جست‌وجوی استاد و پژوهشگر

Seungchul Baekمشاهده پروفایل

دانشیار

• Machine Learning
• High-dimensional data analysis
• Multivariate analysis
• +۳ مورد دیگر

Seungchul Baek is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). His research focuses on statistical methodologies, including machine learning, high-dimensional data analysis, and functional data analysis. He has contributed to interdisciplinary research, particularly in biomedical applications and computational statistics. Baek holds a Ph.D. in Statistics from the University of South Carolina (2018) and advanced degrees from Yonsei University in Seoul, Korea. **Education:** Ph.D. in Statistics, University of South Carolina, 2018 M.A. in Statistics, Yonsei University, 2005 B.A. in Statistics (minor in Mathematics), Yonsei University, 2001 **Research Interests:** Machine learning and high-dimensional data techniques for classification and variable selection Development of semiparametric and nonparametric statistical models Applications in functional data analysis and biomedical research **Teaching and Service:** Active instructor for courses like Statistical Computing, Probability Theory, and Survey Sampling Associate Editor for *Statistical Analysis and Data Mining* since 2023 **Collaborations:** Interdisciplinary projects in oncology and genomics, such as kinase profiling for acute myeloid leukemia Development of computational tools for statistical inference and classification

Tzu-Chieh Weiمشاهده پروفایل

استاد

• Quantum Information Science
• Condensed Matter Physics
• Tensor-Network Methods
• +۴ مورد دیگر

Tzu-Chieh Wei is a Professor at the C.N. Yang Institute for Theoretical Physics and Department of Physics and Astronomy, Stony Brook University. He received his B.A. and M.Sc. from National Taiwan University, and his Ph.D. from the University of Illinois at Urbana-Champaign. His research focuses on quantum information science, condensed matter physics, and tensor-network methods. He has pioneered work on quantum algorithms, error mitigation, and topological phases, and led the development of Stony Brook's quantum master's program. Affiliations: C.N. Yang Institute for Theoretical Physics, Department of Physics and Astronomy, Stony Brook University Educations: B.A. and M.Sc. in Physics, National Taiwan University Ph.D. in Physics, University of Illinois at Urbana-Champaign (2004) Research Interests: Dr. Wei's work spans quantum information science (quantum computing, entanglement theory, quantum algorithms), condensed matter physics (topological phases, superconductivity, optical lattices), and tensor-network methods (MPS, PEPS). He explores applications in quantum error mitigation, quantum machine learning, and quantum many-body systems. His recent focus includes hybrid quantum-classical algorithms, entanglement detection via Green's functions, and symmetry-enriched topological phases. Articles Trends: Recent publications emphasize quantum error mitigation techniques, topological phases with non-invertible symmetries, and adaptive quantum circuits for state preparation. His work bridges theoretical frameworks (e.g., AKLT models) with experimental implementations on quantum hardware (e.g., IBM quantum computers). Awards: IBM Qiskit Certificates (2020–2023) Online Teaching Certificate (2021) International Quantum Summer Summit Participant (2021) Advising & Grants: Advised PhD students on topics like topological phases and quantum algorithms. Active in grants supporting quantum computing education and microgrid control using quantum systems. Labs & Collaborations: Leads a research group focused on quantum information theory and its applications. Collaborates with institutions like the Perimeter Institute, University of Waterloo (IQC), and IBM Quantum. Involved in outreach initiatives to introduce quantum concepts to high school students and educators.

Yangwen Zhangمشاهده پروفایل

استادیار

• Applied Mathematics
• Scientific Computing
• Numerical Analysis
• +۷ مورد دیگر

Yangwen Zhang is an Assistant Professor in the Department of Mathematics at the University of Louisiana at Lafayette. He holds a Ph.D. in Mathematics from the Missouri University of Science and Technology (2018). Prior to his current position, he completed postdoctoral research at the University of Delaware (2018–2021) and Carnegie Mellon University (2021–2023). His research focuses on applied mathematics, scientific computing, numerical analysis, and data science. Core areas include computational methodologies for partial differential equations (PDEs), hybridizable discontinuous Galerkin (HDG) methods, model order reduction for PDEs, optimal control, inverse problems, and engineering applications. Key research trends in his publications emphasize HDG methods for PDEs, with applications in fluid dynamics, semiconductor devices, and electromagnetic wave propagation. Recent work highlights advancements in divergence-free approximations for Stokes equations, superconvergence analysis of HDG schemes, and spectral methods for thin surface modeling. His academic career includes postdoctoral fellowships at top institutions, reflecting expertise in numerical analysis and computational mathematics. No scientific awards are explicitly mentioned, but his extensive publication record underscores contributions to the field. Advising and grants are not detailed in the provided text, but his research collaborations span institutions like Missouri S&T, Delaware, and CMU. He currently contributes to the Mathematics Department at UL Lafayette, bridging theoretical and applied computational mathematics.

James Robinsonمشاهده پروفایل

استاد

• Mathematics
• Fluid Dynamics
• Turbulence
• +۵ مورد دیگر

James Robinson is a Professor of Mathematics at the University of Warwick, serving as Head of the Department of Mathematics. His research focuses on rigorous fluid dynamics, turbulence, infinite-dimensional dynamical systems, non-autonomous systems, and dimension theory. He has authored multiple influential books including Infinite-dimensional dynamical systems (2001) and Classical theory of the three-dimensional Navier-Stokes equations (2016). His work spans theoretical mathematics with applications to fluid mechanics, particularly addressing the Navier-Stokes equations and turbulence modeling. He has held significant grants, including an EPSRC Leadership Fellowship (2008–2014) and a Royal Society University Research Fellowship (1999–2007). His contributions include advancing understanding of global attractors, bifurcation theory, and functional analysis. Robinson has collaborated extensively, co-authoring works on topics such as non-autonomous systems and mathematical aspects of fluid mechanics. His research bridges pure mathematics and applied fields, with a focus on foundational theory and computational validation.

Wasin Soمشاهده پروفایل

استاد

• Matrix Theory
• Linear Algebra
• Graph Theory
• +۳ مورد دیگر

Wasin So is a Professor of Mathematics at San José State University, where he has been a faculty member since 2006. Previously, he served as an Associate Professor at Sam Houston State University from 1999 to 2000, and as an Assistant Professor there from 1992 to 1999. His academic career includes postdoctoral research positions at the Institute for Mathematics and its Applications at the University of Minnesota (1991-1992) and the Center of Linear Structures and Combinatorics at the University of Lisbon (1993). Dr. So received his educational training at prestigious institutions: Ph.D. in Mathematics from the University of California, Santa Barbara (1991) M.Phil. from the University of Hong Kong (1986) B.Sc. from the University of Hong Kong (1983) Professor So's research spans several interconnected areas within pure and applied mathematics. His primary focus is on matrix theory and linear algebra, with significant contributions to quaternion mathematics, graph theory, and numerical analysis. His work on quaternionic matrices has advanced our understanding of non-commutative algebraic structures, while his research on graph spectra has provided insights into the connections between linear algebra and combinatorics. He has also made notable contributions to the theory of matrix exponentials and inequalities, exploring both theoretical foundations and potential applications. An analysis of Professor So's publication record reveals a consistent focus on advanced matrix theory with particular emphasis on non-standard algebraic structures like quaternions. His work bridges pure mathematical theory with potential applications in physics and engineering. The evolution of his research shows progression from foundational work on matrix exponentials and inequalities in the early 1990s to more specialized investigations of quaternionic structures and spectral graph theory in the late 1990s and early 2000s. Among his professional recognitions: Project NExT Fellow (1994-1995) Professor So has taught a wide range of mathematics courses at San José State University, including the Calculus sequence (MATH 30, MATH 32), Discrete Mathematics sequence (MATH 42, MATH 142, MATH 179, MATH 279A, MATH 279B), Linear Algebra sequence (MATH 129A, MATH 129B, MATH 229, MATH 285), Probability and Statistics sequence (MATH 161A, MATH 163), and Applied Math (MATH 203 CAMCOS Project). His teaching portfolio demonstrates expertise across multiple mathematical disciplines and at various levels from undergraduate to advanced graduate courses, with detailed grade distributions provided for his Calculus III courses across multiple semesters. While specific laboratory or research team information is not provided in the available documentation, Professor So's extensive publication record suggests he has likely mentored students and collaborated with colleagues on research projects in matrix theory and related fields, particularly evident through his long-standing engagement with advanced mathematical concepts across nearly two decades of scholarly work.

• Existence and Stability of Waves
• Fluid Dynamics

Wen Feng is an Assistant Professor in the Department of Mathematics and Statistics at Sam Houston State University's College of Science & Engineering Technology. He holds a B.S. from Beijing Normal University (2012) and a Ph.D. from the University of Kansas (2018). His research focuses on existence/stability of waves and fluid dynamics. He received the AMS-Simons Travel Grant (2022-2024) and Niagara University Summer Research Award (2023). His publications primarily explore magnetohydrodynamics (MHD), stability analysis in fluid systems, solitons, and nonlinear wave equations, with recent work emphasizing fractional dissipation effects in 2D MHD models.

Tim Kuniskyمشاهده پروفایل

استادیار

• Computational Complexity of Statistical Problems
• Spectral Algorithms
• Random Matrix Theory
• +۴ مورد دیگر

Tim Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), with affiliations to the Department of Mathematics, Data Science and AI Institute, and Algorithms and Complexity Group. Previously, he was a postdoctoral associate at Yale University (2021–2024) and earned his PhD in Mathematics from NYU’s Courant Institute (2021), advised by Afonso Bandeira and Gérard Ben Arous. His research focuses on the computational complexity of statistical problems, spectral algorithms, random matrix theory, and convex optimization. He explores topics like the theoretical limits of algorithms, phase transitions in statistical estimation, and applications to data science. Key interests include: Computational thresholds for hypothesis testing Eigenvalue distributions in random matrices Algorithmic lower bounds via sum-of-squares methods Applications to community detection and compressed sensing Recent work includes advancements in low-coordinate-degree algorithms, statistical inference in directed graphs, and spectral methods for tensor analysis. He teaches courses such as Random Matrix Theory in Data Science and Probability Theory II at JHU. Upcoming engagements include workshops on combinatorics and theoretical computer science (BIRS, June 2025) and the COLT conference in Lyon (July 2025). His work bridges mathematics and computer science, addressing foundational questions in high-dimensional statistics and algorithmic efficiency.

Luca Gemignaniمشاهده پروفایل

استاد

• Numerical Analysis
• Linear Algebra
• Structured Matrices
• +۳ مورد دیگر

Prof. Luca Gemignani is a Full Professor of Numerical Analysis at the Department of Computer Science, University of Pisa. His research focuses on structured matrices, eigenvalue computations, nonlinear equations, and symbolic-numeric techniques. He has held academic positions since 1990, including Associate Professor at the University of Pisa's Mathematics Department before transitioning to his current role in 2012. Education: Laurea in Mathematics (University of Pisa, 1987-88). Research Interests: Fast algorithms for structured linear systems, eigenvalue problems, matrix factorizations, and applications in computational mathematics. His work integrates numerical and symbolic methods for solving algebraic and geometric problems. Teaching: Courses include Numerical Analysis, Scientific Computing, and Parallel Computing, taught across multiple academic years. He has contributed to curricula in Mathematics, Computer Science, and Engineering. Publications: Over 50 peer-reviewed articles on numerical linear algebra, matrix computations, and algorithm design. Recent work emphasizes efficient algorithms for quasiseparable matrices and polynomial root-finding.

Anna Maltsevمشاهده پروفایل

مدرس ارشد

• Probability
• Mathematical Physics
• Quantitative Biology
• +۶ مورد دیگر

Dr. Anna Maltsev serves as a Senior Lecturer in Applied Probability and Royal Society University Research Fellow at Queen Mary University of London's School of Mathematical Sciences. She joined Queen Mary in August 2017 after holding positions at Bristol University (2013-2017) and the University of Bonn (2010-2012). Dr. Maltsev earned her Ph.D. from the California Institute of Technology in 2010 under Barry Simon's supervision. Her research spans probability, mathematical physics, quantitative biology, and analysis with specific focus on random matrix theory, calcium signaling in cardiac cells, spectral theory, and quantum graphs. Her doctoral work examined spectral theory of Schrödinger operators on the half-line. Her interdisciplinary publications reveal consistent application of advanced mathematical techniques to cardiac physiology, particularly in understanding calcium signaling mechanisms in pacemaker cells. Dr. Maltsev's work demonstrates how mathematical principles like phase transitions and random matrix theory govern fundamental biological processes in the heart. Her significant research funding includes: Royal Society University Research Fellowship renewal (2023-2026): £370,397 Royal Society Enhanced Research Expenses (2023-2026): £246,000 Royal Society University Research Fellowship (2017-2023): £475,484 Leverhulme Early Career Fellowship (2013-2017) Dr. Maltsev supervises PhD students including Olivia Auster (researching eigenvalue distributions of random matrices) and collaborates with Dr. Mohammed Osman. Her work is conducted within Queen Mary's Centre for Complex Systems, where she bridges mathematical theory with biological applications to advance understanding of cardiac function.

Lev Kantorovichمشاهده پروفایل

استاد

• Theoretical Surface Science
• Atomic Force Microscopy (AFM)
• Scanning Tunnelling Microscopy (STM)
• +۷ مورد دیگر

Professor Lev Kantorovich is a distinguished academic at King’s College London’s Department of Physics, affiliated with the Thomas Young Centre and the Centre for Non-Equilibrium Science (CNES). He holds a Professorship since 2009, progressing from a Lecturer role (2002) and Reader (2005). His academic journey includes a PhD from Latvian University (1975), followed by postdoctoral work at Keele University and University College London. His research focuses on theoretical simulations of solid surfaces, atomic-scale imaging via AFM/STM, and self-assembly of molecular systems. Key areas include non-equilibrium statistical mechanics, quantum transport in nanojunctions, graphene growth kinetics, and embedded-cluster methods for electronic structure calculations. He develops advanced computational techniques like non-equilibrium Green’s functions (NEGF) and kinetic Monte Carlo simulations. Recent work explores photon-assisted phenomena in nanojunctions, photoinduced surface reconstructions, and catalytic applications of molybdenum subnanoclusters. His mathematical contributions span transforms (Laplace, Fourier), linear algebra, and partial differential equations, underpinning interdisciplinary modeling. Affiliated with CNES, he collaborates globally on non-equilibrium science. His research addresses energy, materials, and nanotechnology challenges, with applications in molecular electronics and surface engineering.

• Differential geometry
• Spectral geometry
• Global analysis
• +۵ مورد دیگر

Christian Bär is a Professor of Mathematics at the University of Potsdam, where he maintains an active research program in differential geometry, spectral geometry, and mathematical physics. He serves as editor-in-chief of zbMATH Open and previously held the presidency of the German Mathematical Society (DMV) from 2011 to 2012. His research is supported through major collaborative projects including the IMPRS for Mathematical and Physical Aspects of Gravitation, Cosmology and Quantum Field Theory, the DFG Priority Programme 'Geometry at Infinity,' and the Berlin Mathematical School. Professor Bär's primary research interests focus on differential geometry with particular emphasis on spectral properties of Dirac operators, Lorentzian geometry, spin geometry, and scalar curvature problems. His work bridges pure mathematics and theoretical physics, exploring the deep connections between geometric structures and physical phenomena. His research has made significant contributions to understanding the relationship between Dirac operators and geometric invariants, particularly in the context of index theory and rigidity theorems. His recent publications demonstrate a consistent focus on boundary value problems for Dirac-type operators, scalar curvature rigidity, and Lorentzian geometry. The work shows increasing sophistication in handling non-compact boundaries and developing local versions of classical index theorems. His research group has made notable advances in connecting differential geometry with mathematical physics, particularly in the context of general relativity and quantum field theory on curved spacetimes. President of German Mathematical Society (2011-2012) Editor-in-chief of zbMATH Open Professor Bär is actively involved in multiple third-party funded research projects that form a significant part of his research program. The IMPRS for Mathematical and Physical Aspects of Gravitation, Cosmology and Quantum Field Theory provides a framework for interdisciplinary research, while the DFG Priority Programme 'Geometry at Infinity' supports fundamental research in geometric analysis. His collaboration with the Berlin Mathematical School indicates strong connections with graduate education and mentoring of young researchers.

Thomas Machمشاهده پروفایل

پژوهشگر

• Numerical Linear Algebra
• Data Analysis
• Inverse and Ill-Posed Problems
• +۵ مورد دیگر

Dr. Thomas Mach is a Postdoctoral Researcher at the Institute of Mathematics , University of Potsdam, Germany, focusing on Data Assimilation under Prof. Melina Freitag. His work bridges theoretical and applied numerical linear algebra. Research Interests Numerical linear algebra for large/structured matrices Krylov subspace methods and iterative eigenvalue solvers Adaptive cross approximation and regularization techniques Applications to inverse problems and parametric systems Publication Trends show expertise in matrix eigenvalue problems, regularization for ill-posed equations, and algorithm design for structured systems. Co-authored works with leading experts like David S. Watkins demonstrate methodological innovation. Awards : SIAM Outstanding Paper Prize (2015) for polynomial root-finding research Academic Service includes co-organizing workshops, serving as Associate Editor for numerical analysis journals, and active participation in international conferences like ILAS, SIAM, and GAMM.

Vasilije Perovicمشاهده پروفایل

دانشیار

• Matrix Theory
• Numerical Linear Algebra
• Nonlinear Eigenvalue Problems
• +۵ مورد دیگر

Vasilije Perovic is an Associate Professor in the Department of Mathematics and Applied Mathematical Sciences at the University of Rhode Island's College of Arts and Sciences. He joined the university as an Assistant Professor in 2015 and was promoted to Associate Professor in 2023. Dr. Perovic's primary research focuses on matrix theory and numerical linear algebra, with special emphasis on nonlinear eigenvalue problems. His work extends to applications of linear algebra in data science and the development of scientific algorithms for shared and distributed memory systems. His research bridges theoretical mathematics with practical computational applications, particularly in the areas of matrix polynomials, singular value decomposition, and parallel computing. His publication record demonstrates a consistent focus on numerical methods for matrix computations, with recent work centered on hybrid algorithms for singular value decomposition and properties of matrix polynomials. His research shows a progression from theoretical aspects of matrix polynomials to practical computational algorithms with applications in data science. Dr. Perovic has collaborated extensively with James Baglama and other researchers at the University of Rhode Island. His work on software development includes MATLAB implementations of advanced singular value decomposition algorithms that are publicly available. He earned his Ph.D. in Mathematics from Western Michigan University in 2015, with a dissertation titled 'Spectrally Equivalent Matrix Polynomials: Non-standard Representations and Preservation of Structure,' advised by Professor D. Steven Mackey. He also completed his B.Sc. in Applied and Computational Mathematics at Western Michigan University, graduating Summa cum laude and as a member of Phi Beta Kappa and Pi Mu Epsilon honor societies.

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.

• Nonlinear Partial Differential Equations
• Elliptic and Parabolic PDEs
• Bifurcation Theory
• +۲ مورد دیگر

Yasuhito Miyamoto is a Professor at the Graduate School of Mathematical Sciences , The University of Tokyo since April 2022. Previously, he served as Associate Professor (2013–2022), Lecturer at Keio University (2012–2013), and Assistant Professor at Tokyo Institute of Technology (2007–2012). His research focuses on nonlinear partial differential equations, particularly the structure and properties of solutions to elliptic and parabolic PDEs. Education: PhD (2004) from Graduate School of Mathematical Sciences, The University of Tokyo Master's (2001) from Graduate School of Mathematical Sciences, The University of Tokyo Bachelor's (1999) from Department of Mathematics, Faculty of Sciences, The University of Tokyo Research Interests: His work centers on nonlinear PDEs , exploring phenomena described by elliptic and parabolic equations. Key areas include: Bifurcation Theory: Analyzing solution structures and their changes under parameter variations. Reaction-Diffusion Systems: Investigating pattern formation and stability in models like Gierer-Meinhardt. Morse Index Studies: Examining solution stability through spectral analysis. Recent Publications: His 2024–2025 articles delve into exact solutions , asymptotic formulas , and bifurcation diagrams for critical/supercritical PDEs, spanning topics from Allen-Cahn equations to Gross-Pitaevskii systems. Awards: JSIAM Best Author Prize (2010) MSJ Takebe Katahiro Prize (2010) Young Scientists' Prize, MEXT (2012) Professional Roles: He serves as an editorial board member for journals like Lithuanian Mathematical Journal and has held leadership roles in the Mathematical Society of Japan.