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Theofanis SapatinasView profile

Professor

• Functional Data Analysis
• Functional Time Series Analysis
• Signal Detection and Goodness-of-Fit Checks in Ill-Positioned Inverse Problems
• +2 more

Theofanis Sapatinas is a Professor in the Department of Mathematics and Statistics at the University of Cyprus, within the School of Natural and Applied Sciences. He has maintained this position since 2010, following his progression from Assistant Professor (2001-2005) to Associate Professor (2005-2010) at the same institution. His educational background includes: BSc in Mathematics (1989) from the Department of Mathematics, University of Athens, Greece MSc in Statistics (1991) from the Department of Probability and Statistics, University of Sheffield, United Kingdom PhD in Statistics (1994) from the Department of Probability and Statistics, University of Sheffield, United Kingdom Professor Sapatinas has established himself as a leading researcher in several specialized areas of statistics. His primary research interests focus on Functional Data Analysis and Functional Time Series Analysis , where he has made significant contributions to methodological development and theoretical understanding. He has also conducted important work in Signal Detection and Goodness-of-Fit Checks in Ill-Positioned Inverse Problems , addressing challenging statistical problems where traditional methods fail. His research extends to Non-Parametric Regression techniques, particularly through wavelet-based approaches, and the theoretical investigation of Characteristics and Structural Properties of Probability Theoretical Distributions . Much of his work bridges theoretical statistics with practical applications, especially in signal processing and time series forecasting. Analysis of Professor Sapatinas' publication record reveals a consistent focus on functional data analysis, wavelet methods, and inverse problems. His research trajectory shows an evolution from foundational theoretical work on probability distributions to increasingly sophisticated applications in functional time series and signal detection. A notable pattern is his extensive collaboration with researchers across Europe, particularly in Greece, France, and the UK. His work frequently appears in top-tier statistics journals including Annals of Statistics, Biometrika, and Journal of the Royal Statistical Society. Over time, there's been a clear shift toward more applied problems while maintaining strong theoretical underpinnings, with recent work focusing on bootstrap methods for functional data and practical implementations in fields like power systems forecasting. Professor Sapatinas has held postdoctoral positions at the University of Exeter (1993-1996) and the University of Bristol (1996-1998), followed by a Lecturer position at the University of Kent at Canterbury (1998-2000) before joining the University of Cyprus. His research has been supported through various academic appointments and collaborations across European institutions, though specific grant information is not detailed in the available materials. As a professor, he has likely supervised numerous graduate students, though specific names are not provided in the current information.

• Numerical Linear Algebra
• Scientific Computing
• Image Reconstruction
• +5 more

Misha Kilmer is the William Walker Professor of Mathematics at Tufts University, with a secondary appointment in Computer Science. She also serves as Deputy Director of ICERM at Brown University and co-PI of Tufts TRIPODS Institute. Her research focuses on numerical linear algebra, scientific computing, and image reconstruction/restoration. Kilmer has held leadership roles, including Chair of Tufts' Mathematics Department (2013-2019) and has received prestigious recognition, including SIAM Fellowship (2019) and the Tufts Undergraduate Teaching Award (2001). Education: PhD in Applied Mathematics, University of Maryland (1997) MA in Mathematics, Wake Forest University (1994) BS in Mathematics, Wake Forest University (1992) Research Interests: Kilmer's work emphasizes tensor decompositions, iterative methods, and model reduction. She develops algorithms for high-dimensional data analysis, imaging applications, and inverse problems. Her techniques bridge computational mathematics and engineering challenges, with applications in biomedical imaging, environmental monitoring, and machine learning. Publications Trends: Recent work highlights advancements in tensor-based neural networks, dynamic image reconstruction, and efficient algorithms for large-scale data. Key contributions include randomized algorithms for tensor decompositions and compressed sensing techniques for multi-modal data. Awards: SIAM Fellow (2019) Tufts Undergraduate Initiative in Teaching Award (2001) Advising & Grants: Kilmer has secured funding from NSF, NIH, IARPA, DARPA, and IBM. She co-chairs major SIAM conferences and serves on editorial boards for journals like SIAM Review and La Matematica . Labs/Teams: Active in Tufts' Data Intensive Studies Center (DISC) and co-leads the TRIPODS Institute. Collaborates internationally on projects involving tensor analysis and computational science.

• Approximation Theory
• Numerical Analysis
• Partial Differential Equations
• +3 more

Ronald DeVore is the Walter E. Koss Professor and Distinguished Professor of Mathematics at Texas A&M University, affiliated with the Department of Mathematics within the College of Arts & Sciences. His research focuses on approximation theory, numerical analysis, partial differential equations (PDEs), and their intersections with machine learning and computational mathematics. He has contributed significantly to topics such as neural network approximation, high-dimensional problems, and data-driven computational methods. His recent work addresses challenges in solving PDEs with incomplete information, optimal learning algorithms, and the theoretical underpinnings of reduced basis methods. He has also explored the application of compressed sensing and sparse approximation in diverse contexts, including electron microscopy and climate modeling. His research emphasizes interdisciplinary approaches, combining mathematical rigor with computational innovation. DeVore’s contributions include foundational papers on greedy algorithms, model reduction techniques, and the theoretical analysis of deep learning architectures. He has held leadership roles in academic programs and collaborative research initiatives, fostering advancements in numerical methods and their practical implementation.

Tom HorrocksView profile

Academic

• Machine Learning
• Geostatistics
• Geoscientific Data Mining
• +3 more

Tom Horrocks is a Senior Research Fellow at the School of Earth and Oceans, University of Western Australia (UWA). He holds a dual degree in Engineering (Software) and Science (Physics/Applied Mathematics) from UWA, complemented by a research-focused doctoral program supported by the Robert and Maude Gledden Scholarship. His work bridges machine learning with geoscience challenges, focusing on predictive modeling, data mining, and software engineering solutions for resource exploration. Key research areas include machine learning applications in geophysics (e.g., neural networks for potential field modeling), hyperspectral imaging for mineral identification, and automated geological logging using FTIR spectra. His interdisciplinary approach often results in deployable software tools addressing real-world problems, such as the Rio Tinto Data Fusion project. Education : Bachelor of Engineering (Hons) in Software, UWA Bachelor of Science in Physics & Applied Mathematics, UWA Awards : Nick Rock Memorial Prize (2018) ASEG WA Branch Student Award (2015) UWA Vice Chancellor’s Impact & Innovation Award (2015) Current projects include the ChatGEO initiative for semantic document retrieval and collaboration with the Geodata Algorithms Team on predictive logging techniques. His research outputs span geostatistics, mineral exploration, and AI-driven geoscience solutions. Tom has contributed to major grants totaling over 5 years, including partnerships with industry leaders like Fortescue Metals Group and the Geological Survey of Western Australia. His work is disseminated through high-impact journals like Scientific Reports and Ore Geology Reviews , reflecting a strong focus on both theoretical innovation and practical application.

Clem KarlView profile

Professor

• Statistical Signal Processing
• Inverse Problems
• Biomedical Signal and Image Processing
• +7 more

William Clem Karl is a Professor and Chair of the Department of Electrical and Computer Engineering at Boston University, with a joint appointment in Biomedical Engineering. He holds a Ph.D. from MIT (1991) and has been a faculty member at BU since 1995. His research focuses on statistical signal and image processing, inverse problems, biomedical imaging, and synthetic aperture radar. He currently serves as Editor-in-Chief of the IEEE Transactions on Image Processing and is a member of multiple technical committees in signal processing and biomedical engineering. Prof. Karl’s educational background includes degrees from MIT: S.B., S.M., and E.E. in Electrical Engineering and Computer Science, followed by a Ph.D. in the same field. His work bridges theoretical advancements and practical applications in imaging technologies, including computational imaging, CT artifact reduction, and multi-sensor fusion. He leads research initiatives at the Center for Information and Systems Engineering, Neurophotonics Center, and other interdisciplinary groups at BU. His recent contributions include innovations in low-dose CT imaging, deep learning for medical image reconstruction, and radar systems for remote sensing. He has organized international workshops and conferences, emphasizing collaboration across engineering disciplines. Prof. Karl’s research has been supported by grants from the National Science Foundation, Department of Defense, and industry partnerships. He advises students in interdisciplinary projects at the intersection of signal processing, machine learning, and biomedical engineering.

• Inverse Problems
• Partial Differential Equations
• Applied Mathematics
• +3 more

David Johansson is a Researcher in the Department of Mathematics and Statistics at the University of Jyväskylä, affiliated with the Faculty of Mathematics and Science. He is part of the Inverse Problems research group, which focuses on mathematical theory and practical implementation of indirect measurements. His work spans theoretical and applied mathematics, with a focus on inverse problems, partial differential equations, and computational methods. Research interests include inverse problems for PDEs, corrosion detection via boundary condition analysis, and parameter identification in heterogeneous media. He has also contributed to network optimization, including energy-efficient virtual network function placement and robustness strategies in telecommunications. His earlier work includes contributions to coding theory using graph-based approaches and Galois fields. Publications highlight interdisciplinary applications of mathematics, bridging pure and applied domains. No scientific awards are explicitly mentioned, but his active research output reflects sustained contributions to these fields. His position involves collaborative research within the Inverse Problems group and academic outreach through publications.

Shubham JaiswalView profile

Researcher

• Mathematics
• Inverse Problems
• Applied Mathematics
• +1 more

Shubham Jaiswal is a Doctoral Researcher in the Inverse Problems group at the Department of Mathematics and Statistics, University of Jyväskylä, Finland. He focuses on the mathematical theory and practical implementation of inverse problems, a field central to analyzing indirect measurements. His current position is supported by the Finnish Inverse Problems Society, and he is affiliated with the Faculty of Mathematics and Science. Contact details include emails shubham.s.jaiswal@jyu.fi and s.jaiswal.271801@gmail.com , and phone numbers +917007336576 and +358415753498. Shubham's research bridges theoretical and applied mathematics, particularly in inverse problems, which involve reconstructing unknown parameters from indirect observations. His work has implications in fields requiring precise modeling of inaccessible systems, such as geophysics and medical imaging. Contact Information: Room MaD234, Department of Mathematics and Statistics, University of Jyväskylä, PO Box 35 (MaD), 40014 Jyväskylä, Finland.

Rishikesh YadavView profile

Researcher

• Approximation Theory
• Optimization Theory
• Functional Analysis
• +4 more

Rishikesh Yadav is a Postdoctoral Researcher at the Department of Mathematics and Mathematical Statistics, Umeå University, Sweden, since July 2024. Previously, he held a postdoctoral position at the Namur Institute for Complex Systems (naXys) and the Department of Mathematics, University of Namur, Belgium (2022–2024). He earned his Ph.D. in Approximation Theory from Sardar Vallabhbhai National Institute of Technology Surat, India, in 2021 under Prof. Vishnu Narayan Mishra and Prof. Ramakanta Meher. His research focuses on the intersection of Approximation Theory, Dynamical Systems, and Koopman Operator Theory. Current work emphasizes Optimization Theory, particularly developing Frank-Wolfe algorithms for convex optimization on measure spaces, with applications in homotopy approaches and stochastic frameworks. He has also explored compressed sensing and l1-norm minimization problems. Key contributions include approximation studies using Bernstein polynomials for Koopman operators and advancements in Szász-Mirakjan-type operators. His publications span journals like Communications in Nonlinear Science and Numerical Simulation (2025), Mathematical Methods in the Applied Sciences (2024), and the Georgian Mathematical Journal (2022). He received the Kempe Foundation Fellowship (2024–2026) for research on iterative algorithms in regularized optimization. Notable engagements include speaking at SIAM Conference on Applications of Dynamical Systems (2023) and presenting at the International Conference on Nonlinear Analysis and Applications (2022). He is actively involved in academic communities, such as the naXys Research Day (2022), and plans to attend the ACOTCA 2025 conference in France.

• Numerical analysis
• Machine learning
• Scientific computing
• +3 more

Dr. Dmitry Kabanov is a Researcher at the Institute for Analysis and Numerical Analysis, part of the Department of Mathematics and Computer Science at the University of Münster. His primary affiliations include membership in Mathematics Münster, a research cluster focusing on interdisciplinary mathematical challenges. His research expertise spans numerical analysis, machine learning, and scientific computing, with a focus on high-dimensional problems, fluid dynamics, and combustion processes. Key research interests include developing numerical methods for complex systems, applying machine learning to fluid dynamics, and analyzing detonation stability through computational techniques. His work bridges theoretical mathematics with practical applications in physics and engineering, leveraging advanced computational frameworks to address real-world phenomena. Publications highlight contributions to detonation stability analysis, neural network-based fluid flow modeling, and interdisciplinary projects like the MaRDI initiative for scientific computing interoperability. Collaborations involve international teams tackling challenges in computational physics, machine learning, and high-dimensional approximation methods. Dr. Kabanov’s research also emphasizes resolving parameters in elastic anisotropy and developing minimal hyperbolic systems for unstable shock waves. He is affiliated with the Applied Mathematics Münster group and contributes to the Institute’s advanced seminars and numerical analysis projects. His office is located at Orleans-Ring 10, and he can be reached via email at dmitry.kabanov@uni-muenster.de. Administrative support is provided by Ms. Silvia Wernke.

Nico SneeuwView profile

Professor

• Geodesy
• Satellite Gravimetry
• Hydrology
• +3 more

Prof. Dr.-Ing. Nico Sneeuw is a Professor and Vice Dean of the Geodetic Institute at the University of Stuttgart's Faculty of Aerospace Engineering and Geodesy. His research focuses on geodetic applications of satellite data, particularly in gravimetry, hydrology, and remote sensing. He leads projects like STREAM and HydroSat , advancing global water storage monitoring and runoff estimation. His work integrates satellite missions (GRACE, GRACE-FO, CryoSat-2) with advanced statistical and computational methods to address climate change impacts on water resources. Key projects include developing spatial downscaling techniques for GRACE data, improving satellite altimetry waveform processing, and quantifying flood dynamics using remote sensing. He has contributed to orbital optimization for future gravity missions and pioneered probabilistic drought characterization frameworks. His research bridges geophysics, hydrology, and computational methods, with applications in environmental monitoring and disaster assessment. Publications highlight innovations in inverse problem solving, gravitational modeling, and sensor data fusion. He collaborates internationally on initiatives like the ESA St3TART project and coordinates interdisciplinary teams for satellite-derived hydrological products.

Luca CosmoView profile

Associate Professor

• Computer Science
• Machine Learning
• Data Analytics
• +4 more

Luca Cosmo is an Associate Professor at the Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice. His research focuses on machine learning, graph theory, and their applications in pattern recognition, computer vision, and 3D shape analysis. He has contributed to advancements in graph neural networks, diffusion models for music generation, and spectral methods for shape analysis. Teaching Activities: Machine Learning (Master's and PhD programs) Computer Programming and Data Management Lab of Information Systems and Analytics Data Analytics for Business and Society Research Interests: Graph Neural Networks and their interpretability Music generation via diffusion models 3D shape analysis using spectral and quantum-inspired methods Applications in healthcare and molecular design Recent Work Trends: Recent publications emphasize graph-based learning, including novel architectures like GraFix and LESI-GNN, and interdisciplinary applications in music decoding from EEG signals and molecular design. His work bridges theoretical foundations (e.g., spectral geometry) with practical tools for analyzing complex data structures. Labs/Teams: Active in the D.A.I.S. department's research groups focused on informatics and statistical methodologies.

Xingjie LiView profile

Associate Professor

• Numerical Analysis
• Partial Differential Equations
• Mathematical Physics
• +3 more

Xingjie (Helen) Li is an Associate Professor in the Mathematics & Statistics Department at the University of North Carolina at Charlotte. Her research focuses on numerical analysis, partial differential equations, and mathematical physics with an emphasis on nonlocal models, multiscale methods, and computational mechanics. She develops advanced numerical schemes for stochastic systems and explores applications in materials science, image processing, and epidemiological modeling. Her work bridges theoretical analysis and practical computation, addressing challenges in coarse-graining stochastic dynamics, atomistic-to-continuum coupling, and probabilistic collocation methods for heterogeneous materials. Recent studies include innovative approaches to nonlocal diffusion problems, peridynamic fracture modeling, and data-driven time-stepping algorithms for ergodic systems. She has also applied mathematical techniques to analyze real-world scenarios such as pandemic dynamics in Singapore and Japan. Key contributions include energy-preserving finite difference schemes, feature-oriented imaging compression frameworks, and inference-based adaptive methods. Her research portfolio reflects interdisciplinary collaboration spanning computational physics, applied mathematics, and data science.

Elias VänskäView profile

Researcher

• Inverse Problems
• Environmental Monitoring
• Bayesian Methods
• +4 more

Elias Vänskä is a Doctoral Researcher at the Department of Technical Physics, Faculty of Science, Forestry and Technology, University of Eastern Finland. His work centers on developing advanced computational methods for environmental monitoring, particularly greenhouse gas emissions in agricultural settings. His research focuses on solving inverse problems through innovative integration of measurement technology and statistical modeling. Using open-path laser dispersion spectroscopy and Bayesian state-space frameworks, he develops tomographic methods to visualize and quantify spatiotemporal methane emissions. This approach allows for accurate assessment of GHG balances by incorporating spatial heterogeneity and measurement uncertainty. The most recent publications demonstrate a strong trend in applying computational tomography and probabilistic inference to environmental challenges, particularly in agriculture. His work bridges applied physics, environmental engineering, and data science to improve climate monitoring systems. Scientific Awards: Elias Vänskä advises no listed students and there is no public information regarding grants he has led. However, his involvement in the "Tomography in the Field" project suggests participation in externally funded research initiatives focused on sustainable agriculture and emission tracking. He is an active member of the Inverse Problems research group, which specializes in mathematical and computational methods for interpreting indirect measurements, particularly in environmental and industrial applications.

• Operations Research
• Queueing Theory
• Traffic Management
• +3 more

Professor Neil Walton is a Professor and Deputy Head of the Department of Operations Management, Strategy, Innovation, Information Systems, and Entrepreneurship at the Business School. His research primarily focuses on operations research, queueing theory, stochastic processes, and their applications in transportation systems, networking, and optimization. He has contributed significantly to understanding scheduling algorithms, traffic signal control, and decentralized systems. His work bridges theoretical foundations with practical applications in urban mobility and resource allocation. Research interests include advanced scheduling techniques for quantum switches, stochastic approximation methods, and optimization frameworks for vehicular networks. He has explored topics such as regret analysis in multi-arm bandits and the convergence of stochastic processes. His studies often address real-world challenges like traffic congestion and healthcare resource management through rigorous mathematical modeling. Publications highlight a consistent focus on optimizing systems under uncertainty, with notable contributions to network stability, adaptive control mechanisms, and reinforcement learning applications. His work on platooning in connected vehicle networks and decentralized signal control systems underscores a commitment to cutting-edge solutions for modern transportation challenges. Professor Walton has advised students such as Yao Shao, contributing to the training of future researchers in operations management and systems optimization.

• Applied Mathematics
• Nonlinear Dynamics
• Mathematical Physics
• +12 more

Dr Jonathan Deane is a Senior Lecturer in the School of Mathematics and Physics at the University of Surrey, affiliated with the Department of Mathematics and Statistics. His research lies at the intersection of applied mathematics, nonlinear dynamics, and mathematical physics, with strong applications in engineering and planetary science. Undergraduate Degree: Physics, Merton College, Oxford (1979–1982) PhD and Postdoctoral Research: Department of Electrical and Electronic Engineering, University of Surrey (1986–1994) Lecturer, University of Surrey (1994–2000) Senior Lecturer, Department of Mathematics and Statistics, University of Surrey (2000–present) Jonathan Deane's research interests span a wide range of applied mathematical topics, including Poincaré inequalities , nonlinear elasticity , chaos in power electronics , triboelectric energy harvesting , and celestial mechanics . His work often combines rigorous analytical methods with numerical simulations to model complex physical phenomena. He has made significant contributions to the understanding of energy minimization in geometric mappings, cavity formation in elastic materials, and the long-term behavior of dissipative dynamical systems. The 15 most recent publications demonstrate a consistent focus on mathematical analysis of physical systems , particularly in the areas of inequalities , variational problems , dynamical systems , and energy conversion devices . The keywords across these works reflect deep engagement with functional analysis, geometric modeling, and nonlinear dynamics. Subfields such as spin-orbit resonance, triboelectric nanogenerators, and hydrostatic pumps indicate a strong interdisciplinary orientation linking mathematics with physics and engineering. His scientific contributions have been published in high-impact journals including: Proceedings of the Royal Society A SIAM Journal on Mathematical Analysis Nonlinear Differential Equations and Applications (NoDEA) Journal of Dynamics and Differential Equations IEEE Transactions on Circuits and Systems Dr Deane has supervised and collaborated extensively, particularly with researchers such as Jonathan Bevan, Michele Bartuccelli, and Guido Gentile. His work on chaotic DC-DC converters, sigma-delta modulators, and parametric amplifiers highlights applications in power electronics and signal processing. He has also contributed to fundamental modeling in energy harvesting, providing a comprehensive framework for triboelectric nanogenerators beyond simple geometries. He is actively involved in research groups focusing on: Nonlinear dynamics and chaos Mathematical modeling of physical systems Energy harvesting technologies Planetary spin-orbit dynamics