جست‌وجوی استاد و پژوهشگر

• Mathematics
• Complex Polynomial Approximation
• Functional Analysis
• +۵ مورد دیگر

Igor Shevchuk is a Professor and Chair of the Department of Mathematical Analysis at Taras Shevchenko National University of Kyiv, Ukraine. He also holds a 1/4 position at National Pedagogical University since 1986. His work spans over four decades at the Institute of Mathematics of the National Academy of Sciences of Ukraine (1972–present), transitioning from Junior Scientist to Providing Scientist. Education: BSc/MSc (1969) and PhD (1973) from Taras Shevchenko National University of Kyiv; DSc (1985) from the Institute of Mathematics NAS of Ukraine. Research Interests: Shevchuk focuses on Complex polynomial approximation Extension of functions from arbitrary sets Shape-preserving approximation (SPA) Boundary and extremal problems in functional analysis Smoothness criteria and Jackson-type estimates Comonotone approximation Publications: He has contributed to key advancements in approximation theory, including solutions to Sewell’s, Hasson’s, DeVore and Yu’s, Dzyadyk and Korneichuk’s problems. His recent works (2011–2019) emphasize uniform/pointwise SPA and invalidity of certain Jackson-type estimates. Scientific Awards: Shevchuk has received prestigious grants such as the Soros Grants, France-Ukraine 'Dnipro' Grant, and numerous travel grants from international institutions. Grants: His research has been supported by the NRFU (National Research Foundation of Ukraine) grant for collaborative work on constrained/unconstrained polynomial approximation. He has also secured international grants for global academic collaboration.

• Computer Modeling
• Structural Dynamics
• Innovation Management
• +۲ مورد دیگر

Olena Volodymyrivna Korotunova is an Associate Professor at the Department of Applied Mathematics within the Mechanical Engineering Faculty of National University 'Zaporizhzhia Polytechnic'. She holds a Candidate of Technical Sciences degree (Ph.D. equivalent) from Zaporizhzhia National Technical University (2004), specializing in Mechanics of Deformable Solids. Her dissertation focused on the dynamics of structures with complex element interactions. Her research integrates applied mathematics with engineering and economics, spanning: Computer modeling of structural dynamics and non-stationary deformations Innovation assessment in industrial enterprises Cluster-based regional economic development strategies Elastic body mechanics and contact problem solutions Her publications emphasize mathematical modeling in engineering contexts (e.g., deformation analysis, structural integrity) and economic applications (e.g., innovation management, competitiveness). Recent works (2022-2024) show a trend toward sustainable technologies and computational economics. Awards: Certificate of Merit (2009) from Zhovtneva District Administration for contributions to education and research She teaches courses in Higher Mathematics, Probability Theory, Optimization Methods, and Economic-Mathematical Modeling. With 50+ publications, she collaborates internationally (e.g., Malaysia, Bulgaria, France) and maintains active researcher profiles (ORCID, Scopus, Google Scholar).

• Computer Science
• Numerical Algorithms
• Cryptography
• +۳ مورد دیگر

Dr. Joanna Kapusta is an Assistant Professor in the Department of Applied Computer Science at the John Paul II Catholic University of Lublin, Poland. She is affiliated with the Faculty of Natural and Technical Sciences, where she conducts research in computational mathematics and cryptography. Her work primarily focuses on the design and optimization of algorithms for polynomial interpolation, numerical transformations, and cryptographic protocols. Her research interests span several key areas: Numerical Algorithms : Development of efficient methods for polynomial evaluation, interpolation, and transformation. Cryptography : Designing secure systems for secret sharing, e-voting, and encryption. Computational Mathematics : Applications in knot theory, semigroup evaluation, and matrix operations. Her publications demonstrate a consistent focus on optimizing computational efficiency in mathematical operations, with applications ranging from cryptographic security to educational tools. Articles frequently involve collaborative work, particularly with co-author Ryszard Smarzewski. No awards, students, or grant activities are mentioned in the provided materials.

• Computer Science
• Applied Mathematics
• Numerical Analysis
• +۲ مورد دیگر

Magdalena Wilkołazka is an Assistant Professor in the Department of Artificial Intelligence at the John Paul II Catholic University of Lublin, affiliated with the Faculty of Natural Sciences and Technology. Her research focuses on computational geometry and numerical methods. Specializes in interpolation techniques for curve modeling Active in academic publishing since 2015 Participant in science communication initiatives Research interests center on: Exponential parameterization algorithms Spline interpolation methods Trajectory estimation Curve length estimation Recent publications (2015-2019) demonstrate expertise in modifying classical interpolation schemes for improved geometric approximations. Scientific achievements include: National Science Centre project management Participation in international conferences (CISIM 2015, ICNAAM 2016, ICNAAM 2015) Collaboration with researchers like Ryszard Kozera and Lyle Noakes She contributes to academic community through peer-reviewed publications and science outreach events.

• Numerical Analysis
• Multivariate Approximation Theory
• Polynomial Interpolation
• +۵ مورد دیگر

Marco Vianello is a Full Professor of Numerical Analysis at the Department of Mathematics of the University of Padova, Italy. He has held this position since May 2017, following his tenure as Associate Professor (2001-2016) and Lecturer (1993-2000) at the same institution. Professor Vianello is Co-Editor-in-Chief of the Journal of Approximation Software (2024-) and was previously Managing Editor of Dolomites Research Notes on Approximation (2008-2022). He is the co-founder of the CAA: Padova-Verona Research Group on Constructive Approximation and Applications and an active member of the national research networks RITA (Rete ITaliana di Approssimazione) and SIMAI ANA&A Activity Group. Professor Vianello's research focuses on Multivariate Approximation Theory, with particular emphasis on Polynomial Interpolation, Numerical Quadrature and Cubature, Meshless Approximation, and Polynomial Optimization. His work includes the development of special point sets such as Padua Points, Approximate Fekete Points, Subperiodic Trigonometric Points, and CATCH (Caratheodory-Tchakaloff) Points. His research has significant applications in numerical methods for PDEs, computational mechanics, and scientific computing. His research has been recognized with the Optimization Letters Best Paper Award (2020), and his work has accumulated approximately 3,700 citations on Google Scholar. His algorithms have been implemented in several major software packages including ZEMAX OpticStudio, CP2K for molecular dynamics, and the Chebfun package. Optimization Letters Best Paper Award (2020) for 'Quadrature Based Polynomial Optimization' Approximately 3,700 citations on Google Scholar Approximately 1,200 citations by about 700 authors on MathSciNet Implementation of algorithms in ZEMAX OpticStudio, CP2K, Chebfun, and other scientific software Professor Vianello has advised 3 Ph.D. students to completion and approximately 60 B.Sc. and Master's dissertations. As a research coordinator from 2003-2025, he has secured approximately 150 kEuros in funding, supporting an average of 5 researchers per year. He has served on numerous hiring and evaluation committees, including the ASN (Italian National Scientific Habilitation) Committee in Numerical Analysis (2024-26) and hiring committees for professorships at Padova and Calabria Universities. His research group, the CAA: Padova-Verona Research Group on Constructive Approximation and Applications, maintains an active international collaboration network with researchers from institutions including Lawrence Livermore National Laboratory, Jagellonian University, University of Calgary/Verona, University of Toulouse, Colorado School of Mines, ANU Canberra, Indiana University, and several others across Europe, North America, and Asia.

Gregor Gantnerمشاهده پروفایل

استاد

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +۴ مورد دیگر

Gregor Gantner is a Professor for Mathematics at the University of Bonn (Bonn Junior Fellow) since November 2023. Previously, he held a tenured Inria Starting Faculty Position in Paris (2022-2023) and postdoctoral roles at TU Wien, the University of Amsterdam, and TU Wien again. He completed his PhD in Technical Mathematics at TU Wien in 2017, supervised by Dirk Praetorius, and received a Diploma in Technical Mathematics from TU Wien in 2014. His research focuses on numerical methods for partial differential equations, including finite element methods (FEM), boundary element methods (BEM), isogeometric analysis (IGA), and adaptive algorithms for optimal convergence. Key areas include space-time methods, a posteriori error analysis, and adaptive mesh-refining strategies. Notable awards include the 2019 Austrian Mathematical Society Dissertation Prize, Promotio sub auspiciis Praesidentis rei publicae (2018), and the Dr.-Klaus-Körper Prize (2018). He has led projects such as the French-German ANR-DFG project on robust adaptivity for nonlinear PDEs and the FWF Erwin Schrödinger Fellowship on optimal adaptivity for space-time methods. Teaching activities include advanced courses on numerical analysis and scientific computing, such as 'Optimality of Adaptive Finite Element Methods' (2025) and 'Boundary Integral Equations and BEM' (2024). His software contributions include the IGABEM2D package.

Kailai Liمشاهده پروفایل

استادیار

• Robotic Perception
• Sensor Fusion
• State Estimation
• +۲ مورد دیگر

Kailai Li is a tenure-track Assistant Professor at the University of Groningen's Bernoulli Institute, where he leads the Agile Sensing and Intelligence Group (ASIG). His research develops novel methods for robotic perception, including continuous-time state estimation, sensor fusion, and visual navigation. Recent publications focus on Gaussian process representations for motion estimation and multi-robot collaboration using vision-language models. Dr. Li's lab maintains open-source projects like LiLi-OM (LiDAR-inertial odometry) and SFUISE (UWB-inertial fusion). Collaborations include Linköping University and industry partners. Current projects investigate trustworthy perception for autonomous systems under uncertainty and efficient representations for high-dimensional state estimation.

Lisa Kreusserمشاهده پروفایل

پژوهشگر

• Partial Differential Equations
• Machine Learning
• Climate Modeling
• +۳ مورد دیگر

Lisa Kreusser is a researcher affiliated with the University of Bath, holding roles in the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa), the Centre for Climate Adaptation & Environment Research (CAER), and the Institute for Mathematical Innovation (IMI). Her research focuses on the intersection of partial differential equations, data analysis, and mathematical formulations for machine learning, with applications in climate modeling, biology, engineering, and industry. She actively leads and collaborates on projects addressing challenges such as cloud parameterization in atmospheric models, diabetic foot ulcer care through digital health technologies, and neural network-based solutions for complex systems. Her academic contributions span over 20 research outputs, emphasizing interdisciplinary approaches to nonlinear problems. Key areas include information propagation on graphs, stochastic differential equations in machine learning, and anisotropic diffusion models for pattern formation. Kreusser has secured funding for eight projects, demonstrating expertise in both theoretical and applied research. She is open to supervising doctoral students across topics in machine learning, climate science, and biological systems. Education: Advanced degrees in applied mathematics and computational science (specific details not provided). Grants: Includes projects on computational collective decision-making, weather prediction models using machine learning, and diabetic foot ulcer care technologies. Labs/Teams: Collaborates within multi-institutional teams, such as the Institute of Mathematical Innovation and CAER, driving interdisciplinary research initiatives.

Yubin Yanمشاهده پروفایل

دانشیار

• Numerical Analysis
• Stochastic Differential Equations
• Fractional Calculus
• +۵ مورد دیگر

Dr. Yubin Yan is an Associate Professor in the Department of Computer and Engineering Sciences at the University of Chester, UK. He leads the BSc Mathematics program and previously served as Programme Leader for the MSc Mathematics (2012–2019). Recognized as 'Most Inspiring Lecturer' (2016), he teaches advanced mathematical topics including stochastic calculus, partial differential equations, and fractional calculus. His research focuses on numerical methods for stochastic/deterministic differential equations, finite element methods, and fractional calculus, with over 80 peer-reviewed publications. He holds editorial roles in five international journals and organized minisymposia at major conferences like the Biennial Numerical Analysis Conference (2019, 2023, 2025). Research Interests: Specializes in numerical analysis for stochastic and deterministic equations, finite difference/element methods, fractional calculus applications in science/engineering/finance. His groundbreaking 2005 framework for error estimation in stochastic parabolic equations remains a standard reference. Current trends in his work include high-order numerical methods for fractional PDEs, stochastic sub/superdiffusion, and multiplicative noise modeling. Editor: Numerical Applied Mathematics and four other journals Guest Editor: Special issues in Mathematics (2021) and Discrete Dynamics (2017) Grants: EPSRC (UK), NSFC (China), University of Chester Advising: Supervised 5 PhD students and 30 MSc dissertations in Applied/Computational Mathematics. Active in international academic service, including refereeing for over 30 journals and conference organization.

Don Hongمشاهده پروفایل

استاد

• Approximation Theory
• Splines and Wavelets
• Medical Data Analysis
• +۳ مورد دیگر

Dr. Don Hong is a Professor in the Department of Mathematical Sciences at Middle Tennessee State University (MTSU), serving as Actuarial Sciences Coordinator. He holds a Ph.D. in Applied Mathematics from Texas A&M University, with postdoctoral training at the University of Texas-Austin in Computational Mathematics. Prior to MTSU, he was a tenured full professor at East Tennessee State University and held Visiting Professor positions at Vanderbilt University's Mathematics and Biostatistics departments. His research focuses on Approximation Theory, Splines, Wavelets, Medical Data Analysis, and Computational Statistics. He has published two books and over 60 peer-reviewed articles. Notable awards include the 2009 MTSU Distinguished Faculty Research Award and the 2020 CAS University Award for MTSU's Actuarial Program. Dr. Hong has secured over $1M in research funding from agencies like NIH, NSF, and HHS. He supervises graduate and undergraduate research, mentoring three Ph.D. students and a Master’s student who won the 2011 Master’s Thesis Award. Dr. Hong actively contributes to academic service, serving on editorial boards of journals like Frontiers in Applied Mathematics and Statistics and reviewing for NSF, NIH, and international bodies. He organizes conferences and workshops, including events under NSF/NSA grants.

Yuliya Babenkoمشاهده پروفایل

استاد

• Numerical Analysis
• Approximation Theory
• Spline Theory
• +۴ مورد دیگر

Yuliya Babenko is a Full Professor of Mathematics at the Department of Mathematics, Kennesaw State University (KSU), within the College of Science and Mathematics. She specializes in Numerical Analysis, Approximation Theory, and Spline Theory. Her research focuses on optimal recovery of operators, functional inequalities, and computational geometry. Education : PhD in Mathematics, Vanderbilt University (2006) MA in Mathematics, Vanderbilt University (2003) MS in Mathematics, Dnepropetrovsk National University, Ukraine (2001) BS in Mathematics, Dnepropetrovsk National University, Ukraine (2000) Research Interests : Numerical Analysis, Approximation Theory, Spline Theory, Functional Inequalities, Optimization of Quadrature Formulas, and Computational Geometry. She co-organizes the Analysis and Applied Mathematics seminar and the Kennesaw Mountain Undergraduate Mathematics Conference. Publications : Her work spans topics like optimal recovery of integral operators, Hardy-Littlewood-Polya inequalities, and multivariate spline approximation. Recent contributions include studies on operator approximation in Banach spaces and Taikov-type inequalities. Awards : 2015-2016 CSM Distinguished Research Award 2014-2015 CSM Distinguished Teaching Award 2013 MAA Southeastern Section Distinguished Teaching Award Advising & Grants : Mentored undergraduate researchers like Najia Bacha. Active in promoting undergraduate mathematics through conferences and collaborative initiatives. Labs/Teams : Collaborates with researchers including Vladislav Babenko, Sergiy Borodachov, and Jean-Marie Mirebeau on interdisciplinary projects in approximation theory and computational mathematics.

Thomas Takacsمشاهده پروفایل

پژوهشگر

• Isogeometric Analysis
• Computational Mechanics
• Numerical Analysis
• +۳ مورد دیگر

Dr. Thomas Takacs is a Project Leader in the Geometry in Simulations research group at the RICAM (Research Institute for Symbolic Computation), part of the Austrian Academy of Sciences. His work focuses on advancing computational methods for solving partial differential equations, particularly through isogeometric analysis and spline-based numerical techniques . His research interests include smooth basis constructions, multi-patch domain coupling, and adaptive mesh refinement strategies. Key contributions involve developing C1-continuous spline spaces over planar mixed meshes and unstructured quadrilateral meshes, with applications to engineering problems like linear elasticity. He explores the integration of machine learning (e.g., CNNs) for optimizing quad meshing and employs artificial neural networks in adaptive optimization frameworks. His work bridges geometric modeling and numerical analysis, addressing challenges such as singularity treatment in isogeometric analysis and approximation properties over self-similar meshes. Dr. Takacs collaborates on multi-institutional projects, including the Geometry in Simulations group at RICAM. His publications span computational mechanics, numerical methods, and geometric algorithms, with a focus on advancing the theoretical foundations and practical implementations of isogeometric analysis.

Yuan Xuمشاهده پروفایل

استاد

• Analysis
• Approximation Theory
• Harmonic Analysis
• +۳ مورد دیگر

Yuan Xu is a Professor of Mathematics at the University of Oregon's Department of Mathematics, part of the College of Arts and Sciences. His research focuses on analysis, approximation theory, harmonic analysis, orthogonal polynomials, numerical analysis, and special functions, with an emphasis on higher-dimensional problems such as multivariate approximation and orthogonal polynomials of several variables. He has authored or co-authored multiple books and over 200 journal articles, including works on constructive approximation, Fourier analysis, and special functions. Notable contributions include advancements in orthogonal polynomials, cubature formulas, and approximation theory. Xu serves on the editorial boards of prestigious journals like Constructive Approximation , Journal of Approximation Theory , and Proceedings of the American Mathematical Society . His research spans theoretical developments and applications in numerical analysis, with a focus on geometric and algebraic structures in polynomial systems. Publications highlight contributions to multivariate orthogonal polynomials, Fourier analysis on non-standard domains, and approximation on spheres and balls. He has also edited memorial volumes for prominent mathematicians like Donald Newman and Ward Cheney.

John Ormerodمشاهده پروفایل

دانشیار

• Statistics
• Bayesian Inference
• Data Mining
• +۴ مورد دیگر

Associate Professor John Ormerod is affiliated with the Statistics Research Group at the School of Mathematics and Statistics , University of Sydney . His research focuses on advanced statistical methodologies, including Variational Bayes , Generalized Linear Mixed Models , Splines , and Missing Data analysis. Current research students: Rajan Shankar and Jackson Zhou Key research strengths: Precision and Digital Health , Data and Decisions , National Security His work spans computational statistics, bioinformatics, and applications in health and biological data. Recent publications emphasize scalable Bayesian inference, variable selection, and single-cell data analysis frameworks. Grants include multiple Australian Research Council (ARC) Discovery Projects (2021, 2017, 2012, 2010) supporting research in feature selection, network modeling, and computational efficiency. He has supervised projects on modern regularization techniques and fast expectation propagation , contributing to BMC Bioinformatics , Bioinformatics , and Journal of Computational and Graphical Statistics .

Maryke van der Waltمشاهده پروفایل

دانشیار

• Data Science
• Computational Harmonic Analysis
• Approximation Theory
• +۱ مورد دیگر

Maryke van der Walt is an Associate Professor of Mathematics in the Department of Mathematics and Computer Science at Westmont College. She joined Westmont in 2017 after serving as an assistant professor at Vanderbilt University. Her academic journey spans multiple continents, reflecting her South African origins and international educational background. Her educational background includes: PhD in Applied Mathematics (2015) from University of Missouri-St. Louis MSc in Mathematics (2012) from Stellenbosch University, South Africa BSc in Mathematical Sciences (2009) from Stellenbosch University, South Africa Professor van der Walt's research focuses on applying advanced mathematical techniques to real-world problems, particularly in medical applications. Her work bridges theoretical mathematics with practical implementation, with special emphasis on computational harmonic analysis, approximation theory, and spline theory. She develops mathematical models that have direct applications in signal processing and medical data analysis, creating solutions that can be implemented in real-time systems for healthcare monitoring and prediction. Her publication record demonstrates consistent contributions to applied mathematics, with recent work focusing on diabetic blood glucose prediction using deep learning approaches, real-time spline interpolation techniques, and advanced signal analysis methods. These publications appear in reputable journals including Frontiers in Applied Mathematics and Statistics, Applied Mathematical Sciences, and GEM - International Journal on Geomathematics. Professor van der Walt maintains an active teaching schedule at Westmont College, instructing courses ranging from Calculus and Linear Algebra to specialized topics like Introduction to Subdivision Methods. Her teaching experience spans multiple institutions including Vanderbilt University and the University of Missouri-St. Louis, covering both undergraduate and graduate level mathematics courses.