Eng. (Hon.) from Tsinghua University. Her research centers on theoretical and algorithmic foundations of data science with expertise in signal processing, machine learning, and inverse problems. Key application areas include sensing, imaging, decision-making systems, and societal-scale data challenges. Her work bridges mathematical theory with real-world implementations through preconditioning techniques, overparameterization frameworks, and diffusion models for ill-conditioned estimation problems. Major recognitions include: Presidential Early Career Award for Scientists and Engineers (PECASE) IEEE Signal Processing Society Early Career Technical Achievement Award IEEE Signal Processing Society Young Author Best Paper Award Goldsmith Lecturer by IEEE Information Theory Society Distinguished Lecturer by IEEE Signal Processing Society IEEE Fellow (2023) for contributions to statistical signal processing with low-dimensional structures Dr. Chi serves as Associate Editor for IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing, IEEE Transactions on Pattern Recognition and Machine Intelligence, Information and Inference: A Journal of the IMA, and SIAM Journal on Mathematics of Data Science. Her research program integrates theoretical guarantees with practical implementations in AI systems, particularly through collaborations with CyLab on security-aware data science frameworks.

Olivier Scailletمشاهده پروفایل

استاد

• Finance
• Statistics
• Econometrics
• +۳ مورد دیگر

Olivier Scaillet is a Full Professor at the Geneva Finance Research Institute (GFRI) of the University of Geneva and holds a senior chair at the Swiss Finance Institute . His academic focus spans Finance , Statistics , and Econometrics , with expertise in Derivatives Pricing , Asset Pricing , Econometric Theory , and Quantitative Risk Management . Education : PhD in Applied Mathematics from University Paris IX Dauphine Leadership : Director of GFRI and Deputy Director (Education) His research includes nonparametric methods , instrumental variable regression , stochastic volatility models , and systemic risk measures . Recent work explores dynamic portfolio optimization , high-frequency jump analysis , and time-varying risk premia in equity and cryptocurrency markets. Professor Scaillet has been recognized with awards for best papers in Journal of Empirical Finance and Banque Privée Espirito Santo , and is a Fellow of the Society of Financial Econometrics . He serves as an associate editor for journals in Econometrics , Statistics , and Finance , and advises BNPParibas research teams in Paris and London.

Li Wangمشاهده پروفایل

دانشیار

• numerical analysis
• scientific computing
• applied analysis
• +۴ مورد دیگر

Li Wang is an active Associate Professor at the School of Mathematics, University of Minnesota, with a distinguished research career in applied mathematics. His work bridges theoretical analysis and computational methods across multiple scientific domains. Dr. Wang's research focuses on numerical analysis, scientific computing, applied analysis, kinetic theory, optimal transport, inverse problems, and scientific machine learning . His work demonstrates a strong emphasis on developing numerical methods for partial differential equations with applications in physics, engineering, and data science. He has made significant contributions to asymptotic preserving schemes, gradient flows, and computational methods for kinetic equations. His publication record shows consistent output in top-tier journals including Journal of Computational Physics, SIAM Journal on Applied Mathematics, and SIAM Journal on Numerical Analysis. The research trends indicate growing integration of machine learning techniques with traditional numerical methods, particularly evident in recent work on DeepONet applications and scientific machine learning approaches to inverse problems and evolution equations. Dr. Wang actively mentors students and researchers, inviting highly motivated individuals to join his research group. He is organizing significant academic events including a micro-workshop on Data Driven methods in May 2025 and previously organized a Workshop on Kinetic and Optimal Transport in October 2023. His research group maintains an active presence in the applied mathematics community, with particular strengths in computational methods for kinetic theory and optimal transport problems. The group appears to collaborate extensively with researchers at other institutions, as evidenced by the diverse author lists on recent publications.

Magda Joachimiakمشاهده پروفایل

پژوهشگر

• Heat Transfer
• Thermal Engineering
• Mathematical Modeling
• +۳ مورد دیگر

Dr. Magda Joachimiak is affiliated with the Thermal Power Institute at Poznań University of Technology, working in the Fuel and Renewable Energy Department. Her research focuses on mathematical modeling of heat flow processes in thermal machines, inverse heat conduction problems, and thermochemical treatment furnace analysis. She has conducted industrial internships at SECO/WARWICK and Filen, addressing heat flow in engine components and gasifier walls. Her work emphasizes numerical methods like Chebyshev polynomials for solving heat-related engineering challenges. Key publications span 2019-2021, addressing regularization techniques, furnace thermodynamics, and heat transfer analysis. Research Projects: Includes heat transfer coefficient determination in cylinders, Laplace equation solutions, and condenser heat flow analysis. She leads student laboratory activities and contributes to academic-industrial collaborations.

• Statistical Inverse Problems
• Regularization Theory
• Uncertainty Quantification
• +۷ مورد دیگر

Prof. Dr. Werner Frank holds the Chair of Mathematics IX (Inverse Problems) at the University of Würzburg. He is a leading researcher in statistical inverse problems, focusing on regularization theory, uncertainty quantification, and applications in biophysics, particularly fluorescence microscopy. His work bridges statistics and inverse problem methodologies with non-Gaussian noise analysis. Education: PhD in Mathematics from Georg-August-Universität Göttingen (2012) Postdoctoral Research (2012–2014) at the Institute for Numerical and Applied Mathematics Group Leader, Statistical Inverse Problems in Biophysics, at the Max Planck Institute for Biophysical Chemistry (2014–2020) Research Interests: Statistical inverse problem theory, multiscale scanning, minimax testing, super-resolution microscopy, and imaging techniques with statistical guarantees. His recent work emphasizes applications in biophysics and nanoscale imaging. Publications: Over 30 peer-reviewed articles in top journals like Inverse Problems , Journal of the Royal Statistical Society , and SIAM Journal on Scientific Computing . Key topics include Poisson data regularization, ill-posed problem analysis, and variational methods for image denoising. Affiliations: Active in professional societies such as the Society for Inverse Problems (GIP), IFIP Working Group 7.4, and the International Photonic Imaging Association (IPIA). Regular speaker at international conferences like Inverse Problems: Modeling and Simulation (IPMS) and Applied Inverse Problems (AIP). Future Work: Expanding research on adaptive regularization methods, spatial statistical resolution in microscopy, and interdisciplinary collaborations in imaging science.

Charles SOUSSENمشاهده پروفایل

استاد

• Inverse Problems
• Sparse Approximation
• Signal Processing
• +۳ مورد دیگر

Charles SOUSSEN is a Professor at CentraleSupélec and L2S (Laboratoire des Signaux et Systèmes). He holds a PhD from Université Paris-Sud (2000) and a Habilitation from Université de Lorraine (2013). His research focuses on inverse problems, sparse approximation, signal processing, and biomedical applications. He leads the Signals and Statistics Pole at L2S since 2020 and has coordinated the French ANR project BECOSE (2016–2020). His work spans areas like hyperspectral imaging, optical spectroscopy, and algorithm development for sparse representations. Education : Ecole Nationale Supérieure en Informatique et Mathématiques Appliquées (ENSI-MAG), Grenoble (1996) PhD in Signal and Image Processing, L2S, Université Paris-Sud (2000) Habilitation à Diriger des Recherches, Université de Lorraine (2013) Research Interests : His primary research areas include inverse problems, sparse approximation, signal processing algorithms, biomedical imaging, and hyperspectral data analysis. He develops methods for signal deconvolution, source separation, and modal analysis with applications in astronomy, geology, and medical diagnostics. Grants & Projects : ANR BECOSE (2016–2020): Sparse approximation algorithms for ill-conditioned inverse problems Labs & Teams : He is affiliated with L2S and leads the Signals and Statistics research group. His work involves collaborations in biomedical optics, hyperspectral analysis, and algorithm development for engineering and medical applications.

• Partial Differential Equations
• Integral Equations
• Inverse Scattering Problems
• +۳ مورد دیگر

Vassilios Sevroglou is a Professor at the Department of Statistics and Insurance Science, University of Piraeus, School of Finance and Statistics. His academic career spans since 2002, with roles including Assistant Lecturer (2002-2006) and Lecturer (2007-2011) at the same university, and visiting positions at the University of Ioannina. He holds a PhD in Applied Mathematics and a bachelor's degree in Mathematics from the National and Kapodistrian University of Athens. His research focuses on Partial Differential Equations, Integral Equations (Riesz-Fredholm Theory), and inverse scattering problems in elasticity and acoustics. Key areas include 2D/3D elastic wave scattering, thermoelastic interactions, chiral media applications, and computational methods for boundary value problems. The 15 most recent articles highlight advancements in chiral electromagnetic scattering, stochastic boundary value problems, thermoelastic wave interactions, and numerical reconstruction techniques. These works emphasize inverse problems, Wiener chaos expansions, and regularization methods in acoustics and elasticity. Teaching activities encompass Calculus, Differential Equations, Stochastic Finance, and advanced applied mathematics courses. No specific scientific awards or lab affiliations are mentioned in the provided texts.

Damek Shea Davisمشاهده پروفایل

استادیار

• Optimization
• Stochastic Algorithms
• Nonconvex Optimization
• +۴ مورد دیگر

Damek Shea Davis is an Assistant Professor in Cornell University's School of Operations Research and Information Engineering, College of Engineering. He received his Ph.D. in Mathematics from UCLA in 2015 and joined Cornell in 2016. Davis focuses on mathematical foundations of data science, with particular expertise in optimization, statistical learning, and numerical algorithms. His research develops theoretical frameworks and efficient algorithms for nonconvex, nonsmooth, and stochastic optimization problems. Key contributions include convergence guarantees for stochastic subgradient methods on nonconvex functions, analysis of optimization landscapes for phase retrieval, and development of methods for high-dimensional statistical estimation. His work bridges mathematical optimization with machine learning and signal processing applications. Davis has received numerous honors including the Sloan Research Fellowship (2020) and INFORMS Optimization Society Young Researchers Prize (2019). His publications advance understanding of optimization algorithm behavior in statistically challenging regimes, with recent work exploring geometric convergence properties and optimality conditions.