جست‌وجوی استاد و پژوهشگر

Hongkai Zhaoمشاهده پروفایل

استاد

• Computational and Applied Mathematics
• Numerical Analysis
• Scientific Computing
• +۴ مورد دیگر

Hongkai Zhao is the Ruth F. DeVarney Distinguished Professor of Mathematics and Chair of the Department of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. in Mathematics from UCLA (1996) and has held prior positions including Chancellor's Professor at UC Irvine and Gábor Szegő Assistant Professor at Stanford University. His research focuses on computational and applied mathematics with applications in inverse problems, imaging, PDEs, and scientific computing. Key research areas include numerical methods for wave propagation, radiative transfer, and shape analysis. Zhao has led NSF-funded projects on PDE learning, radiative transfer, and analysis training. He has received prestigious awards including the Feng Kang Prize (2007) and SIAM Fellowship (2022). Teaching includes advanced courses on stochastic processes, numerical linear algebra, and mathematical analysis. His work bridges theory and practice, contributing to medical imaging algorithms, computational physics, and geometric modeling.

Ron Morganمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Mathematics
• Linear Algebra
• +۲ مورد دیگر

Ron Morgan is a Professor of Mathematics at Baylor University and serves as an Undergraduate Advisor. His primary affiliation is with the Department of Mathematics, where he focuses on numerical analysis and computational methods. Research Interests Numerical analysis of large linear systems and eigenvalue problems Development of efficient algorithms for Monster Matrices Applications in computational mathematics and sparse matrix techniques Specialized in Krylov subspace methods like GMRES and Arnoldi Scientific Contributions are centered on iterative solvers, deflation techniques, and multigrid methods for eigenvalue problems. His work spans publications in prestigious journals such as SIAM and Mathematics of Computation. Former Ph.D. Students Zhao Yang (2015) Huy Nguyen (2015) Quan Liu (2014, Co-Advised) Dywayne Nicely (2008) Dean Darnell (2006, Co-Advised) Min Zeng (1996) Contact: Ronald_Morgan@baylor.edu | (254) 710-6575

• Matrix Theory
• Mathematical Biology
• Pure Mathematics
• +۳ مورد دیگر

Dr. Robert Reams is a Professor and Chair of the Department of Mathematics at SUNY Plattsburgh. He holds a B.A. in Mathematics from Trinity College Dublin, followed by an M.A. and Ph.D. in Mathematics from University College Dublin. His academic journey includes postdoctoral work in mathematical biology and teaching positions at institutions in Ireland, England, and the U.S., including the National University of Ireland, Galway. He joined SUNY Plattsburgh in 2008. Dr. Reams teaches Introductory Statistics (MAT161) and other mathematics courses, including Statistical Inference (MAT362), Probability Models (MAT365), and Financial Math (MAT460). His research focuses on matrix theory, copositive matrices, magic squares, and applications of matrix theory to mathematical biology. His homepage provides detailed insights into his pure mathematics research. His research interests span pure mathematics, with notable contributions to copositive matrices, matrix scaling, and stochastic matrix properties. He has explored interdisciplinary applications in distance geometry and molecular conformations. His work often intersects linear algebra, optimization, and spectral theory. Contact details include his office at 244B Hawkins Hall, SUNY Plattsburgh, and email addresses: rream001@plattsburgh.edu and robert.reams@plattsburgh.edu. Additional resources include his faculty website and publications on topics like matrix completion and inverse eigenvalue problems.

Mark Rudelsonمشاهده پروفایل

استاد

• Banach Spaces
• Convex Bodies
• Probability Theory
• +۳ مورد دیگر

Mark Rudelson is a Professor of Mathematics at the University of Michigan, part of the College of Literature, Science, and the Arts (LSA). He holds a Ph.D. from the Hebrew University of Jerusalem (1997) and an M.Sc. from Leningrad Polytechnical Institute (1988). His research focuses on geometric functional analysis, probability theory, and random matrix theory, with contributions to the study of high-dimensional normed spaces, convex bodies, and spectral properties of random graphs and matrices. Rudelson's work bridges pure mathematics and applications in data science and signal processing. His research interests include the geometric and spectral properties of random matrices, convex geometry, and the interplay between probability and high-dimensional structures. Notable contributions include studies on the invertibility of random matrices, delocalization of eigenvectors, and the circular law for sparse matrices. Rudelson has collaborated widely, with impactful papers in top journals such as Annals of Mathematics , Probability Theory and Related Fields , and Advances in Mathematics . Rudelson's recent work explores the volume estimation of convex bodies, sparse matrix theory, and applications of random matrix techniques to problems in statistics and machine learning. His research often addresses foundational questions in mathematics with implications for computational and theoretical data analysis.

• Finite Element Methods
• Numerical Analysis
• Linear Elasticity
• +۱۱ مورد دیگر

Carsten Carstensen is a Professor at the Humboldt University of Berlin, affiliated with the Faculty of Mathematics and Natural Sciences and the Institute of Mathematics. His research focuses on numerical analysis, finite element methods, computational mechanics, and adaptive algorithms. He has contributed significantly to the development and theoretical analysis of numerical techniques for solving complex mathematical models in solid and fluid mechanics, including studies on linear elasticity, Stokes equations, and non-Newtonian fluids. His work emphasizes error estimation, method robustness, and optimal convergence rates. His research interests include the application of advanced finite element methodologies such as hybrid high-order (HHO) methods, virtual elements, and mixed formulations. He also explores eigenvalue problems, nonlinear partial differential equations, and plasticity models, with a particular attention to the interplay between mathematical theory and computational implementation. Recent studies highlight his efforts to unify error analysis frameworks and improve adaptivity in numerical simulations. While no scientific awards are explicitly listed, his extensive publication record reflects a deep engagement with foundational and applied aspects of computational mathematics. His advising and grants activities are not detailed in the text, but his research spans collaborations on topics such as microstructure modeling and numerical algorithms for elastoplasticity. He is based at the Institute of Mathematics, contributing to its research on differential equations and computational engineering.

Jim Jiمشاهده پروفایل

استاد

• Magnetic Resonance Imaging (MRI)
• Biomedical Image Processing
• Constrained Imaging
• +۷ مورد دیگر

Jim Ji is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. His research focuses on advanced MRI and biomedical image processing, including constrained imaging, reconstruction algorithms, cardiac and cancer imaging, and image analysis techniques. He leads the Magnetic Resonance Systems Lab, developing 3D real-time imaging capabilities and automated analysis methods. Education: PhD from University of Illinois at Urbana-Champaign (2003), M.S. and B.S. from Tsinghua University (1997/1993). Active in teaching courses like Signals and Systems, Medical Imaging, and Digital Signal Processing. Supervised over 12 graduate students, including notable alumni like Jong Bum Son and Yuttapong Jiraraksopakun. Developed open-source tools: PULSAR (parallel imaging toolbox) and SID-MRI (dynamic MRI simulation) Received NSF CAREER Award (2008) and Texas A&M travel grants Organized sessions at IEEE EMBS conferences (2003-2005) Recent publications emphasize numerical methods for radiation transport, including diffusion asymptotics, model reduction, and parallel computing solutions. His work bridges computational physics with medical imaging, addressing challenges in dynamic biological imaging and radiation-hydrodynamics coupling.

Anastasios Kyrillidisمشاهده پروفایل

دانشیار

• Optimization for Machine Learning
• Convex and Non-convex Algorithms
• Large-Scale Optimization
• +۳ مورد دیگر

Overview Anastasios Kyrillidis (Tasos) is a Noah Harding Associate Professor in the Department of Computer Science at Rice University, part of the George R. Brown School of Engineering. His research focuses on optimization for machine learning, convex/non-convex algorithms, and large-scale systems, with recent ventures into quantum computing and bioinformatics applications. He has held postdoctoral positions at IBM T.J. Watson Research Center and the University of Texas at Austin, following his PhD in Computer Science from EPFL (Switzerland). Education PhD, School of Computer and Communication Sciences, EPFL (2014) MSc, Electrical and Electronics Engineering, Technical University of Crete (2010) Diploma, Electrical and Electronics Engineering, Technical University of Crete (2008) Research Interests His work bridges optimization theory and practical machine learning systems. Key areas include: Designing efficient algorithms for high-dimensional problems Quantum computing applications in optimization Protein structure prediction via 3D Patterson maps (CrysFormer framework) Federated learning and distributed systems Neural network compression and acceleration Awards & Funding Recipient of NSF CAREER Award, Amazon Research Award, and Welch Foundation Grant. His research is supported by NSF, Amazon, Microsoft, and Intel collaborations. Notable recognitions include the Goldstine Fellowship (IBM) and Simons Foundation Postdoctoral Fellowship. Teaching Instructs advanced optimization courses (COMP 545, COMP 414) and Algorithmic Thinking (COMP 182). Focuses on connecting theory with real-world machine learning challenges. Labs & Teams Leads the OptimaLab at Rice, advancing research in optimization, quantum algorithms, and bioinformatics. Collaborates with interdisciplinary teams on projects like AI-OWLS and CrysFormer protein modeling.

• Thermoacoustics
• Combustion Systems
• Fluid Dynamics
• +۳ مورد دیگر

Jonas Moeck is a Professor at the Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU). His research focuses on thermoacoustics, combustion systems, and nonlinear dynamics, with a particular emphasis on understanding and stabilizing combustion instabilities in gas turbine combustors. He has contributed extensively to the study of can-annular combustors, acoustic spectra analysis, and flame transfer functions. His work integrates advanced numerical methods and experimental validation to address challenges in combustion stability and acoustic mode interactions. Recent research explores the dynamics of ammonia-hydrogen blends and autoignition fronts, highlighting innovations in low-carbon fuel systems. Moeck collaborates internationally and has published in top-tier journals like Combustion and Flame and Journal of Fluid Mechanics . Key research themes include: combustion instability mechanisms, thermoacoustic eigenvalue problems, plasma-assisted combustion, and the application of quantum-mechanical methods to fluid dynamics. His studies often address practical engineering challenges in turbine design and emission reduction. Publications span combustion physics, acoustics, and nonlinear systems, with a focus on interdisciplinary approaches to solving complex fluid-thermal interactions. Current work emphasizes predictive modeling of intrinsic oscillations and the development of robust stability criteria for modern combustor configurations.

• Numerical analysis
• Linear algebra
• Optimization
• +۳ مورد دیگر

Michael L. Overton is a Silver Professor of Computer Science and Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds a B.Sc. (Honors) from the University of British Columbia, and M.S. and Ph.D. degrees from Stanford University. Roles: Academic Leadership, Research, Teaching, and Editorial Work Affiliations: Courant Institute, NYU; Editorial Boards of IMA Journal of Numerical Analysis , Numerische Mathematik , and others. Research Interests: Focus on numerical analysis, linear algebra, optimization, and mathematical programming. Notably contributes to nonsmooth optimization, numerical algorithms, and control theory. His work bridges theory and applications, including the Crouzeix conjecture and robust control design. Publications: Overton has authored influential books, including Numerical Computing with IEEE Floating Point Arithmetic , and published extensively on topics like gradient sampling methods, pseudospectra, and eigenvalue optimization. Recent works explore subdifferentiability of matrix functions and optimization algorithms. Awards: SIAM and IMA Fellowships, highlighting his impact on computational mathematics. Advising: Supervised MS students including Wilmer Henao, Allan Kaku, and Anders Skajaa, focusing on nonsmooth optimization and control problems. Professional Activities: Organized conferences, served on boards (e.g., SIAM, FoCM), and contributed to software like HIFOO for control design. Labs/Teams: Active in collaborative projects at the Courant Institute, with interdisciplinary research in electronic structure calculations and semidefinite programming.

Gözde ÖZYURTمشاهده پروفایل

استادیار

• Algebra
• Quaternion Algebras
• Matrix Theory
• +۳ مورد دیگر

Gözde ÖZYURT serves as an Assistant Professor in the Department of Mathematics at Istanbul Beykent University's Faculty of Arts and Sciences since 2024, following her role as Research Assistant at the same institution from 2020-2024. Education: 2018: Doctorate, Yildiz Technical University Institute of Science (Thesis: "Generalized quaternions") 2015: Master's Degree, Yildiz Technical University Institute of Science (Thesis: "Matrix representations of split and semi-quaternions with complex coefficients") 2010: Bachelor's Degree, Yildiz Technical University Faculty of Arts and Sciences Research Focus: Her work centers on advanced algebraic structures, specializing in quaternion variants including generalized quaternions, split quaternions, and semi-quaternions. She investigates matrix representations, linear equation systems, and algebraic properties within non-commutative frameworks, bridging abstract algebra with computational mathematics through rigorous theoretical analysis. Publication Trends: Her 2018-2025 publications reveal a cohesive research trajectory exploring hyperbolic, complex, and generalized quaternion systems. The work progresses from foundational matrix properties (2018-2019) to sophisticated extensions like hyper-dual quaternions (2025), demonstrating methodological evolution in solving non-commutative algebraic problems with applications in mathematical physics and engineering. Administrative Roles: 2025: Quality Commission Membership, Istanbul Beykent University 2022: Quality Commission Measurement and Evaluation Working Group, Istanbul Beykent University

Ctirad Matyskaمشاهده پروفایل

دانشیار

• Mantle dynamics
• Numerical modelling of mantle convection
• Free oscillations
• +۷ مورد دیگر

Ctirad Matyska is an Associate Professor at the Department of Geophysics , Faculty of Mathematics and Physics , Charles University . His career spans over three decades at the same institution, where he has held academic and administrative roles including Head and Deputy Head of the Department of Geophysics, and budgetary responsibility for 23 years. He maintains active involvement in editorial boards (e.g., Studia geophysica et geodaetica ), scientific committees (e.g., EGU Augustus Love medal committee), and institutional advisory boards. Research Focus : Mantle dynamics, viscoelastic Earth response, seismic source modeling, and non-linear geophysical systems Technical Expertise : Numerical modeling, matrix eigenvalue methods, pseudospectral approaches Academic Leadership : 1997–present: Associate Professor; 2006–2014: Department Head; 2020–present: Extended Dean Advisory Board Publications reflect a long-term research program on: Mantle viscosity and convection dynamics Free oscillations and seismic wave propagation Thermal modeling of lithospheric processes Earth's viscoelastic response to geophysical loads Notable scientific awards include recognition by Charles University (1982) and the Czech Literature Fund (1987). He has supervised over 20 PhD and Master's theses, and participated in numerous international collaborative research projects with institutions in the US, Canada, and EU.

Dong Anمشاهده پروفایل

استادیار

• Quantum Computing
• Applied Mathematics
• Quantum Algorithms
• +۳ مورد دیگر

Dong An is an Assistant Professor at the Beijing International Center for Mathematical Research (BICMR) , Peking University, since September 2024. He previously held a QuICS Hartree Postdoctoral Fellowship at the University of Maryland (2021-2024). He earned his Ph.D. in Applied Mathematics (2021) from UC Berkeley under Professor Lin Lin and a B.S. in Computational Mathematics (2016) from Peking University. Research Focus: Quantum algorithms for classical scientific computing problems and classical algorithms for quantum many-body problems. Key Areas: Quantum simulation, Hamiltonian dynamics, quantum linear systems, differential equations, and adiabatic quantum computing. Publications highlight advancements in quantum differential equation solvers, linear combinations of Hamiltonian simulations, and multi-product methods with explicit commutator scaling. His work bridges theoretical quantum computing and practical scientific computation, emphasizing optimal parameter dependence and error bounds. Scientific Awards: Co-Principal Investigator of NSF Robust Quantum Simulation Seed Grant QuICS Hartree Fellowship Bernard Friedman Memorial Prize in Applied Mathematics Multiple honors from Peking University Teaching: Currently instructs graduate-level quantum algorithms courses at Peking University. Previously served as a teaching assistant for differential equations and calculus at UC Berkeley. Service: Organizer for workshops on quantum differential equations and SIAM special sessions. Active reviewer for journals like PRX Quantum and conferences including QIP and TQC.

Nathaniel Johnstonمشاهده پروفایل

دانشیار

• Quantum Information Theory
• Linear Algebra
• Matrix Analysis
• +۳ مورد دیگر

Nathaniel Johnston is an Associate Professor in the Department of Mathematics and Computer Science at Mount Allison University. His research focuses on quantum information theory, linear algebra, matrix analysis, and cellular automata. He is known for contributions to quantum entanglement, operator theory, and the development of the QETLAB toolbox for quantum entanglement research. Education details are not explicitly listed in the provided information. Johnston's research interests span quantum entanglement, matrix analysis, and the mathematical foundations of quantum computing. He has published extensively on topics such as quantum state characterization, tensor analysis, and cellular automata dynamics. His work often bridges abstract mathematical concepts with computational applications, as seen in his development of algorithms for quantum information processing. In his recent publications, he explores advanced techniques in quantum entanglement certification, spectral graph theory, and the application of linear algebra to quantum systems. His contributions include studies on matrix factorizations, operator norms, and the theoretical underpinnings of quantum coherence. No scientific awards are listed in the provided materials. Johnston has advised no students listed here, and no grants are mentioned. His work is primarily theoretical, with a focus on advancing mathematical frameworks in quantum information science. He is associated with the Department of Mathematics and Computer Science at Mount Allison University, contributing to both research and teaching in these fields.

• Numerical Linear Algebra
• High Performance Computing
• Machine Learning
• +۵ مورد دیگر

Ren-Cang Li is a Professor of Mathematics at The University of Texas at Arlington, with affiliations including past roles as Chair Professor at Hong Kong Baptist University and positions at University of Kentucky and Oak Ridge National Laboratory. He earned his BS from Xiamen University (1985), MS from Chinese Academy of Sciences (1988), and PhD in Applied Mathematics from UC Berkeley (1995). His research focuses on numerical linear algebra, eigenvalue problems, high-performance computing, and machine learning, with impactful contributions like secular equation solvers in LAPACK and work on matrix equations. Awards include the NSF CAREER Award (1999) and multiple distinguished paper awards. Education: PhD in Applied Mathematics, UC Berkeley, 1995 MS in Computational Mathematics, Chinese Academy of Sciences, 1988 BS in Computational Mathematics, Xiamen University, 1985 Research interests span numerical algorithms, eigenvalue computations, optimization, and applications in machine learning. Notable contributions include methods for large-scale eigenvalue problems, matrix equations, and compressed sensing. His work on the secular equation solver is foundational in scientific computing libraries. Key Awards: Friedman Memorial Prize in Applied Mathematics (1996) NSF CAREER Award (1999) Three Distinguished Paper Awards from ICCM (2018-2020) INFORMS Data Mining Best General Paper (2019) Advising and Grants: Supervised numerous PhD students and led federally funded projects totaling over $5M, including work on probabilistic graph learning and nonlinear matrix equations. Active in editorial roles for journals like SIAM Journal on Matrix Analysis and Mathematical Communications .

Tyler McMillenمشاهده پروفایل

استاد

• Dynamical Systems
• Differential Equations
• Mathematical Biology
• +۳ مورد دیگر

Tyler McMillen is a Professor in the Department of Mathematics at California State University, Fullerton. He holds a PhD from the University of Arizona, an MS from Utah State University, and a BA from the University of Utah. His research focuses on applying dynamical systems and differential equations to biological problems, including anguilliform swimming mechanics, decision-making processes, and whip dynamics. He also explores purely mathematical areas like spectral theory and matrix analysis. Education: PhD, University of Arizona MS, Utah State University BA, University of Utah Research Interests: Mathematical Biology: Modeling fish locomotion and neuromechanical systems Spectral Theory: Asymptotics of matrices and operators Decision-Making: Hebbian learning and multi-alternative models Fluid Dynamics: ABC flows and wave propagation His recent work emphasizes spectral asymptotics for matrices, nonlinear muscle modeling in swimming, and the interplay between signal acuity and decision-making. Articles consistently bridge pure mathematics with applied problems in biology and physics. No awards are listed, though his publications reflect sustained academic engagement. He advises no listed students and has no grants explicitly mentioned. His research is conducted within the Department of Mathematics, with collaborations noted in biomechanics and mathematical physics.