Spline Approximation Professors and Supervisors

Jacek Jagodzińskiمشاهده پروفایل

مدرس

Wrocław University of Science and TechnologyPoland

Jacek Jagodziński is a Lecturer at the Department of Control Systems and Mechatronics within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. His office is located in building C-3, room 317A at ul. Janiszewskiego 11/17, 50-372 Wrocław, with office hours on Monday 7:00-8:00, Tuesday (even weeks) 12:45-15:00, and Wednesday (odd weeks) 10:45-13:00. His research spans Control Theory , Industrial Networks , Logistics , and Decision-Making Systems , with significant contributions to Lean Management methodologies including Kaizen and Total Quality Management. Early work focused on mathematical control systems (nilpotent approximations, Chen-Fliess-Sussmann equations), while recent publications demonstrate a strategic pivot toward applied logistics, fashion industry analytics, and sustainable transportation systems. Analysis of his 15 most recent publications reveals an evolving research trajectory: initial focus on theoretical control systems (2008-2014) transitioned to logistics decision theory (2014-2017), then expanded into interdisciplinary domains including fashion forecasting (2019-2022) and zero-emission transportation (2020-2024). Current work emphasizes practical applications of fractional-order systems in building automation and Padé approximations for industrial control. Professional Contributions: Active research in industrial network optimization and game-theoretic logistics applications Specialized expertise in B-Spline identification methods for cascade control systems Developed frameworks for forecasting fashion demand with cost-optimization models Contributed to EU-focused studies on zero-emission bus fleet implementation His methodological approach combines rigorous mathematical modeling with empirical validation in industrial settings, particularly within Polish manufacturing and logistics sectors. Recent work shows increasing engagement with sustainability challenges in urban transportation and circular economy systems.

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Işın Ererمشاهده پروفایل

استاد

Istanbul Technical UniversityTurkey

Işın Erer is a Professor at the Department of Electronics and Communication Engineering, Faculty of Electrical and Electronic Engineering, Istanbul Technical University (ITU). She is actively involved in research on radar signal processing, artificial intelligence, and image analysis, with a focus on ground-penetrating radar (GPR) and remote sensing applications. University: Istanbul Technical University School: Faculty of Electrical and Electronic Engineering Department: Department of Electronics and Communication Engineering Academic Rank: Professor Email: ierer@itu.edu.tr Her research interests span signal processing, radar systems, clutter removal, target detection, deep learning, vision transformers, U-Nets, and vital signs detection using stepped-frequency radar . She applies advanced machine learning techniques to enhance radar imaging and improve performance in challenging environments such as debris fields and outdoor conditions. The recent publication trends indicate a strong focus on integrating deep learning models (e.g., Vision Transformers, YOLOv5, U-Net) with radar signal processing for clutter removal, image restoration, and segmentation . Her work combines low-rank approximations, autoencoders, B-spline activation functions, and attention mechanisms to improve accuracy and robustness in GPR and remote sensing imagery. Applications include parcel boundary delineation, road segmentation, and life detection in search-and-rescue scenarios. She has received notable recognition for her academic mentorship: Best PhD Thesis Advisor in Telecommunications Engineering Program, 2018 She leads multiple active research projects funded by TÜBİTAK and ITU-BAP, focusing on real-time AI-based radar systems, through-wall vital sign detection, and clutter removal in GPR. She has supervised numerous graduate students, with 47 theses in progress or completed. Her research group works on both theoretical algorithm development and practical system implementation, bridging the gap between academia and real-world deployment. Current projects include developing integrated AI models for real-time GPR systems and ultra-wideband radar methods for behind-obstacle detection.

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Fatma Zürnacı Yetişمشاهده پروفایل

پژوهشگر

Istanbul Technical UniversityTurkey

Fatma Zürnacı Yetiş is a Researcher at the Department of Mathematics Engineering, College of Engineering at Istanbul Technical University. Her research focuses on non-polynomial methods, divided differences, generalized Taylor series, B-spline functions, hypergeometric series, and convergence analysis of numerical schemes for partial differential equations. She has contributed to advancing theoretical frameworks in applied mathematics and numerical analysis. Her work includes studies on operator splitting methods for equations like the Rosenau-Burgers and Benjamin-Bona-Mahony equations, emphasizing convergence and numerical stability. She also explores quantum Bernstein bases and their connections to hypergeometric series, as well as non-polynomial divided differences and their applications in approximation theory. Zürnacı Yetiş has led research projects such as 'New Proofs for Some Hypergeometric Series and Their q-Versions Using Divided Differences' and 'Non-polynomial Divided Differences and Blossoming', funded by Istanbul Technical University. Her contributions span peer-reviewed articles in journals like Mathematical Methods in the Applied Sciences and Filomat.

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Kirill Kopotunمشاهده پروفایل

استاد

University of ManitobaCanada

Kirill Kopotun is a Professor in the Department of Mathematics at the University of Manitoba , where he has held a faculty position since at least 1995. His research focuses on approximation theory , particularly in polynomial and spline approximation , moduli of smoothness , and convex/constrained approximation . He actively contributes to numerical analysis , linear algebra , and partial differential equations . Research Themes : Uniform/pointwise estimates for polynomial approximation, applications of Jacobi weights, shape-preserving approximation, k-monotone functions Collaborations : Extensive co-authorship with D. Leviatan , I. A. Shevchuk , and others in approximation theory. Publications span 2019–2015, emphasizing shape-preserving approximation , Jacobi-weighted approximation , and moduli of smoothness . His work appears in journals like Constructive Approximation , Journal of Approximation Theory , and Ukrainian Mathematical Journal . Contact : Office 422 Machray Hall, Kirill.Kopotun@umanitoba.ca

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Roland Langrockمشاهده پروفایل

استاد

Bielefeld UniversityGermany

Prof. Dr. Roland Langrock holds the Chair of Statistics and Data Analysis at the Faculty of Economics, University of Bielefeld . He is a spokesperson for the Center for Statistics and a subproject manager in the Transregio 212 NC³ collaboration. His research spans ecological statistics, sports analytics, and time series modeling. 2026–present: Principal investigator for "Data-based indication of fraud in live betting" (DFG) 2025–present: Subproject manager D06 in TRR 212 NC³ 2021–present: ERASMUS representative for Master of Statistical Sciences Research Interests: His work focuses on hidden Markov models for analyzing animal movement, sports performance, and commercial data. Key applications include marine predator behavior , football match dynamics , and fraud detection in betting . He develops flexible statistical frameworks for state-switching processes across domains. Scientific Awards: Multiple German Research Foundation grants (2017–2026) and participation in EU-funded projects. Notable publications in Journal of the Royal Statistical Society , Ecology Letters , and Science . Additional Roles: Member of the Bielefeld Graduate School in Theoretical Sciences, organizer of advanced statistical methods courses, and contributor to software packages like moveHMM . His collaborations extend to marine biology (blue whales), subterranean rodent studies, and retail demand forecasting.

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Knut Martin Mørkenمشاهده پروفایل

استاد

University of OsloNorway

Knut Martin Mørken is a Professor and Vice Dean for Education at the Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Oslo. His academic career spans both research and leadership in computational mathematics and science education reform. Roles: Vice Dean of Education (since 2019), Leader of the CCSE (Centre for Computing in Science Education), Former leader of the InterAct educational development project (2012–2019) Teaching: Co-instructor of MAT-INF1100 and INF-MAT5340 courses Research Interests: Approximation theory with focus on spline functions, B-splines, wavelets, and geometric modeling Integration of computational methods into mathematics and science education, spanning the CSE (Computers in Science Education) project and its successor, the CCSE (now a national Centre of Excellence in Education) Scientific Contributions: His work includes algorithm development for function approximation, stability analysis of wavelets on nonuniform triangulations, and interdisciplinary studies on mathematics-computing integration in education. Awards & Recognition: CCSE awarded Centre of Excellence in Education (SFU) by DIKU Collaborations: Active partnerships across departments and institutions, including collaborations with Lyche, Reif, Lockwood, Caballero, and Melvær in research and educational initiatives.

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Bahodir Siddikovمشاهده پروفایل

استاد

Ferris State UniversityUnited States

Bahodir Siddikov serves as Professor in the Department of Mathematics within Ferris State University's College of Arts, Sciences and Education. His academic foundation includes dual doctoral qualifications: a PhD in Mathematics from the University of Wisconsin and a PhD in Applied Mathematics from Kiev University, complemented by BS and MS degrees in Applied Mathematics from Kiev University. His educational credentials are detailed as follows: PhD in Mathematics, University of Wisconsin PhD in Applied Mathematics, Kiev University MS in Applied Mathematics, Kiev University BS in Applied Mathematics, Kiev University Siddikov's research centers on numerical methods for partial differential equations with engineering applications, particularly in magnetic refrigeration systems and hydroacoustic signal processing. His work bridges theoretical mathematics with thermal physics and fluid dynamics, evidenced by publications spanning computational simulations of regenerators, heat capacity modeling in magnetic materials, and inverse problem solutions for wave propagation. Analysis of his publication timeline reveals evolving research trajectories: early work (1989) addressed hydroacoustic signal inversion, while mid-career research (2002-2005) focused on magnetic regenerator simulations. His most recent publications (2010-2012) demonstrate advanced numerical techniques for singular perturbation problems and material property modeling, alongside pedagogical contributions to calculus education through computational software development. He maintains active scholarly engagement through presentations at major international forums including SIAM's 50th Anniversary Conference, International Conference on Mathematics in Istanbul, and IEOM conferences across Asia and Hawaii, covering topics from Mathematica-based numerical analysis instruction to magnetic refrigeration advancements.

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Nira Dynمشاهده پروفایل

استاد

University of Sorbonne Paris NordFrance

Nira Dyn is a Professor of Applied Mathematics at Tel Aviv University's School of Mathematics, where she has established herself as a leading researcher in geometric modeling and approximation theory. Her academic career spans decades of contributions to subdivision methods and computational mathematics, with a consistent focus on both theoretical foundations and practical applications in computer graphics and image processing. Research Interests Professor Dyn's primary research areas include Geometric Modeling , Subdivision methods , and Multivariate approximation theory , with significant contributions to Computer-Aided Geometric Design (CAGD) and Image Compression. Her current work centers on Nonlinear subdivision schemes and the Approximation of set-valued functions , representing cutting-edge extensions of classical approximation theory to handle complex geometric structures and uncertain data. These interests form a cohesive research program that bridges pure mathematical analysis with computational applications, particularly in handling geometric data through innovative subdivision techniques. Publication Trends Analysis of her recent publications reveals a strong emphasis on advancing subdivision methodologies beyond linear frameworks, with increasing focus on nonlinear schemes capable of handling complex geometries and set-valued data. Her work demonstrates consistent progression from foundational subdivision theory toward practical applications in image compression and geometric modeling, with notable contributions to metric-based approximation techniques. The publications showcase interdisciplinary reach spanning mathematics, computer science, and engineering applications, while maintaining rigorous mathematical foundations in approximation theory. Professional Activities While specific advising relationships and grant information aren't detailed in the available materials, Professor Dyn's extensive publication record in top-tier journals indicates active research leadership. Her collaborations span multiple institutions and disciplines, reflecting the interdisciplinary nature of modern geometric modeling research. The absence of explicit laboratory or team information suggests her work may be primarily theoretical or conducted through collaborative networks rather than a dedicated physical research space.

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Oleg Davydovمشاهده پروفایل

استاد

Justus-Liebig-University GiessenGermany

Professor Oleg Davydov holds a Professorship for Numerical Analysis at the Department of Mathematics, University of Giessen, Germany. His research focuses on developing advanced numerical methods with strong theoretical foundations and practical applications. He maintains an active research program with numerous recent publications and international collaborations. Position: Professor of Numerical Analysis Institution: University of Giessen, Department of Mathematics Contact: Heinrich-Buff-Ring 44, 35392 Giessen, HRZ Room 117 Email: oleg.davydov@math.uni-giessen.de Homepage: https://oleg-davydov.de/ Professor Davydov's research interests center around meshless numerical methods, approximation theory, and computational mathematics. His primary focus areas include: Meshless Finite Difference Method - Developing robust meshless techniques that avoid the need for structured grids Finite Element Method - Particularly Bernstein-Bézier finite elements and specialized approaches for complex geometries Scattered Data Fitting - Creating efficient algorithms for approximating data on irregular domains Approximation Theory - Investigating theoretical properties of splines, radial basis functions, and other approximation tools His research has resulted in several software packages including mFDlab (Meshless Finite Difference Method), BBFEM (Bernstein-Bézier Finite Elements), and TSFIT (Two-Stage Scattered Data Fitting), demonstrating the practical implementation of his theoretical work. Analysis of Professor Davydov's recent publications shows a consistent focus on improving meshless methods, particularly in stencil selection, error analysis, and applications to complex problems. His work spans both theoretical developments (like error bounds and optimal approximation orders) and practical implementations (for fluid dynamics, manifold learning, and interface problems). A notable trend is the increasing sophistication of adaptive techniques and the handling of challenging geometries. Professor Davydov has supervised several doctoral students to completion, including: Gaelle Andriamaro Fabien Rabarison Abid Saeed Wee Ping Yeo His research group appears to maintain active collaborations with institutions worldwide, as evidenced by his extensive co-authorship network. The group focuses on developing both theoretical foundations and practical implementations of numerical methods, with particular attention to problems involving irregular domains, singularities, and complex geometries. Students in his group would gain experience in both theoretical analysis and software development for numerical methods.

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Lili Yuمشاهده پروفایل

استاد

Georgia Southern UniversityUnited States

Lili Yu is the Karl E. Peace Endowed Chair of Biostatistics and Professor in the Department of Biostatistics, Epidemiology & Environmental Health Sciences at Georgia Southern University, where she has been faculty since 2007. Her academic appointments include serving as Principal Investigator for the Office of International Chinese Statistical Association (2007-2017) and as Co-Principal Investigator for current research projects including 'Hierarchical Bayes Regression Models for Geospatial Inquiries' at Georgia Southern University. Dr. Yu's educational background is highly interdisciplinary, with degrees spanning medicine and statistics: PhD in Biostatistics from Ohio State University (2007) MS in Statistics from Ohio State University (2004) MS in Neuroscience from Capital Medical University (2001) MD in Clinical Medicine from Tianjin Medical University (1995) Her research interests focus on biostatistical methodology development and application, particularly in survival analysis, categorical data analysis, and multivariate data analysis. Dr. Yu has made significant contributions to accelerated failure time models, Bayesian statistics, and spatiotemporal modeling. Her work often addresses important public health questions related to disease modeling, mortality risk factors, and health outcomes assessment. Analysis of her publication trends shows a consistent trajectory from methodological development in survival analysis to applied studies addressing significant health issues. Dr. Yu has received recognition for her scholarly contributions, with an h-index of 11 according to Scopus data, and 1,876 citations across her publications. Her work has appeared in numerous high-impact journals across biostatistics, epidemiology, and public health disciplines. Her research funding includes multiple successful grant awards, demonstrating her ability to secure competitive research support. Notable projects include 'Hierarchical Bayes Regression Models for Geospatial Inquiries' (2022-2023), 'Efficient statistical methods in AFT model for cancer data' (2009-2010), and leadership of the Office of International Chinese Statistical Association (2007-2017). Dr. Yu's collaborative network is extensive, with co-authors from multiple institutions across the United States and internationally. Her work bridges methodological statistics with practical applications in public health and medicine, contributing to United Nations Sustainable Development Goals related to health and well-being.

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Miriam Primbsمشاهده پروفایل

استاد

Hochschule Ruhr WestGermany

Professor Miriam Primbs is a faculty member at Hochschule Ruhr West (HRW) in the Institute of Natural Sciences, where she teaches Mathematics and Simulation. She has been a professor at HRW since 2011 and coordinates the HRW research focus MARTA since 2022. Professor Primbs studied Mathematics and Physics (Diplom and 1. Staatsexamen) and earned her PhD in Mathematics at the University of Duisburg-Essen. Her career includes a research stay at IMATI CNR in Italy, teaching on the school ship Thor Heyerdahl, and working in Research and Development at Siemens Energy Mülheim before joining HRW. This blend of academic and industry experience informs her teaching and research approach. Her research focuses on applying mathematical and numerical methods to technical problems across engineering disciplines, from mechanics to electrical engineering. As coordinator of the MARTA research focus, she leads various projects including those funded by BMBF and collaborations with industry partners like Siemens Energy. Her scholarly work spans wavelet analysis, numerical methods, signal processing, and power systems analysis. Professor Primbs teaches Engineering Mathematics I and II, Mathematics for E-Commerce, MATLAB, Scientific Simulation, and LaTeX Basics. She employs a flipped classroom approach for foundational courses, providing students with tailored materials and prompt feedback through weekly exercises. In higher semesters, she emphasizes project-based learning and specialized topics. Currently supervising doctoral student Martin Wachs in cooperation with University of Duisburg-Essen Member of the Senate Committee for Research and Transfer at HRW Encourages industry collaboration for practical research applications Professor Primbs maintains strong connections between academic research and industrial applications, leveraging her Siemens Energy experience to bridge theory and practice in mathematical engineering.

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Arshad Raza Syedمشاهده پروفایل

مدرس

University of Southern BrittanyFrance

Dr Arshad Raza Syed serves as a Lecturer in Data Analytics & Business Analytics within the Department of Global Business and Enterprise at Ulster University Business School, based at the Derry~Londonderry campus. His academic role focuses on delivering high-quality education in data-driven business decision making and analytical methodologies. Educational Background: PhD: Systemic Problems related to Multiple Stakeholders in Information Technology adoption and use in Organizations: A Systems Thinking Perspective, Edith Cowan University (2012) Master: Visualization with Spline using a Genetic Algorithm, King Fahd University of Petroleum and Minerals (2001) Bachelor: A Computer based Simulation of a Diesel Engine Combustion Process, NED University of Engineering and Technology, Karachi (1997) Dr Syed's research expertise encompasses Data Analytics , Business Analytics , Operations Research , Stochastic Processes , Software Engineering , and Complex Systems . His work addresses inherent complexities in organizational IT adoption and develops innovative approaches for managing stochastic failures in multistage processes. Recent publications (2024-2025) reveal a clear trajectory in algorithmic development for discrete approximation of continuous distributions and reliability engineering under uncertainty, highlighting his dual focus on theoretical rigor and practical business applications. No scientific awards or major grants are documented in the available profile information. There is no mention of specific laboratories or research teams led by Dr Syed in the provided materials.

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