جستجوی استاد راهنمای دانشگاهی

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نمایش ۱۶۶-۱۸۰ از ۱۹۲ استاد راهنما

اطلاعات پروفایل‌ها از صفحات عمومی دانشگاه‌ها گرفته شده و ممکن است به‌روز نباشد.

Christian Goutمشاهده پروفایل

استاد

National Institute of Applied Sciences of RouenFrance

Christian Gout is a Professor at INSA Rouen Normandie, affiliated with the Laboratoire de Mathématiques de l'INSA de Rouen Normandie. His research focuses on data approximation, AI, partial differential equations (PDEs), numerical analysis, shape optimization, and image processing, with applications in geophysics, energy, and medicine. He is an external collaborator with MAKUTU (INRIA Bordeaux Sud Ouest) and UNAM (Mexico). Director of Research and Doctoral Studies at INSA Rouen Normandie (2018–2022). Previously held roles include Director of the Mathematics Lab at INSA Rouen (2010–2017) and Professor (PR) at UVHC (2006–2010). Research interests bridge numerical methods (e.g., spectral element and discontinuous Galerkin methods) with applications in seismic imaging, wind field modeling, and medical imaging. He has supervised numerous PhD students and co-authored books like Data Approximation: Mathematical Modelling and Numerical Simulations (2019). Collaborations span industries including Dassault Systèmes, Siemens-Gamesa, and Total. His work includes developing mathematical models for wind velocity reconstruction, image segmentation, and fog computing in smart factories. He has also contributed to projects like ECOS NORD for PDE-based inverse problems in energy and geophysics.

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Volker Michelمشاهده پروفایل

استاد

University of SiegenGermany

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.

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Aanjaneya Mridulمشاهده پروفایل

دانشیار

Rutgers, The State University of New JerseyUnited States

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.

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Anna Soósمشاهده پروفایل

دانشیار

Babes-Bolyai UniversityRomania

Anna Soós is an Associate Professor at the Hungarian Mathematics and Informatics Institute, Faculty of Mathematics and Informatics, Babes-Bolyai University (Cluj-Napoca, Romania). She holds a PhD in Mathematics (2002) and has been active in stochastic analysis, fractal theory, and approximation methods. Her academic career includes leadership roles such as Vice-Rector for Curriculum and Hungarian Studies (2012–present) and Vice-Dean of the Faculty of Mathematics and Informatics (2008–2012). Education: MSc in Mathematics (Babes-Bolyai, 1982), MSc in Computer Science (Eötvös Loránd University, 1996), PhD in Mathematics (Babes-Bolyai, 2002) Her research focuses on deterministic and random fractals, stochastic differential equations, and homogenization on fractal structures. She has contributed to stochastic analysis through: Developing contraction methods in probabilistic metric spaces Wavelet approximations for SDE solutions Fractal interpolation techniques Scientific awards include: Bolyai János Research Fellowship Erasmus and CEEPUS Fellowships Domus Hungarica Scientiarum et Artium Grants World Bank Project Participation Anna Soós has coordinated international research projects like ERASMUS IP DSL2013 and FP7/MASCIL, while organizing conferences including the International Symposium on Numerical Analysis and Approximation Theory and Central European Functional Programming Summer Schools.

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Jeffrey Hoganمشاهده پروفایل

دانشیار

University of Newcastle Australia (UON)Australia

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.

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Markus Heglandمشاهده پروفایل

استاد

Technical University of MunichGermany

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.

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Boris Levitمشاهده پروفایل

استاد

Queen's University at KingstonCanada

Boris Levit is a Professor in the Department of Mathematics and Statistics at Queen's University, located in Kingston, Ontario. He holds a Ph.D. from the Institute of Information Transmission in Moscow and a Dr.Sc. from Vilnius University. His research focuses on non-parametric estimation, semi-parametric models, optimal design in non-parametric regression, and optimal interpolation methods. Levit has made significant contributions to statistical theory, particularly in applying differential geometry and partial differential equations to statistical problems. Levit's academic journey includes positions at Moscow State University, the University of Utrecht in the Netherlands, and Queen's University. His work emphasizes modern non-asymptotic approaches to statistical estimation, utilizing Jacobi elliptic functions and addressing challenges in moderate or small sample sizes. He is currently recruiting graduate students. His research has been recognized through awards, including the Dr.Sc. from Vilnius University for integrating differential geometry into statistical research. His publications span over five decades, addressing topics like minimax estimation, spline interpolation, and adaptive filtering. He teaches courses in statistical inference and nonparametric methods.

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Lucia Romaniمشاهده پروفایل

استاد

University of BolognaItaly

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.

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Leili Rafiee Sevyeriمشاهده پروفایل

استادیار

University of WaterlooCanada

Leili Rafiee Sevyeri is an Assistant Professor in the Teaching Stream at the University of Waterloo, affiliated with the David R. Cheriton School of Computer Science. Her research focuses on hybrid symbolic-numeric computation, scientific computing, and computer algebra. She holds a PhD and MSc in Applied Mathematics from Western University (2020 and 2016, respectively). Her teaching spans theoretical computer science, numerical methods in finance/business, and introductory computer science courses. She is based in location MC 6431 on campus and can be reached at leili.rafiee.sevyeri@uwaterloo.ca . Research interests include developing computational methods at the intersection of symbolic and numeric techniques, with applications in linear algebra, polynomial systems, and matrix theory. Recent work emphasizes interpolation methods, Bohemian matrix analysis, and approximate GCD algorithms. Her publications (e.g., Google Scholar ) reflect a focus on advancing computational tools for scientific and mathematical problems. No scientific awards are explicitly listed in the provided information. Advising and grant details are not mentioned, but her academic webpage provides further details on ongoing and past projects. She is actively involved in computational mathematics education and research within the university's Faculty of Mathematics.

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Selim Şekerمشاهده پروفایل

استاد

Boğaziçi UniversityTurkey

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.

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Amit Acharyaمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Amit Acharya is the Paul P. Christiano Professor of Civil and Environmental Engineering at Carnegie Mellon University (CMU), with affiliations in the Center for Nonlinear Analysis and Mathematical Sciences, and Materials Science and Engineering (courtesy). He leads the Mechanics, Materials, and Computing (MMC) research group, focusing on theoretical and computational defect mechanics in crystalline, liquid crystalline, and metallic glass systems. His work bridges continuum mechanics, applied mathematics, and materials science, emphasizing the interplay between structural defects and material behavior. Education: Ph.D. (Theoretical & Applied Mechanics, UIUC 1994); MS (Mining Engineering, University of Utah 1991); BTech (Mining Engineering, Indian School of Mines 1988) Research interests include dislocation dynamics, coarse-graining of nonlinear systems, and differential geometry in structural mechanics. Notable contributions include the Hysteresis nonlinear viscoelastic material model and S4 shell element during his tenure at HKS/Simulia (1995–1998). He has pioneered continuum models for defect interactions in polycrystalline materials and explored topological defects in nematic systems. Recent work (2023–2025) advances variational principles in dislocation mechanics, thermomechanical coupling, and machine learning-enhanced computational methods. His studies address grain boundary dynamics, fracture mechanics, and the geometric foundations of plasticity. Ongoing projects explore actuation mechanisms in nematic glasses and the unification of dislocation-based plasticity theories. Awards and recognition are not explicitly listed, though his contributions are reflected in foundational models used in commercial software (e.g., ABAQUS) and academic research. He advises graduate students through CMU’s CEE department and collaborates on DOE-funded initiatives, such as the Center for Simulation of Advanced Rockets (UIUC, 1998–2000). Labs/Teams: Lead researcher in the MMC Group, affiliate of CMU’s Center for Nonlinear Analysis, and collaborator in multi-scale materials modeling networks.

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Guido Kanschatمشاهده پروفایل

استاد

Heidelberg UniversityGermany

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)

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Martin Lindمشاهده پروفایل

استادیار

Karlstad UniversitySweden

Martin Lind is an Assistant Professor of Mathematical Analysis at Karlstad University. His research focuses on advanced topics in mathematical analysis, including discrepancy theory, functions of bounded variation, approximation theory, and multiscale systems. He holds a PhD from Karlstad University (2013) and completed a post-doctoral fellowship at the University of South Carolina (2014-2015). Education: PhD in Mathematics, Karlstad University, 2013 His work bridges pure and applied mathematics, addressing problems in number theory, functional analysis, and numerical methods. Key research themes include: Discrepancy estimates for pseudorandom sequences Properties of functions with bounded variation (p-variation, Lambda-variation) Nonlinear approximation methods using splines Analysis of multiscale elliptic-parabolic systems His recent publications (2020-2024) emphasize number-theoretic aspects of sequences and their applications in quasi-Monte Carlo methods. He collaborates actively on applied projects like pollution reduction modeling with photocatalytic materials. Notable contributions include: Developing variational characterizations of Sobolev spaces Establishing convergence rates for semidiscrete Galerkin schemes Exploring Fubini-type properties in multivariate analysis

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João Guerraمشاهده پروفایل

دانشیار

University of LisbonPortugal

João Guerra is an Associate Professor at ISEG (University of Lisbon) in the Department of Mathematics, specializing in Financial Analysis and Mathematics. He holds a PhD in Mathematics from the University of Barcelona (2009), as well as degrees in Technological Physics Engineering (IST, 1995) and Applied Mathematics (University of Évora, 1999). His research focuses on stochastic analysis, Lévy processes, and fractional stochastic processes applied to mathematical finance. Education: PhD in Mathematics, University of Barcelona (2009) DEA in Mathematics, Universitat de Barcelona (2003) Master's in Applied Mathematics, University of Évora (2003) Bachelor's in Technological Physics Engineering, Instituto Superior Técnico (1995) Research Interests: Stochastic calculus, Lévy processes, fractional stochastic systems, and their applications in financial modeling. He has published widely in journals like Quantitative Finance , Journal of Computational Finance , and Stochastic Processes and Their Applications . Teaching and Academic Roles: Coordinator of the Master's in Financial Mathematics and PhD in Applied Mathematics for Economics and Management (MAEG) Developed courses on stochastic calculus, differential equations, and financial models Supervised 2 PhD and 19 Master's theses Professional Experience: Assistant Professor at ISEG (2009–2023) Researcher in six international projects, including a current bilateral Portugal-Germany project

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Baltasar Enrique Beferull Lozanoمشاهده پروفایل

استاد

University of AgderNorway

Baltasar Enrique Beferull Lozano is a tenured Professor at the University of Agder , leading the Center Intelligent Signal Processing and Wireless Networks (WISENET) since 2015. With a PhD in Electrical Engineering from USC (2002) and prior roles at EPFL, AT&T Shannon Labs, and University of Valencia, his career spans 20+ years of academic and industrial research in signal processing, wireless systems, and AI. Education: PhD (USC), MSc (USC), MSc (University of Valencia) Expertise: Data Science, Machine Learning, Graph Signal Processing, Cyber-Physical Systems His research focuses on AI-driven wireless networks and in-network collective intelligence , addressing fundamental and applied challenges in smart water systems , energy management , and next-gen 5G/6G . He has secured 20+ international projects including 10 EU-funded initiatives (HYDROBIONETS, SENDORA) and 5 RCN-funded projects. Recent publications emphasize dynamic graph learning from time series data, quantized graph filters , and multi-agent reinforcement learning for networked environments. Awards include IEEE Best Paper Awards (2012, 2021), TOPPFORSK Grant (2015), and Ramón y Cajal Program Rank #1 (2005). As a Senior IEEE Member , he serves as Area Editor for IEEE Transactions on Signal Processing and evaluates research proposals for the European Commission , NSF , and Qatar National Research Fund . His lab has produced 15 PhD graduates and collaborates with 12+ industry partners including Telenor, IBM, and SINTEF.

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