جست‌وجوی استاد و پژوهشگر

Thomas Takacsمشاهده پروفایل

پژوهشگر

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

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

Yuan Xuمشاهده پروفایل

استاد

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

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

John Ormerodمشاهده پروفایل

دانشیار

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

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

• Harmonic Analysis
• Applied Mathematics
• Machine Learning
• +۴ مورد دیگر

Christina Frederick is an Associate Professor in the Department of Mathematical Sciences at New Jersey Institute of Technology. Her research spans computational mathematics, harmonic analysis, and machine learning applications in robotics and ocean acoustics. Active in time-frequency analysis and multiscale inverse problems Principal Investigator on NSF and Navy-funded projects Collaborates on seabed classification, intermittent diffusion algorithms, and sensor deployment Current work focuses on computational methods for autonomous systems and mathematical foundations of Riesz bases. She has contributed to robotics path planning and sonar imaging techniques. Her recent publications (2022-2024) demonstrate interdisciplinary integration of harmonic analysis with machine learning for applications in seabed classification, mobile sensor deployment, and robotics coordination. Key trends include time-frequency analysis, computational methods for autonomous systems, and Gaussian processes in acoustics. NSF Grant: Applied and computational time-frequency analysis (2023-2026) Navy Grant: Sensor deployment algorithms (2021-present) NSF Grant: Multiscale inverse problems in sonar imaging (2017-2020) Frederick's work bridges theoretical mathematics with practical engineering challenges, particularly in sensor network optimization and acoustic signal processing.

Volker Michelمشاهده پروفایل

استاد

• Geomathematics
• Inverse Problems
• Gravimetry
• +۴ مورد دیگر

Prof. Volker Michel is a Professor of Mathematics in Engineering at the Department of Mathematics, University of Siegen, leading the Geomathematics Group. He holds a PhD and habilitation from the University of Kaiserslautern. His research focuses on applying advanced mathematical techniques to geoscience problems, including inverse problems, gravimetry, and medical imaging. Michel has organized major conferences and secured numerous DFG grants. He serves as editor-in-chief of the International Journal on Geomathematics and editorial roles in other journals. Notable achievements include a prize for his PhD thesis and international collaborations with institutions like the Isaac Newton Institute and NASA's JPL. His academic leadership includes heading the Department of Mathematics at Siegen from 2014–2016. Education: Industrial Mathematics Diploma, University of Kaiserslautern (1995) PhD in Mathematics, University of Kaiserslautern (1999) Habilitation in Mathematics, University of Kaiserslautern (2002) Research Interests: Geomathematics, multiscale approximation methods, inverse problems in geosciences, medical imaging techniques, numerical analysis, and functional analysis applications. His work bridges theoretical mathematics with practical geophysical and engineering challenges, emphasizing data-driven solutions for Earth sciences. Publications: His books synthesize cutting-edge methodologies, such as Geomathematics (2022), which addresses mathematical modeling in geodesy and geophysics, and Lectures on Constructive Approximation (2013), detailing wavelet and spline techniques across domains. These works reflect his expertise in translating complex mathematical theories into applicable geoscientific frameworks. Awards and Recognition: PhD Prize from Kreissparkasse Kaiserslautern (1999) Editorial leadership in top-tier journals International collaboration recognitions Grants and Advising: Secured multiple DFG-funded projects and advised numerous theses. His research group actively engages in third-party funded initiatives focusing on geomathematical problem-solving. Labs/Teams: Founded and directs the Geomathematics Group at Siegen, a hub for interdisciplinary research in mathematical geosciences.

Aanjaneya Mridulمشاهده پروفایل

دانشیار

• Computer Graphics
• Scientific Computing
• Programming Languages
• +۳ مورد دیگر

Aanjaneya Mridul is an Associate Professor in the Department of Computer Science at Rutgers University, where he joined in 2017. He is also a member of the Computational Biomedicine Imaging and Modeling Center (CBIM). His research spans computer graphics, scientific computing, programming languages, and robotics, with a focus on developing numerical methods in computational physics that leverage modern computing power. Dr. Aanjaneya completed his doctoral studies in Computer Science from Stanford University in 2013 under Ronald Fedkiw, followed by postdoctoral studies at the University of Wisconsin-Madison with Eftychios Sifakis. He received his undergraduate degree in Computer Science and Engineering from the Indian Institute of Technology Kharagpur in 2008. His research interests include: Computer graphics and visual simulation Scientific computing and numerical methods Programming languages for computational physics Robotics and differentiable physics engines Computational physics for interdisciplinary applications Dr. Aanjaneya's recent work has focused on applying numerical methods to robotics for learning unknown physical parameters, and designing correctly-rounded implementations of elementary functions for new floating-point variants. His long-term goal is to enable next-generation algorithms that facilitate interdisciplinary collaboration with researchers in engineering and medicine. He has received several prestigious awards including the Ralph E. Powe Junior Faculty Enhancement Award (2019) and the NSF CAREER Award (2023). Ralph E. Powe Junior Faculty Enhancement Award (2019), sponsored by Oak Ridge Associated Universities (ORAU) NSF CAREER Award (2023) At Rutgers, Dr. Aanjaneya directs the Laboratory for Interactive Virtual Environments (LIVE), where he mentors PhD students and postdoctoral researchers. He has secured multiple NSF grants including the CAREER award, an NSF NRI grant with Professors Boularias and Yu, and grants with Professor Santosh Nagarakatte for projects on formally certified low-dimensional linear programs and correctly rounded math libraries. Outside of his academic work, Dr. Aanjaneya enjoys running, rock climbing, hiking, watching movies, and playing piano.

Jeffrey Hoganمشاهده پروفایل

دانشیار

• Harmonic Analysis
• Signal Processing
• Wavelet Analysis
• +۵ مورد دیگر

Jeffrey Hogan is an Associate Professor in the School of Information and Physical Sciences (Mathematics), University of Newcastle. He holds academic appointments including Head of Mathematics Discipline and former Deputy Head of the School of Mathematical and Physical Sciences. His research focuses on harmonic analysis, wavelet theory, and applications in signal processing, including bandlimited signals, prolate spheroidal wave functions, and quaternion-valued wavelets. Hogan earned his BSc (Hons) and PhD from the University of New South Wales, followed by postdoctoral roles at institutions like the University of Texas and University of Arkansas before joining Newcastle in 2009. Education: Bachelor of Science, University of New South Wales PhD (Mathematics), University of New South Wales Research Interests: Jeffrey Hogan explores pure, applied, and computational harmonic analysis, including singular integrals, wavelet frames, Clifford-Fourier theory, and applications in mineral fractionation. His work emphasizes spatio-spectral limiting, multidimensional wavelet construction, and quaternionic signal processing. Key projects involve developing algorithms for EEG analysis, optimizing wavelet design via projection methods, and analyzing signals on graphs and hypercubes. Grant Funding: A$546,913: Enhanced Fractionation of Mineral Particles According to Density (ARC Discovery Project, 2020–2022) A$593,621: Relaxed Reflection Methods for Feasibility and Matrix Completion Problems (ARC Discovery Project, 2016–2018) Collaborations & Teams: Hogan collaborates with experts like Prof. Kevin Galvin (University of Newcastle), Prof. Joseph Lakey (New Mexico State University), and Dr. Matthew Tam (University of Göttingen). His work integrates interdisciplinary approaches, combining pure mathematics with applications in engineering and mineral processing. His lab focuses on computational harmonic analysis and optimization-driven wavelet design.

• Civil Engineering
• Mechanical Engineering
• Manufacturing Engineering
• +۳ مورد دیگر

Dr. Shiwei Zhou is an Associate Professor in the School of Engineering at RMIT University. His research focuses on structural topology optimization, materials engineering, and additive manufacturing, with applications in civil, mechanical, and biomedical engineering. He is actively involved in supervising research students and developing innovative methods for optimizing structural designs using computational techniques such as B-spline level set methods and reaction diffusion-based models. His work bridges theoretical advancements with practical applications in composite materials, porous structures, and energy-absorbing systems. Research Interests: Structural topology optimization for fiber-reinforced composites Design of lightweight and high-performance materials Additive manufacturing and 3D concrete printing Computational methods for material design and homogenization Energy absorption structures and metamaterials Articles and Contributions: Dr. Zhou’s recent work emphasizes advancing topology optimization techniques, particularly for complex materials and structures. His research trends include integrating B-spline and NURBS methods with level set approaches, exploring fluid-structure interactions, and developing open-source tools for structural analysis. His studies on porous materials and fail-safe truss designs highlight practical engineering solutions. Advising and Grants: Open to supervising PhD and Masters students in structural engineering and additive manufacturing. His projects include optimizing buildable structures for 3D concrete printing and designing high-performance shell lattices.

• Numerical Analysis
• Geometric Modeling
• Approximation Theory
• +۳ مورد دیگر

Lucia Romani is a Full Professor in Numerical Analysis at the Department of Mathematics, University of Bologna. She holds a PhD from the University of Padua (2004) and a Master's Degree in Mathematics (summa cum laude) from the University of Bologna (2000). Her research focuses on geometric modeling, approximation theory, numerical linear algebra, and applications in computer-aided design and image processing. She has authored over 80 scientific publications and serves on editorial boards of journals like Computer-Aided Design and Computer Aided Geometric Design . She actively participates in SIAM and the Solid Modeling Association (SMA), holding leadership roles such as Vice Chair of the SIAM Activity Group on Geometric Design (2023-2024) and Program Co-Chair of SPM 2025. Her work includes organizing international conferences and supervising numerous theses across PhD, Master's, and Bachelor's levels. Her professional journey includes roles at the University of Milan-Bicocca (2007-2022) and postdoctoral fellowships at the University of Bologna (2003-2007) and the University of Cambridge (2002). She has conducted research visits at institutions like the University of Cambridge, EPFL, and ICTP Havana. She has also served on evaluation committees for doctoral theses and research projects in Italy and abroad. Research interests span subdivision schemes, Pythagorean Hodograph curves, and spline-based interpolation. Her recent work includes advancements in algebraic characterizations of curves, non-stationary subdivision algorithms, and applications in biomedical imaging and computer graphics.

• Electromagnetic Wave Propagation
• Electromagnetic Compatibility (EMC)
• Biomedical Effects of Radiation
• +۳ مورد دیگر

Selim Şeker is a part-time Professor at Bogazici University. His research focuses on electromagnetic wave propagation, electromagnetic compatibility (EMC), biomedical effects of radiation, and remote sensing. He has contributed to interdisciplinary studies spanning neurology, telecommunications, and environmental science. Research interests include advanced MRI techniques for glial tumors, neuroimaging for hydrocephalus diagnostics, and modeling electromagnetic radiation effects in biological systems. His work bridges theoretical electromagnetics with practical applications in healthcare and telecommunications infrastructure. Key publications span neuroimaging case studies, tumor recurrence analysis, and propagation models for wireless systems. Notable contributions include curve-fitting models for energy consumption forecasting and analysis of rare medical cases involving cranial trauma and metastatic carcinomas.

• Statistics
• Probability
• Copulas
• +۲ مورد دیگر

Johanna Genest Nešlehová is a Professor at the Institute for Statistics and Mathematics at Vienna University of Economics and Business. She previously served as Assistant Professor at McGill University (2009-2020) and holds a PhD in Mathematics from Carl von Ossietzky University of Oldenburg. Her research spans statistics, probability theory, and financial mathematics with focus areas including multivariate analysis, dependence modeling, copulas, and statistical methods for financial applications. She serves as editor for the Canadian Journal of Statistics and Statistics and Risk Modeling. Research publications demonstrate focus on multivariate statistical methods, dependence modeling, and applications in financial mathematics. Recent work includes stochastic decomposition methods, causal inference techniques, and rank-based estimation. Carrie M. Derick Award for Graduate Supervision and Teaching CRM-SSC Prize

Guido Kanschatمشاهده پروفایل

استاد

• Numerics
• Scientific Computing
• Finite Element Methods
• +۷ مورد دیگر

Professor Dr. Guido Kanschat is affiliated with Ruprecht-Karls-Universität Heidelberg at the Interdisciplinary Center for Scientific Computing (IWR), where he holds the Chair for Mathematical Methods of Simulation. His research focuses on numerics, scientific computing, and finite element methods with applications in radiation transfer, coupled flows, and multigrid solvers. His recent publications emphasize hybrid discretizations, GPU-accelerated multigrid methods, and discontinuous Galerkin techniques for Stokes equations and biharmonic problems. He is a co-founder of the open-source deal.II library and received the Wilkinson Prize for Numerical Software in 2007. Scientific Awards: Wilkinson Prize for Numerical Software (2007)

• Computational Imaging
• Compressed Sensing
• Signal Processing
• +۵ مورد دیگر

Dr. Karen Eguiazarian is a Professor of Signal Processing at the Department of Computing Sciences , Tampere University . He leads the Computational Imaging research group and has served as head of the Signal Processing Research Community (SPRC) at Tampere University of Technology (2016-2018). Education: M.Sc. in Mathematics, Yerevan State University, Armenia (1981) Ph.D. in Physics and Mathematics, Moscow State University, Russia (1986) Doctor of Technology in Signal Processing, Tampere University of Technology, Finland (1994) His research focuses on Computational Imaging , Compressed Sensing , and Efficient Signal Processing Algorithms , with significant contributions to Image/Video Restoration and Compression . Recent work includes AI-driven phase imaging, hyperspectral reconstruction, and noise-robust algorithms for remote sensing and biomedical applications. Scientific Awards: Service Award from the Society for Imaging Science and Technology (IS&T) (2014) Honorary Doctoral Degree from Don State-Technical University, Russia (2015) Dr. Eguiazarian has supervised 25 doctoral theses and published over 650 papers. He serves as Editor-in-Chief of the Journal of Electronic Imaging and associate editor of the IEEE Transactions on Image Processing , while co-founding Noiseless Imaging Oy , a Tampere University spin-off.

Jesús Requena Carriónمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Computer Modelling
• +۶ مورد دیگر

Jesús Requena Carrión is the Executive Vice Dean and Director of Queen Mary School Hainan (QMSH), and a Professor of Data Science and Engineering at Queen Mary University of London's School of Physical and Chemical Sciences. His research focuses on statistical data analysis, machine learning applications in healthcare and robotics, computer modeling of complex systems, and ethical approaches to data science. He advocates for rigorous and ethical use of digital technology and authored the open-access book Principles of Machine Learning: A Deployment-First Perspective . His work spans interdisciplinary areas, including healthcare technology, serious games for emotion and decision-making studies, and sensor systems for infrastructure maintenance. He leads the Data Science and Engineering Research Group, emphasizing practical deployment and societal impact. Recent research trends include machine learning-driven solutions for medical diagnostics, autonomous systems, and educational tools. Publications reflect innovation in robotics trajectory planning, neurophysiological risk assessment, and stress recognition via wearable sensors. He emphasizes collaboration in global education through QMSH and contributes to neuromorphic sensor networks and bio-inspired engineering applications. No scientific awards are explicitly listed, but his work impacts diverse fields from biomedical engineering to urban planning.

• Finite Element Technology
• Computational Fluid-Structure Interaction
• Structural Finite Elements
• +۸ مورد دیگر

Professor Wulf Dettmer is affiliated with the School of Aerospace, Civil, Electrical and Mechanical Engineering at Swansea University. He specializes in Finite Element Technology , Computational Fluid-Structure Interaction (FSI) , and Morphing Aircraft research. His work spans disciplines including: Fluid-Structure Interaction Structural Dynamics Renewable Energy Systems Multi-Scale Modeling Key research trends include: Advanced time integration schemes for dynamic problems Machine learning applications in material modeling Cryogenic hydrogen energy systems Partitioned FSI solvers Flexible structures for energy harvesting Professor Dettmer has supervised numerous PhD and EngD projects in collaboration with Prof Djordje Peric and other colleagues, focusing on topics like neural network-based finite element frameworks and multi-scale composite analysis.