جست‌وجوی استاد و پژوهشگر

Vadim Olshevskyمشاهده پروفایل

استاد

• Applied Mathematics
• Numerical Analysis
• Structured Matrices
• +۳ مورد دیگر

Vadim Olshevsky is a Professor in the Department of Mathematics at the University of Connecticut, College of Liberal Arts and Sciences. His research focuses on applied mathematics and numerical analysis, with a particular emphasis on structured matrices, linear algebra, and numerical algorithms. He has contributed to the development of fast and stable algorithms for matrix computations, including work on quasiseparable matrices, Schur decompositions, and discrete sine transforms. His research interests include matrix perturbation theory, stability analysis of numerical methods, and efficient algorithms for solving linear systems. He has authored numerous papers on topics such as Jordan canonical forms, Bezoutians, and signal processing applications. Olshevsky’s articles frequently address the stability and efficiency of matrix operations, with applications to signal flow graphs, filter design, and polynomial inversion. His work bridges theoretical mathematics with practical computational methods, emphasizing both algorithmic innovation and rigorous mathematical analysis. No scientific awards are explicitly listed in the provided text. His advising and grants sections remain unspecified, though his extensive publication record suggests significant contributions to the field of numerical linear algebra.

Karl Bringmannمشاهده پروفایل

استاد

• Theoretical Computer Science
• Fine-Grained Complexity
• Algorithm Design
• +۵ مورد دیگر

Karl Bringmann is a Professor at Saarland University since November 2019 and is affiliated with the Max Planck Institute for Informatics, where he works in the Department of Algorithms and Complexity. He has established himself as a leading researcher in theoretical computer science, particularly in fine-grained complexity and algorithm design. His work bridges theoretical insights with practical applications in optimization problems. Bringmann's research focuses on conditional lower bounds (often based on the Strong Exponential Time Hypothesis) and algorithm design, with particular emphasis on optimization problems, string algorithms, and computational geometry. His work has significant implications for fundamental problems like Subset Sum, Knapsack, and Integer Programming, with applications ranging from scheduling to post-quantum cryptography. He develops innovative approaches combining modern algorithmic techniques, mathematical structure theory, and fine-grained complexity to design faster algorithms and establish optimality. His publication record shows a consistent trend toward developing near-optimal algorithms for fundamental problems, with significant contributions to fine-grained complexity theory. His work often establishes tight conditional lower bounds while simultaneously providing matching upper bounds, creating a comprehensive understanding of problem complexity. He has made notable advances in string algorithms (particularly edit distance), geometric problems, and optimization. ERC Starting Grant 2019: Technology Transfer between Integer Programming and Efficient Algorithms (TIPEA) EATCS Presburger Award for Young Scientists 2019 Heinz Maier-Leibnitz-Prize 2019 EATCS Distinguished Dissertation Award 2015 Google European Doctoral Fellowship 2012-2014 Bringmann leads the ERC-funded TIPEA project (2019-2024), which investigates fundamental optimization problems with the goal of developing next-generation industrial solvers. He advises several PhD students including Nick Fischer, Alejandro Cassis, and Vasileios Nakos, and has served on numerous program committees for top theoretical computer science conferences including STOC, FOCS, SODA, and ICALP. His teaching includes advanced courses on Fine-Grained Complexity Theory and Competitive Programming.

Josh Almanمشاهده پروفایل

استادیار

• Algorithm Design
• Complexity Theory
• Matrix Multiplication
• +۴ مورد دیگر

Josh Alman is an Assistant Professor of Computer Science at Columbia University, affiliated with the Theory Group. His research focuses on algorithm design, complexity theory, and algebraic methods in computer science. Previously, he was a Rabin Postdoc in Theoretical Computer Science at Harvard University (2020–2023), and completed his PhD in Computer Science at MIT (2016–2019), advised by Ryan Williams and Virginia Vassilevska Williams. His work bridges foundational questions in algorithms and complexity, with notable contributions to matrix multiplication, lower bounds, and applications of algebraic tools. He advises PhD students at Columbia, including Hengjie Zhang, Kevin Yeo, and Hantao Yu. His notable awards include the Best Paper Award at SOSA 2023, the Machtey Award for Best Student Paper at FOCS 2019, and the Best Student Paper Award at CCC 2019. Alman’s research interests span theoretical computer science, with a focus on algebraic and combinatorial approaches to problems in algorithms, complexity, and machine learning. His recent work explores the limits of fast algorithms for fundamental tasks (e.g., matrix multiplication, kernel density estimation) and their implications for broader computational models. Education PhD in Computer Science, MIT (2019), advised by Ryan Williams and Virginia Vassilevska Williams M.S./B.S. in Computer Science, Stanford University (2014–2016) Key Research Themes Lower bounds for arithmetic circuits and matrix rigidity Algebraic methods for algorithm design Fine-grained complexity and its applications Machine learning theory and attention mechanisms

Richard Mikaël Slevinskyمشاهده پروفایل

دانشیار

• Numerical Analysis
• Scientific Computing
• Spectral Methods
• +۳ مورد دیگر

Richard Mikaël Slevinsky is an Associate Professor in the Department of Mathematics at the University of Manitoba. Originally from Edmonton, Alberta, he previously held a postdoctoral position at the University of Oxford and earned his Ph.D. at the University of Sydney under Sheehan Olver, with co-supervision from Hassan Safouhi and Tony Lau at the University of Alberta. Education: B.Sc. in Engineering Physics, M.Sc. in Applied Mathematics, Ph.D. in Applied Mathematics (University of Alberta) Postdoctoral Experience: NSERC Postdoctoral Research Fellow at University of Oxford His research focuses on numerical analysis, scientific computing, and spectral methods for differential/integral equations. He develops high-precision software in Julia for polynomial transforms and function approximation, available on GitHub . NSERC Discovery Grant (2024–2029) Terry G. Falconer Emerging Researcher Award (2022) Research Manitoba Operating Grant (2019–2021) He teaches courses such as Numerical Analysis, Ordinary Differential Equations, and Spectral Methods for PDEs, emphasizing hands-on learning and open-access resources.

• Numerical Analysis
• Approximation Theory
• Fractional Calculus
• +۳ مورد دیگر

Amin Faghih is a postdoctoral scholarship holder at KU Leuven's Numerical Analysis and Applied Mathematics (NUMA) research unit within the Arenberg campus. His academic work focuses on advanced computational mathematics with emphasis on numerical methods for differential equations and approximation theory. His research interests span Numerical Analysis , Approximation Theory , and Linear Algebra , particularly investigating Sobolev spaces, orthogonal polynomials, and fractional differential equations. His recent work develops novel algorithms for rational approximation and spectral solvers, bridging theoretical mathematics with practical computational implementations. Analysis of his 2024-2025 publications reveals a strong trend toward efficient numerical methods for Sobolev-related problems, with increasing focus on rational Arnoldi approaches and orthogonal polynomial recurrences. His work demonstrates interdisciplinary connections between computational mathematics, linear algebra, and differential equations. Faghih actively contributes to the academic community through seminar presentations at institutions including UC Louvain, KU Leuven, and international conferences such as the International Linear Algebra Society meetings. His research presentations cover specialized topics in numerical linear algebra and approximation theory. Based at Celestijnenlaan 200a (box 2402) in Leuven, he operates within KU Leuven's NUMA research environment, collaborating with prominent researchers including Van Barel, Vandebril, and Van Buggenhout on cutting-edge numerical algorithms.

• Orthogonal Polynomials
• Numerical Analysis
• Approximation Theory
• +۳ مورد دیگر

Elías Manuel María Berriochoa Esnaola is a full-time professor at the University of Vigo , affiliated with the Faculty of Sciences and the Department of Applied Mathematics I . His research is centered on Orthogonal Polynomials , Numerical Analysis , and Approximation Theory , particularly focusing on interpolation techniques and quadrature formulas. Research Themes: Orthogonal polynomials on the unit circle, Hermite-Fejér interpolation, Sobolev orthogonality, asymptotic behavior of polynomials, and numerical quadrature. Collaborations: Extensive work with Alicia Cachafeiro, José M. García-Amor, and Francisco Marcellán. His publications span from 1998 to 2020, addressing problems in interpolation , quadrature rules , and anti-periodic trigonometric systems . A notable contribution is the characterization of Chebyshev orthogonal families and the study of Gibbs-Wilbraham oscillations in interpolation problems. He can be contacted at esnaola@uvigo.es .