Multiresolution analysis Professors and Supervisors

Radek ValaView profile

Assistant Professor

Tomas Bata University in ZlinCzech

Radek Vala serves as an Assistant Professor at the Institute of Informatics and Artificial Intelligence within the Faculty of Applied Informatics at Tomas Bata University in Zlín. His academic career spans over a decade at the institution, progressing from assistant to his current position since 2015. His educational background includes a Doctoral degree (2009-2015), Master's degree (2007-2009), and Bachelor's degree (2004-2007) in Engineering Informatics from Tomas Bata University, preceded by secondary education at Gymnasium in Bruntál. Vala's research focuses on web information systems development , mobile technology , and cybersecurity , with significant contributions to Industry 4.0 applications and modern development frameworks. His work bridges theoretical research with practical implementation through numerous industry collaborations. His publication portfolio demonstrates expertise across evolutionary algorithms , wavelet transforms , and security systems , showing consistent focus on optimization techniques and cybersecurity applications from 2011-2014. Gold Winner in Horizon Interactive Awards (School and University Websites category) 2nd place in Czech Golden Semicolon competition (Best Websites and Online Applications) As principal investigator for Ministry of Industry and Trade Innovation Voucher projects, Vala has led development of information systems for Industry 4.0 monitoring, order management, and CRM applications. He previously served as Head of Web Development for utb.cz (2017-2018), where he mentored over 50 students to completion of their Ing. degrees. Vala remains actively involved in the Penetration Testing Laboratory and Mobile Technology Testing Laboratory (PTLab) since 2015, contributing to practical security testing and mobile technology research.

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Knut Martin MørkenView profile

Professor

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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Nurdal WatsujiView profile

Assistant Professor

Gaziantep UniversityTurkey

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

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Nira DynView profile

Professor

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 DavydovView profile

Professor

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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Vicente Alarcon-AquinoView profile

Professor

National Research Institute for Mathematics and Computer ScienceNetherlands

Dr. Vicente Alarcon-Aquino is a Professor in the Department of Computing, Electronics, and Mechatronics at Universidad de las Americas Puebla (UDLAP), Mexico. He received his Ph.D. and D.I.C. degrees in Electrical and Electronic Engineering from Imperial College London in 2003. He previously served as department head from October 2012 to June 2018 and spent a research stay at King's College London in 2017. Dr. Alarcon-Aquino is a Senior Member of IEEE, a Level I member of the Mexican National System of Researchers (SNI), and a Fellow of the Mexican Academy of Sciences. His educational background includes: Ph.D. and D.I.C. in Electrical and Electronic Engineering, Imperial College London, UK (2003) Dr. Alarcon-Aquino's research focuses on cybersecurity, network monitoring, anomaly detection, wavelet analysis, and machine learning. His work spans theoretical foundations to practical applications in network security, with significant contributions to intrusion detection systems, cryptographic techniques, and machine learning approaches for security applications. He has developed innovative methods combining wavelet transforms with neural networks for various security and signal processing applications. His scholarly contributions include over 180 research articles in refereed journals and conference proceedings, a book on MPLS networks, and numerous citations. As an editor, he serves as Associate Editor for IEEE Access Journal and as Academic & Section Editor for PeerJ Computer Science, focusing on Security & Privacy. Notable professional recognitions include: Senior Member of IEEE Mexican National System of Researchers (SNI Level I) Member of the Mexican Academy of Sciences Dr. Alarcon-Aquino has supervised over 70 theses, including 8 Ph.D. dissertations, 15 Master's theses, and more than 37 Bachelor's theses. His supervision spans topics including network intrusion detection, information security, encryption algorithms, wavelet-based signal processing, neural networks, EEG signal processing, and biometric cryptosystems. He has hosted international research students from institutions including the Polytechnic University of Madrid and Kiel University of Applied Sciences. His research group at UDLAP focuses on developing advanced security solutions for modern network environments, with particular emphasis on applying machine learning techniques to cybersecurity challenges. Current projects include blockchain-based security solutions, federated learning approaches for intrusion detection, and advanced anomaly detection systems for IoT environments.

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Johannes KrebsView profile

Professor

Catholic University of Eichstätt-IngolstadtGermany

Prof. Dr. Johannes Krebs holds the Chair of Mathematics - Statistics at the Faculty of Mathematics and Geography, Catholic University of Eichstätt-Ingolstadt since 2021. His academic service includes membership on the editorial board of Statistics , the tenure track board, examination committees for B.Sc. Mathematics and Data Science programs, and the faculty council. His research focuses on topological data analysis, persistent homology, and spatial statistics with applications in stochastic geometry. Funded by Deutsche Forschungsgemeinschaft (KR 4977/2-1 and KR 4977/1-1), his work bridges theoretical statistics with computational topology to develop novel methods for analyzing complex data structures. His methodological contributions span wavelet analysis, functional data, and bootstrap techniques for dependent data. Analysis of his recent publications reveals strong emphasis on topological inference (persistent homology, Betti numbers), spatial statistics, and wavelet methods across multiple high-impact journals including Bernoulli , Annals of Statistics , and Stochastic Processes and their Applications . His work demonstrates consistent innovation in applying topological concepts to statistical problems with theoretical guarantees. Academic service highlights include: Editorial Board Member, Statistics journal Member of Tenure Track Board Examination Committee for B.Sc. Mathematics & Data Science Faculty Council Member His research program is supported by significant DFG funding for projects on Topological Data Analysis (2021-2023) and Dynamic Objects on Random Fields (2017-2020), reflecting sustained contribution to advancing statistical methodology through topological frameworks. He maintains active collaborations with researchers at UC Davis, KU Leuven, and Heidelberg University.

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Wylie StrobergView profile

Assistant Professor

University of AlbertaCanada

Wylie Stroberg is an Assistant Professor in the Department of Mechanical Engineering within the Faculty of Engineering at the University of Alberta. He leads the Computational BioSystems Lab, where he conducts research at the interface between cell biology, physiology and engineering using a range of computational and theoretical techniques. His research interests span multiple areas including: Biomechanics and Biomedical Engineering Biomaterials Mechanics and Materials Composites and Polymers Nano and Micro Materials Computational Mechanics Systems Biology of Proteostasis and the Unfolded Protein Response Reactions in Crowded Cellular Compartments Wetting Phenomena of Nanoscale Structures Dr. Stroberg's research focuses on developing new quantitative techniques for studying complex biological systems across disparate length and time scales. His work has significant implications for understanding cellular physiology, protein homeostasis, and developing new therapeutic approaches for age-associated and protein folding diseases. He employs advanced computational methods including multiscale modeling, machine learning, and theoretical analysis to investigate biological phenomena from the molecular to cellular level. His research group has published extensively in mathematical biology and computational mechanics, with a consistent focus on enzyme kinetics, cellular stress responses, and computational modeling of biological processes. The publications demonstrate a strong interdisciplinary approach combining techniques from mechanical engineering, computational science, and molecular biology. Dr. Stroberg has received recognition for his work in mathematical biology and systems biology, with publications in journals such as the Journal of Theoretical Biology, Biophysical Journal, and Mathematical Biosciences. He currently advises several graduate students working on diverse projects including multiscale modeling of enzymatic nanosensors, protein-based modulation of endoplasmic reticulum shape, development of machine learning-based coarse grained models for high-entropy alloys, and simulation of high-entropy alloys for thermal-spray deposition. His lab has trained undergraduate and master's students who have gone on to further academic pursuits. The Computational BioSystems Lab maintains active collaborations with other researchers at the University of Alberta and beyond, working at the intersection of engineering, biology, and computational science.

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