جست‌وجوی استاد و پژوهشگر

Matthew Hirnمشاهده پروفایل

دانشیار

• computational mathematics
• harmonic analysis
• data science
• +۷ مورد دیگر

Matthew Hirn is a computational mathematician with expertise in harmonic analysis, data science, and machine learning. He was a tenured Associate Professor at Michigan State University (MSU), jointly appointed in the Department of Computational Mathematics, Science & Engineering and the Department of Mathematics. As the scientific leader of the ComplEx Data Analysis Research (CEDAR) Team at MSU, he developed innovative methods for analyzing complex data, including wavelet scattering transforms for atomistic systems and geometric deep learning techniques. He is currently a Senior Quantitative Researcher at Citadel LLC, contributing to algorithmic trading strategies. His research bridges theoretical mathematics and applied data science, with notable contributions to graph neural networks, scattering transforms, and privacy-preserving methods. His academic career included organizing conferences such as the SIAM Mathematics of Data Science mini-symposium on geometric learning and co-authoring influential software like the Kymatio library for wavelet scattering transforms. He advised PhD students Ryan LaRose, Xavier Brumwell, and Jieqian He, who have since pursued impactful roles in academia and industry. His work on 3D wavelet scattering networks for material science and geometric scattering on manifolds has advanced the intersection of mathematics and machine learning. Matthew’s research interests span signal processing, graph representation learning, and applications in bioinformatics and many-particle physics. He has published extensively in top journals and conferences, including the Journal of Chemical Physics and the Journal of Machine Learning Research.

Haye Hinrichsenمشاهده پروفایل

استاد

• Quantum Information Theory
• Exactly Solvable Systems
• Tensor Networks
• +۴ مورد دیگر

Prof. Haye Hinrichsen holds a C3 professorship at the University of Würzburg within the Chair of Theoretical Physics III . His academic journey includes roles as Acting Professor at the University of Wuppertal (2001), Senior Assistant at Duisburg-Essen (2000), and postdoctoral research at institutions like the Weizmann Institute of Science (1995–1997). He earned his PhD in 1993 from the University of Bonn under Prof. V. Rittenberg, focusing on quantum groups. His research interests span quantum information theory , exactly solvable systems , statistical physics far from equilibrium , reaction-diffusion processes , and non-equilibrium phase transitions . Notable achievements include the 2007 Award for Good Teaching and contributions to understanding entropy-based tuning systems in music. His recent work explores topics like causal set propagators in anti-de Sitter spacetime, eigenmodes on hyperbolic lattices, and renormalization techniques in quantum field theories. He has also applied physics principles to music acoustics, developing adaptive tuning schemes using entropy maximization. Key Research Themes: Quantum gravity, non-equilibrium dynamics, topological complexity, and interdisciplinary applications in music. Teaching: Courses include Theoretical Physics I, Computational Physics, and General Relativity. Lab/Affiliation: Chair of Theoretical Physics III at Hubland South, Building M1.

• Numerical Analysis
• Approximation Theory
• Multiresolution Analysis
• +۷ مورد دیگر

Francesc Arandiga Llau is a Professor in the Department of Mathematics at the Faculty of Mathematics, Universitat de València, Spain. He is affiliated with the ANIMS (Numerical Analysis, Images, Multiresolution and Simulation) research group, where he conducts research in applied mathematics with a focus on numerical methods and their applications. Education: PhD from Universitat de València (1992), thesis on operator approximation and spectral radius continuity, supervised by Dr. Vicent Caselles Costa. His research interests center on Numerical Analysis , Approximation Theory , and Multiresolution Methods , with significant contributions to WENO schemes , nonlinear interpolation , and image and signal compression . His work often bridges theoretical developments with practical implementations in computational mathematics and engineering. He has made notable advances in the stability, accuracy, and adaptability of reconstruction techniques for piecewise smooth and discontinuous functions. The analysis of his recent publications reveals a consistent focus on high-order numerical methods, particularly in the context of image processing and data compression . His work leverages multiresolution analysis , radial basis functions , and adaptive interpolation to improve accuracy and efficiency. Themes across his articles include monotonicity preservation, error control, and the design of nonlinear schemes that avoid spurious oscillations near discontinuities. There are no scientific awards explicitly mentioned in the provided text. Francesc Arandiga has extensive collaborative research, particularly with scholars such as Rosa Donat, Dionisio F. Yáñez, Pep Mulet, and Antonio Baeza. His work has been supported through various research projects, though specific grants are not detailed in the text. He has advised students, including those who have completed theses under his supervision, although a full list is not provided. He is a key member of the ANIMS research group, which focuses on Numerical Analysis, Images, Multiresolution, and Simulation. This team works on developing and analyzing advanced computational methods for scientific and engineering applications, particularly in the areas of data representation, image processing, and numerical solutions to differential equations.

Evgeniy Redekopمشاهده پروفایل

دانشیار

• Catalysis
• Chemistry
• Energy
• +۷ مورد دیگر

Evgeniy Redekop is an Associate Professor at the University of Oslo , Department of Chemistry. His research focuses on sustainability-relevant catalytic reactions, combining time-resolved perturbation-enhanced kinetics (TAP) , in situ spectroscopy (AP-XPS, IR, XAS) , and multiscale modeling to decode molecular mechanisms. His work spans University of Oslo collaborations, including projects like NanoReactor, TAPXPS, TOMOCAT, and intriCAT. Research interests include: Zeolite- and MOF-catalyzed alkene conversion Metal/oxide-catalyzed CO 2 hydrogenation Selective oxidation on bimetallic surfaces Dynamic processes in reaction/diffusion systems Next-generation materials characterization tools Recent publications highlight 2024 studies on CO 2 hydrogenation zeotypes and 2023 work on ZnZrOx catalyst volatility , alongside 2022 investigations into TAP-AP-XPS synergy for ALD-derived catalysts. His methodologies bridge transient kinetics and surface spectroscopy , advancing understanding of material stability and catalytic efficiency . Contact: evgeniyr@smn.uio.no | Office: ØU40, Department of Chemistry, Sem Sælands vei 26, Oslo

Eran Treisterمشاهده پروفایل

استادیار

• Computational Science
• Scientific Computing
• Numerical Methods
• +۸ مورد دیگر

Eran Treister is an Assistant Professor at the Ben Gurion University of the Negev in the Department of Computer Science. He completed his postdoctoral fellowship at the University of British Columbia (2014-2016) and earned his PhD from the Technion in 2014 under Prof. Irad Yavneh. His research spans computational science, numerical methods, and machine learning, with a focus on: Scalable algorithms for inverse problems Graph Neural Networks (GNNs) optimization Seismic and optical imaging via PDE solvers Low-precision deep learning acceleration Multilevel preconditioning techniques Recent work explores: Graph neural networks for PDEs with adaptive meshes Deep learning approaches to Helmholtz equation modeling 3D shape reconstruction via parametric level sets He serves on editorial boards: SIAM Journal on Scientific Computing (2024-) Copper Mountain Conference on Multigrid Methods (2025) International Conference on Machine Learning (ICML) as Area Chair (2025) Current teaching: Optimization Methods for Data Science (Spring 2025) Deep Learning Mini-Project (Winter 2024/5) Advanced Numerical Optimization (Spring 2025)

Bhabani Shankar Mallikمشاهده پروفایل

استاد

• Computational Chemistry
• Molecular Dynamics
• Statistical Mechanics
• +۸ مورد دیگر

Bhabani Shankar Mallik is a Professor in the Department of Chemistry at the Indian Institute of Technology Hyderabad . His research focuses on Computational Chemistry , Molecular Dynamics , and First Principle Calculations for energy materials and catalysis. He leads the BSM Lab , which utilizes High-Performance Computing (HPC) resources like ParamSeva@IITH (838 TFLOPS, 7500 cores). Research Areas : Structure/Dynamics of Ionic Liquids, Catalysis (Homogeneous/Heterogeneous), Energy Materials, Microkinetic Theory, Machine Learning in Chemistry, Vibrational Spectroscopy His recent publications analyze ionic transport mechanisms in solid-state electrolytes, electrocatalytic processes for nitrogen reduction, and proton transfer dynamics in aqueous systems. He teaches courses like Modern Simulation Methods and Principles of Quantum Chemistry .

Peter Oswaldمشاهده پروفایل

استاد

• Approximation Theory
• Function Spaces
• Applied Harmonic Analysis
• +۷ مورد دیگر

Peter Oswald is a Professor and holds a Bonn Research Chair at the Hausdorff Center for Mathematics, affiliated with the Institute for Numerical Simulation at the University of Bonn. His research lies at the intersection of approximation theory, function spaces, and numerical methods for partial differential equations, with a focus on wavelets, splines, and multiscale computational techniques. University: University of Bonn School: Hausdorff Center for Mathematics Department: Institute for Numerical Simulation Email: oswald@ins.uni-bonn.de His research interests include Approximation Theory, Function Spaces and Applied Harmonic Analysis, Multiscale Methods in Scientific Computing, Numerical Methods for PDEs, Finite Elements, Splines, Wavelets, and Mathematical Modelling. These areas reflect a deep commitment to both theoretical foundations and computational applications in modern applied mathematics. The analysis of his recent publications reveals a consistent focus on iterative and subspace correction methods, preconditioning in sparse grid contexts, function space theory (especially Besov and Hilbert spaces), and the mathematical underpinnings of finite element and wavelet-based discretizations. His work often bridges pure and computational mathematics, with increasing exploration of stochastic and randomized algorithms in numerical linear algebra and high-dimensional problems. Peter Oswald has not been mentioned as having formal advisees in the provided texts, and no scientific awards are listed. However, his extensive collaboration with Michael Griebel and publication in prestigious journals and book series (e.g., Springer Series in Computational Mathematics) underscores his active and influential role in the mathematical community. He is involved in advanced research on space splittings, iterative solvers, and high-dimensional function approximation, contributing to both theoretical developments and practical computational frameworks. His work supports broader efforts in scientific computing, uncertainty quantification, and the numerical solution of complex physical models.

Yongjia Xuمشاهده پروفایل

پژوهشگر ارشد

• Optical Surface Metrology
• Structured Light Techniques
• Phase Measuring Deflectometry
• +۴ مورد دیگر

Dr. Yongjia Xu is a Research Fellow at the Department of Engineering, School of Computing and Engineering, University of Huddersfield. He actively contributes to precision metrology through structured light technologies and serves as a core researcher for two EPSRC projects: The EPSRC Future Advanced Metrology Hub (EP/P006930/1) and A Multiscale Digital Twin-Driven Smart Manufacturing System (EP/T024844/1). PhD in Mechanical Engineering (University of Huddersfield, 2019) MSc in Instrument Science and Technology (Hebei University of Technology, 2014) BEng in Control Technology and Instrument (Hebei University of Technology, 2011) His research spans optical surface metrology , fringe projection , phase measuring deflectometry , and 3D optical measurement systems . Key expertise includes calibration algorithms , error compensation , and portable metrology for industrial applications. Recent publications highlight advancements in deep-learning-enabled 3D measurement , refraction error modeling , and digital twin frameworks for smart manufacturing. His work has been cited over 409 times (Scopus h-index: 12) and 375 times (Google Scholar h-index: 11). Awarded the 2019 Chinese Government Award for Outstanding Self-financed Students Abroad, he also serves as a peer reviewer for journals like Optics Express and PhotoniX . He is a member of Optica and SPIE.

Jun Yuمشاهده پروفایل

استاد

• Statistical Learning
• Spatiotemporal Data Analysis
• Compressive Sensing
• +۷ مورد دیگر

Jun Yu is a Full Professor in Mathematical Statistics at the Department of Mathematics and Mathematical Statistics, Umeå University, Sweden, where he also serves as Director of Doctoral Studies. His research focuses on Statistical Learning and Inference for Spatiotemporal Data, with applications in artificial intelligence and various scientific domains. Professor Yu leads a research group dedicated to tackling theoretical data science problems and developing statistical learning methods for solving real-world challenges across multiple disciplines. Professor Yu's primary research interests include statistical learning with sparsity, compressive sensing, mathematics of data science, hierarchical spatiotemporal modeling, nonparametric density/intensity estimation, statistical inference for hidden Markov models, and wavelet theory applied to signal and image analysis. His work spans numerous application areas including atmospheric icing, automobile industry, biomedical engineering, climate research, epidemiology, forestry, geochemistry, hydrology, radiation oncology, spatial ecology, sports science, and transportation. Professor Yu's recent publications demonstrate a strong trend toward interdisciplinary research at the intersection of statistical methodology and environmental science, medical imaging, and transportation systems. His work on tree-ring isotope analysis for climate reconstruction, compressive sensing for medical imaging, and statistical models for train delay prediction shows his ability to develop sophisticated statistical methods that address complex real-world problems across diverse domains. As Director of Doctoral Studies, Professor Yu oversees doctoral education in Mathematical Statistics and has supervised numerous PhD students. His teaching spans mathematical statistics at all levels, from basic education to postgraduate courses, for students in mathematics, statistics, biology, engineering, and forestry, delivered in English, Swedish, or Chinese. Professor Yu leads the research group on statistical learning and inference for spatiotemporal data at Umeå University. This group develops innovative approaches for analyzing complex spatiotemporal datasets using tools such as intelligent data sampling, large-scale environmental data modeling, multimodal image processing, and tree growth models, with applications across multiple scientific fields.

Euan McGonigleمشاهده پروفایل

مدرس

• Nonstationary time series analysis
• Change point detection
• Multiscale methods in statistics
• +۳ مورد دیگر

Euan McGonigle is a Lecturer in Statistics at the University of Southampton, affiliated with the Department of Mathematical Sciences and the Statistical Sciences Research Institute (S3RI). He holds a PhD in Statistics and Operational Research from Lancaster University (2020), an MRes in the same field (2017), and an MSci in Mathematics from the University of Glasgow (2016). His research focuses on nonstationary time series analysis, change point detection, and multiscale statistical methods, particularly involving wavelet-based techniques for analyzing complex temporal data. His work emphasizes developing robust methodologies for segmenting time series data, analyzing locally stationary processes, and addressing nonstationarity in statistical models. He has contributed to software packages like TrendLSW and CptNonPar, which implement his research into practical tools for data analysis. McGonigle currently supervises PhD students in Mathematical Sciences and is accepting new PhD applications. Key areas of application include signal processing, financial time series, and environmental data analysis. His recent publications highlight advancements in wavelet-based trend estimation, multiscale variance analysis, and nonparametric change point detection techniques for multivariate settings.

• Materials Physics
• Porous Media
• X-ray Tomography
• +۲ مورد دیگر

Dr. Mahyar Madadi is a Research Fellow at the Australian National University , affiliated with the Materials Physics department. His work focuses on computational analysis of porous and granular materials using advanced imaging techniques like X-ray tomography. Research Interests Dr. Madadi's research spans multiple disciplines including Materials Physics , Geophysics , and Computational Modeling . Key areas of investigation include pore-scale fluid dynamics, elastic properties of granular media, and microstructural analysis of carbonates and foams. Publications His work demonstrates strong expertise in digital rock physics and computational materials science , with significant contributions to understanding relationships between microstructure and macroscopic properties in diverse systems from geological formations to synthetic foams.

Paul Richmondمشاهده پروفایل

استاد

• GPU Computing
• Agent-Based Modelling
• High-Performance Computing
• +۵ مورد دیگر

Paul Richmond is a Professor of Research Software Engineering at the University of Sheffield's School of Computer Science. He holds an EPSRC Early Career Research Software Engineering Fellowship focused on accelerating scientific discovery through GPU-accelerated architectures. With a strong interdisciplinary research track record, he develops software solutions for complex systems simulation using high-performance computing architectures. His research spans GPU computing, agent-based modeling, complex systems simulation, and high-performance computing methodologies. Richmond leads the development of FLAME GPU, a framework enabling large-scale agent-based simulations on GPUs, applied to domains including computational biology, transportation systems, and neural simulations. Analysis of his recent publications reveals strong emphasis on GPU-accelerated scientific computing, computational biology applications (particularly oncology and neuroblastoma modeling), transportation simulations, and parallel algorithm development. His work consistently bridges theoretical computer science with practical engineering applications. EPSRC Early Career Research Software Engineering Fellowship Fellow of the Higher Education Academy Richmond has secured substantial research funding including £4.3M as PI/CI over five years. Major grants include leadership roles in PRIMAGE (childhood cancer diagnostics), ExaTEPP (particle physics), and industrial collaborations with Fujitsu, Siemens, and Department for Transport. He founded and led the University of Sheffield's Research Software Engineering group, growing it to 13.5 FTEs supporting £13M in research projects. He established the GPUComputing@Sheffield group, leads the FLAME GPU development team, and served as Engineering Lead for Cambridge's Institute of Computing for Climate Science. Richmond is also former President of the Society for Research Software Engineering.

• Stochastic Analysis
• Partial Differential Equations
• Numerical Analysis
• +۳ مورد دیگر

Dr. Petru Cioica-Licht is an Assistant Professor (Akademischer Rat) at the Institut für Mathematik, Universität Kassel, specializing in Stochastic Analysis and Applied Mathematics. His research focuses on stochastic partial differential equations (SPDEs), regularity theory in functional spaces (Sobolev/Besov), and numerical methods for high-dimensional problems. He holds a PhD from Philipps-Universität Marburg (2010-2013) and has held positions at the University of Otago (New Zealand) and the University of Duisburg-Essen. His work bridges theoretical analysis and computational approaches, addressing challenges in stochastic integration, domain geometry effects, and adaptive numerical schemes. Professional History: Since 2021 – Academic Council Member (Akademischer Rat), Universität Kassel. 2019–2021 – Researcher in Stochastic Analysis at Universität Duisburg-Essen. 2017–2018 – Lecturer (Assistant Professor) at University of Otago. 2016–2017 – Postdoctoral Fellow funded by the Marsden Fund (New Zealand). 2014–2015 – Postdoc in DFG-funded project on SPDE regularity in quasi-Banach spaces. Research Interests: SPDEs on non-smooth domains, regularity theory in weighted spaces, numerical approximation of high-dimensional PDEs, and adaptive wavelet methods. His work emphasizes the interplay between stochastic processes and functional analytic techniques, particularly in analyzing solution regularity and developing efficient computational frameworks. Publications Trends: Recent articles address Sobolev/Besov regularity for SPDEs on angular domains, stochastic integration in quasi-Banach spaces, and overcoming the curse of dimensionality via deep neural networks. Earlier work includes Besov regularity for Navier-Stokes equations and convergence analysis of adaptive Rothe methods. Grants & Funding: Recipient of DFG and Marsden Fund support for projects on SPDE regularity and stochastic fluctuations. Active contributor to collaborative research in numerical analysis and stochastic processes. Labs/Teams: Part of the Analysis and Applied Mathematics group in Stochastics at Universität Kassel, focusing on interdisciplinary applications of stochastic and functional analytic methods.

Martin Schäferمشاهده پروفایل

پژوهشگر

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

Dr. Martin Schäfer is a Researcher at the Chair of Applied Analysis within the Faculty of Mathematics at Chemnitz University of Technology. His research focuses on advanced mathematical frameworks such as α-molecules, coorbit space theory, and approximation methods in harmonic analysis. He earned his Diploma in Mathematics from the University of Bonn (2012) and completed his PhD at the Technical University of Berlin (2018) with a thesis on α-molecules. His work bridges pure and applied mathematics, addressing challenges in signal processing, image analysis, and high-dimensional approximation. Key research interests include approximation theory, Fourier analysis, function spaces, and the development of adaptive representation systems like shearlets and curvelets. Schäfer has contributed to conferences such as the International Conference on Multivariate Approximation (2016) and the GAMM Annual Meeting (2014), presenting on topics like phase retrieval and sparse recovery. His publications explore theoretical foundations of wavelet systems and their applications in optimal function recovery and sampling problems. Notable achievements include establishing new upper bounds for sampling numbers and developing subsampling techniques for finite frames. While no specific awards are listed, his prolific publication record in top journals like Applied and Computational Harmonic Analysis and Journal of Fourier Analysis and Applications highlights his academic impact. Schäfer collaborates internationally, evidenced by his participation in workshops in Germany, Canada, and the USA.

• Signal Processing
• Image Processing
• Radar Systems
• +۶ مورد دیگر

Corina Nafornita is a Professor at the Department of Communications, Politehnica University of Timisoara. She holds a B.Sc., M.Sc., and Ph.D. in Electronics and Telecommunications from Politehnica University of Timisoara (2003) and Technical University of Cluj-Napoca (2008). She received her Habilitation certificate in 2015. Her research focuses on signal/image processing, radar systems, watermarking, wavelets, and GNSS localization. She is an IEEE Senior Member since 2020 and has held leadership roles in IEEE committees, including Chair of Humanitarian Activities (2021-2022) and Secretary of the IEEE Joint Chapter COM/SP/IT Romania (2019–present). Dr. Nafornita has authored numerous publications, led significant grants (e.g., SAMVA, IMFUSING), and advised multiple PhD students. Her work includes advancements in synthetic aperture radiometry, automotive radar calibration, and SAR image enhancement. She is affiliated with the Intelligent Signal Processing Research Centre and holds roles in organizing international conferences like IEEE ISETC and EHB. Research Interests: Statistical signal processing, radar technology, image denoising, watermarking, wavelet-based methods, and GNSS navigation. Her work bridges theoretical signal processing with practical applications in automotive safety, remote sensing, and secure communication systems. Grants & Leadership: Directed grants such as SAMVA (2022–2024) for multi-copter VTOL aircrafts and IMFUSING (2016–2019) for GNSS localization. Served as Publication Chair for IEEE ISETC since 2010 and TPC member for top conferences like ICASSP and EUSIPCO. Active in IEEE standards committees, including Synthetic Aperture Radiometry (SAR) working groups. Awards: Multiple UEFISCDI awards for research excellence (2015–2021), including recognition for her habilitation and publications on hyperanalytic wavelet edge detection and GNSS localization. Recognized for contributions to signal processing theory and applications. Labs/Teams: Member of the Intelligent Signal Processing Research Centre at UPT, steering committee member, and former Board member of the Communications Department (2017–2020). Collaborates internationally via partnerships with institutions like ENSEIRB Bordeaux, Thales Alenia Space, and ESA.