جست‌وجوی استاد و پژوهشگر

• Numerical Analysis
• Applied Mathematics
• Computational Mathematics
• +۳ مورد دیگر

Emre Mengi is a Professor in the Department of Mathematics at Koc University, affiliated with the School of Computational Sciences and Engineering. He holds a PhD (2006) and Master’s (2003) in Mathematics from New York University, and a B.A. (2000) in Computer Engineering from Middle East Technical University. Research areas: Numerical Analysis , Eigenvalue Optimization , and Model Reduction Current focus: Subspace frameworks for nonlinear eigenvalue problems and pseudospectral abscissa minimization His recent work emphasizes large-scale stability analysis of descriptor and Hamiltonian systems, distance-to-instability measures, and L∞-norm computation for robust control. He has developed algorithms for nonsmooth eigenvalue optimization and software tools like Eigopt. Scientific Awards : BAGEP Award (2014), Householder Award Honorable Mention (2008), Janet Fabri Prize Honorable Mention (2007), IMA Leslie Fox Prize (2004), Continuous Optimization Young Researcher Award

• Machine Learning
• Mathematical Image Analysis
• Scientific Computing
• +۶ مورد دیگر

Rihuan Ke serves as a Lecturer within the School of Mathematics at the University of Bristol, United Kingdom. With expertise spanning mathematical theory and computational applications, Dr. Ke actively contributes to interdisciplinary research that bridges abstract mathematics with real-world imaging challenges across scientific domains. Education: BSc MSc PhD Research Interests: Dr. Ke's work centers on developing mathematical frameworks integrated with data-driven methodologies for large-scale imaging analysis. Their research pioneers hybrid approaches combining variational methods, tensor algebra, and deep learning architectures to solve complex inverse problems. Key application areas include medical diagnostics (MRI/CT enhancement), materials science (microscopy segmentation), environmental monitoring (satellite imagery), and astronomical observations (telescope data processing). The research emphasizes creating interpretable models that maintain mathematical rigor while leveraging modern machine learning techniques. Publication Trends: Analysis of 15 recent publications (2016-2024) reveals a strong trajectory from foundational tensor mathematics toward applied deep learning for imaging. The 2020-2024 period shows concentrated innovation in semi-supervised segmentation frameworks, invertible neural architectures for inverse problems, and unsupervised denoising methods. A consistent theme involves developing mathematically grounded solutions for data-scarce scenarios, with growing emphasis on real-world deployment in traffic monitoring, medical imaging, and astronomical systems. Scientific Awards: No scientific awards or honors were documented in the provided materials. Advising and Grants: The available information does not specify any doctoral students, postdoctoral researchers, or research funding sources. Dr. Ke's collaborative network includes prominent figures in mathematical imaging such as Carola-Bibiane Schönlieb, indicating active participation in interdisciplinary research consortia. Laboratory Affiliations: While specific lab assignments aren't detailed, Dr. Ke's work aligns with computational imaging groups at Bristol, particularly those focused on inverse problems and machine learning applications in scientific imaging domains.

Qiang Yeمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Numerical Analysis
• +۳ مورد دیگر

Qiang Ye is a Professor in the Department of Mathematics at the University of Kentucky. His research focuses on machine learning, deep learning, numerical analysis, and optimization algorithms, with a particular emphasis on neural network architectures, generative adversarial networks (GANs), and numerical methods for solving large-scale linear systems and eigenvalue problems. He has contributed to advancements in recurrent neural networks, batch normalization preconditioning, and symmetry-exploiting convolutional networks. Ye's work bridges theoretical foundations and practical applications, including speech enhancement, molecular representation learning, and industrial welding process monitoring. His research often integrates insights from numerical linear algebra to improve the stability and efficiency of machine learning models. Recent efforts include developing adaptive optimization techniques and regularization methods to address challenges in deep learning training and model accuracy. Awards and grants are not explicitly listed in the provided information. Ye collaborates on interdisciplinary projects, as evidenced by co-authored papers with researchers from engineering and computational chemistry domains. His lab's activities likely center on computational methods for scientific and engineering problems, though specific lab names or teams are not mentioned.

Xiangxiong Zhangمشاهده پروفایل

دانشیار

• Numerical Analysis
• Scientific Computing
• Applied Mathematics
• +۳ مورد دیگر

Xiangxiong Zhang is an Associate Professor in the Department of Mathematics at Purdue University, specializing in numerical analysis, scientific computing, and applied mathematics. His research focuses on numerical methods for partial differential equations, optimization algorithms, and computational fluid dynamics. He holds a PhD and has been actively involved in teaching graduate and undergraduate courses, including numerical PDEs, optimization, and linear algebra. His work emphasizes high-order numerical methods such as discontinuous Galerkin schemes, finite element methods, and spectral element methods, with applications in fluid dynamics, plasma physics, and compressible flow simulations. Notable contributions include positivity-preserving limiters, bound-preserving schemes, and Riemannian optimization techniques for matrix constraints. Zhang collaborates with researchers in computational mathematics and has published extensively in top journals like Journal of Computational Physics and SIAM Journal on Scientific Computing . His research also addresses challenges in GPU-accelerated computing for large-scale scientific simulations.

Fatma Kilinc-Karzanمشاهده پروفایل

استاد

• Operations Research
• Analytics
• Machine Learning
• +۸ مورد دیگر

Fatma Kilinc-Karzan is a Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University, and holds the Frank A. and Helen E. Risch Faculty Development Chair. She is also an Associate Professor of Computer Science (by courtesy) and affiliated with the Algorithms, Combinatorics, and Optimization (ACO) PhD Program. Her career includes visiting roles at institutions like the Simons Institute at UC Berkeley and extensive professional service on editorial boards and conference committees. PhD in Industrial and Systems Engineering (minor in Mathematics) from Georgia Institute of Technology B.S. and M.S. in Industrial Engineering (minor in Information Systems) from Middle East Technical University Research Interests : Her work focuses on convex optimization , structured nonconvex optimization , and their applications in optimization under uncertainty (robust optimization, chance constraints), machine learning (preference learning from limited data), and business analytics . She explores theoretical aspects like semidefinite programming (SDP) relaxations, convex hull characterizations, and algorithmic efficiency for large-scale problems. Article Trends : Her recent publications emphasize semidefinite programs , rank-one function optimization , and chance-constrained programming with applications in portfolio optimization , healthcare , and recommender systems . Key methodologies include perspective reformulation , submodularity , and first-order algorithms . Scientific Awards : 2015 INFORMS Optimization Society Prize for Young Researchers 2014 INFORMS JFIG Best Paper Award Advising and Grants : She has advised over a dozen PhD students, many of whom won awards like the INFORMS Optimization Society Best Student Paper Prize. Her research is supported by grants including an NSF CAREER Award , ONR grant , and AFOSR grant . She collaborates with institutions like IBM and the Simons Institute.

James Brannickمشاهده پروفایل

استاد

• Lattice Field Theory
• Numerical Solutions to PDEs
• Multigrid Methods
• +۳ مورد دیگر

James Brannick is a Professor in the Department of Mathematics at Pennsylvania State University, affiliated with the Eberly College of Science. His research focuses on numerical solutions to partial differential equations (PDEs), multigrid methods, and lattice field theory, with applications in computational physics and quantum chromodynamics (QCD). He holds a Ph.D. from the University of Colorado Boulder (2005). Research interests include developing advanced multigrid algorithms for complex systems, such as coupled PDEs and spacetime discretizations, with a particular emphasis on algebraic multigrid (AMG) methods. His work bridges theoretical foundations (e.g., convergence analysis) and practical implementations (e.g., parallel solvers for large-scale problems). Recent publications emphasize AMG optimization, bootstrap techniques, and applications to fluid dynamics, quantum field theory, and materials science. Collaborative projects include NSF-funded initiatives for parallel space-time solvers and multigrid methods in lattice QCD. Brannick’s contributions address challenges like critical slowing down in lattice simulations and efficient handling of anisotropic or indefinite problems. Grants include subcontract B629176 (2019) and prior NSF awards for multigrid solvers in coupled PDE systems. His research often intersects with high-performance computing and interdisciplinary applications, such as neutron star dynamics and phase-field modeling.

Julien Langouمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Linear Algebra
• Parallel Algorithms
• +۳ مورد دیگر

Julien Langou is a Professor and Department Chair in the Department of Mathematical and Statistical Sciences at the University of Colorado Denver, part of the College of Liberal Arts and Sciences. His expertise lies in Computational Mathematics, focusing on numerical linear algebra, parallel algorithms, and high-performance computing. He has contributed significantly to the development of software libraries such as PLASMA and MAGMA, advancing dense linear algebra computations on modern architectures. His research emphasizes communication-avoiding algorithms, fault-tolerant numerical methods, and optimization of data movement in large-scale systems. Langou's work includes foundational contributions to matrix factorization techniques, such as QR and Cholesky algorithms, as well as innovations in distributed memory systems. He has led projects like the BALLISTIC initiative to modernize linear algebra libraries for exascale computing. His research has been supported by grants from NSF and other agencies, addressing challenges in parallel computing efficiency and algorithmic robustness. Key awards include the NSF CAREER award for foundational work in numerical linear algebra and leadership in collaborative research frameworks. Langou has authored numerous publications on topics ranging from error handling in numerical libraries to optimization of I/O-bound algorithms. His academic leadership includes roles in curriculum development and fostering interdisciplinary collaboration in computational science.

Jesse Laeuchliمشاهده پروفایل

مدرس ارشد

• Cyber Security
• Quantum Computing
• Linear Algebra
• +۵ مورد دیگر

Dr. Jesse Laeuchli is a Senior Lecturer in the Department of Computer Science and Engineering at UNSW, specializing in Cyber Security, Quantum Computing, and Numerical Linear Algebra. He holds a PhD from the College of William and Mary, where his dissertation focused on matrix function diagonal estimation. His research bridges theoretical foundations (e.g., graph analytics, differential privacy) with practical applications like quantum remote memory attestation and posthumous data management through tools like BeyondLife. He has conducted security reviews for the Australian Government and led projects funded by the Defense Innovation Network and EU's HERMES initiative. Education : PhD in Numerical Linear Algebra, College of William and Mary Research Interests : Quantum Cybersecurity: Developing protocols using quantum entanglement for remote hardware verification Cyber Defense: Specializing in penetration testing and securing IoT systems Data Privacy: Innovating differential privacy techniques for centrality measures and graph analysis Legacy Systems: Creating open-source solutions for posthumous data management Grants & Awards : $528k IoT Security Grant (CSCRC, 2023) $136k Quantum Security Grant (Defense Innovation Network) Cyber Security CRC Award for pandemic contact tracing efforts Advising & Labs : Supervises research in quantum security and data forensics. Collaborates on projects like BeyondLife, an open-source digital will platform.

Lothar Reichelمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Ill-Posed Problems
• +۳ مورد دیگر

Lothar Reichel is Professor in the Department of Mathematical Sciences at Kent State University, specializing in Numerical Analysis and Scientific Computing. His research focuses on developing computational methods for large-scale problems in linear algebra and inverse problems. Research domains include: Algorithms for ill-posed inverse problems and image restoration Large-scale eigenvalue and singular value computations Iterative methods for linear systems Orthogonal polynomials and quadrature rules Structured matrix computations Professor Reichel founded the first electronic mathematics journal, Electronic Transactions on Numerical Analysis (ETNA), featuring innovative publication approaches including interactive numerical experiments. He has organized numerous conferences on numerical linear algebra and inverse problems.

• Partial Differential Equations
• Probability Theory
• Stochastic Homogenization
• +۳ مورد دیگر

Charles Smart is a Professor of Mathematics at Yale University, located at Kline Tower - 219 Prospect Street, New Haven, CT 06511. His research focuses on partial differential equations, probability theory, and stochastic homogenization, with notable contributions to elliptic equations, statistical physics, and discrete models such as the Abelian sandpile. He holds a prominent academic position within the Department of Mathematics at Yale's Faculty of Arts and Sciences. Smart's work bridges theoretical mathematics with applications in physics and engineering, addressing topics like unique continuation properties, localization phenomena, and scaling limits. His recent publications explore advanced concepts such as large-scale analyticity in elliptic systems, free boundary problems, and the interplay between singular integrals and mixing flows. He has contributed to foundational studies in stochastic homogenization, including quantitative error estimates and convex integral functionals. His research also extends to geometric analysis, such as convex hull peeling and facet formation in free boundary problems. No specific grants or awards are listed in the provided information. His advising record remains unspecified, though his academic standing suggests significant mentorship potential.

Thomas Huckleمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Scientific Computing
• Parallel Computing
• +۷ مورد دیگر

Prof. Thomas Huckle is a Professor of Scientific Computing at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science. His research focuses on numerical linear algebra, parallel computing, and their applications in physics and computer science. Key interests include solving linear problems on parallel architectures, image processing, multigrid methods, preconditioning, and tensor-based high-dimensional problem approximation. Education: Studied mathematics and physics at the University of Würzburg (diploma in mathematics, 1985 PhD, 1991 habilitation). Professional History: DFG-funded research at Stanford University (1993–1994), appointed to TUM in 1995, and member of the Mathematics Department since 1997. Research Interests: Prof. Huckle’s work spans numerical methods for large-scale systems, including structured matrices, regularization techniques, and quantum computing applications. He develops algorithms for parallel computing environments and contributes to software tools like ELPA for eigenvalue problems. Grants and Labs: Engaged in projects such as the ELPA-AEO eigensolver and ESSEX-II initiatives. Active in the SCCS (Scientific Computing and Computational Science) group at TUM, focusing on high-performance computing and numerical methods.

Karl Meerbergenمشاهده پروفایل

استاد

• Algebraic eigenvalue problems
• Algebraic model order reduction
• Preconditioning
• +۷ مورد دیگر

Karl Meerbergen is a Full Professor in the Department of Computer Science at KU Leuven, Faculty of Engineering Sciences. He leads research in numerical analysis and applied mathematics with a focus on eigenvalue problems, model order reduction, and computational linear algebra. He is a member of the Numerical Analysis and Applied Mathematics (NUMA) research unit and holds affiliations with multiple KU Leuven institutes including iSi Health, Leuven.AI, Leuven.AM, and the LGI Gravitation Institute. His research spans several key areas of numerical mathematics with emphasis on algebraic eigenvalue problems, algebraic model order reduction, preconditioning techniques, computational acoustics, tensor computations, exascale computing, generic programming, and parallel computing. His work bridges theoretical numerical analysis with practical applications in engineering and scientific computing. Analysis of his recent publications shows a strong focus on advanced numerical methods for eigenvalue problems, model order reduction techniques, and parallel computing approaches. His work frequently addresses challenges in large-scale scientific computing, with applications in structural dynamics, acoustics, and optimization problems. The research demonstrates increasing sophistication in handling nonlinear and parametric systems through rational approximation methods and specialized preconditioning techniques. Dr. Meerbergen actively contributes to the academic community through his teaching responsibilities and supervision of graduate students. His work has significant implications for computational science and engineering applications requiring efficient numerical solutions to complex mathematical problems.

Marija Miloloža Pandurمشاهده پروفایل

استادیار

• Numerical Mathematics
• Numerical Linear Algebra
• Matrix Theory
• +۲ مورد دیگر

Marija Miloloža Pandur is an Assistant Professor at the School of Applied Mathematics and Informatics , Josip Juraj Strossmayer University of Osijek , Croatia. Her expertise lies in numerical mathematics and numerical linear algebra, with a focus on eigenvalue problems and matrix theory. Education: PhD in Mathematics, University of Zagreb, 2016 BSc in Mathematics and Computer Science, University of Osijek, 2007 Research Interests: Her research spans numerical linear algebra, including the development of algorithms for detecting definite Hermitian matrix pairs, solving quadratic eigenvalue problems, and improving preconditioned solvers for large-scale matrix equations. She has also contributed to applied areas such as electoral methods and mathematical education. Publications & Trends: Her recent work (2014–2025) shows a consistent focus on numerical algorithms for eigenvalue problems, particularly involving Hermitian and quadratic matrix pencils. She has published in high-impact journals like BIT Numerical Mathematics , Numerical Algorithms , and Electronic Transactions on Numerical Analysis . Supervision & Teaching: She has supervised 7 undergraduate theses and taught a wide range of courses from differential calculus to digital literacy across multiple faculties including physics, economics, and food technology. She has also been active in organizing workshops and science festivals for students. Service & Outreach: She is a member of the Croatian Mathematical Society and has served as a reviewer for journals such as Croatian Operational Research Review and International Journal of Mathematical Education in Science and Technology .

Zoran Tomljanovićمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• Damping Optimization in Mechanical Systems
• Control Theory
• +۱ مورد دیگر

Zoran Tomljanović is an Associate Professor and Vice-Dean for Teaching and Students at the School of Applied Mathematics and Informatics, Josip Juraj Strossmayer University of Osijek. His research focuses on numerical linear algebra, damping optimization in mechanical systems, control theory, and matrix equations, with significant contributions to model reduction and parametric system optimization. His work includes novel approaches to $H_{\infty}$ norm analysis for multi-agent systems, dimension reduction techniques for damped vibrational systems, and parametric dominant pole algorithms for semi-active damping optimization. These methods have been applied to problems in mechanical engineering, synchronization, and high-dimensional data partitioning. Key projects led by Tomljanović include Accelerated solution of optimal damping problems (DAAD 2021–2022), Vibration Reduction in Mechanical Systems (Croatian Science Foundation 2020–2023), and Robustness optimization of damped mechanical systems (DAAD 2017–2018). He has also contributed to educational initiatives through publications like Metode optimizacije (2014), a textbook on optimization methods. Professional activities include co-organizing the 8th Croatian Mathematical Congress (2024), Winter School on Model Reduction (2024), and multiple international workshops on optimal control and model reduction. Teaching responsibilities include courses on linear algebra and control theory applications.

Ninoslav Truharمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Systems and Control Theory
• Applied Mathematics
• +۴ مورد دیگر

Ninoslav Truhar is Full Professor at the School of Applied Mathematics and Informatics , Josip Juraj Strossmayer University of Osijek , Croatia, where he also heads the Department of Applied Mathematics and Computer Science . Education B.S. in Mathematics and Physics, 1987 – University of Osijek M.S. in Mathematics, 1995 – University of Zagreb Ph.D. in Mathematics, 2000 – University of Zagreb Research interests lie at the intersection of numerical linear algebra , systems and control theory , and applied mathematics . His work centres on linear matrix equations , damping optimization in mechanical systems, and the perturbation theory of invariant subspaces . These themes repeatedly appear in his extensive publication record, driving both theoretical advances and practical applications in vibration control and matrix computations. Across his recent publications, the dominant trends are the derivation of sharp perturbation bounds for eigenvalues and eigenspaces of structured matrix problems, the development of efficient algorithms for damping parameter optimisation, and the creation of dimension-reduction techniques that accelerate numerical solution of large-scale Lyapunov and quadratic eigenvalue problems. Professional service Member of the editorial boards of Computational Mathematics and Computer Modeling with Applications , Numerical Algebra, Control and Optimization , Mathematics (MDPI) , Mathematical Communications , and Osječki matematički list . Teaching Undergraduate/graduate courses: Applied Linear Algebra and Scientific Computing , Optimisation Methods , Dynamical Systems . Supervises B.Sc. and M.Sc. theses on topics aligned with his research expertise.