جست‌وجوی استاد و پژوهشگر

• Mathematical Analysis
• Stochastic Analysis
• Metric Geometry
• +۴ مورد دیگر

Dr. Michael Hinz is a researcher at Bielefeld University's Faculty of Mathematics with significant involvement in multiple research initiatives. He is affiliated with the International Research Training Group 2235 as a participating scientist and contributes to the Collaborative Research Center 1283 (SFB 1283) project titled 'Taming uncertainty and profiting from randomness and low regularity in analysis, stochastics and their applications,' specifically working on subproject A3: 'Analysis of manifolds, metric spaces and graphs.' Additionally, he is a member of the Bielefeld Graduate School in Theoretical Sciences academic staff. His research focuses on the intersection of mathematical analysis and stochastic processes, with particular emphasis on geometric structures. Dr. Hinz maintains offices at locations UHG V4-239 and UHG V3-237 on campus and can be reached through the Faculty of Mathematics secretariat at +49 521 106-4773. Research interests include: Analysis on manifolds and metric measure spaces Stochastic processes in geometric settings Spectral theory on graphs and fractals Dirichlet forms and potential theory Geometric analysis with applications to mathematical physics Dr. Hinz contributes to the 'Mathematical World' strategic research area at Bielefeld University, which develops fundamental mathematical concepts and theories with applications to solve long-standing open problems in economics and the natural sciences. His work aligns with the university's interdisciplinary research culture, particularly connecting mathematical theory with applications in complex systems.

Terence Chi-Shen Taoمشاهده پروفایل

استاد

• Harmonic analysis
• Partial differential equations
• Algebraic combinatorics
• +۵ مورد دیگر

Terence Tao is an Australian-American mathematician and professor of mathematics at the University of California, Los Angeles (UCLA), where he holds the James and Carol Collins Chair in the College of Letters and Sciences. Widely regarded as one of the greatest living mathematicians, Tao has received numerous prestigious awards including the Fields Medal, the Breakthrough Prize in Mathematics, and the MacArthur Fellowship. Dr. Tao's educational background includes: Bachelor's and Master's degrees from Flinders University (1991) Ph.D. from Princeton University (1996) under Elias M. Stein Tao's research spans an extraordinary breadth of mathematical fields. He is particularly known for his work in harmonic analysis, partial differential equations, combinatorics, and analytic number theory. His research has included groundbreaking contributions to compressed sensing, the Green-Tao theorem on arithmetic progressions in prime numbers, and progress on the Navier-Stokes equations. Tao is renowned for his collaborative approach, having worked with over 60 co-authors throughout his career. Tao's publications demonstrate remarkable diversity across mathematical disciplines. His work shows strong trends in connecting seemingly disparate areas of mathematics, often bringing techniques from one field to solve problems in another. He has made significant contributions to both theoretical and applied mathematics, with applications ranging from signal processing to number theory. Among his numerous scientific achievements, Tao has received: Fields Medal (2006) Breakthrough Prize in Mathematics (2014) Royal Medal (2014) MacArthur Fellowship (2006) Crafoord Prize (2012) Princess of Asturias Award (2020) Tao has mentored numerous students throughout his career, with Monica Vișan among his doctoral students. He has secured significant research funding through prestigious awards including the Packard Fellowship, Sloan Fellowship, and Simons Investigator award. His collaborative research has been supported by multiple National Science Foundation grants. Tao maintains an active research group at UCLA and frequently collaborates with mathematicians worldwide. His blog and public lectures have made advanced mathematical concepts accessible to broader audiences, demonstrating his commitment to mathematical education and outreach.

• Dynamical Systems
• Geometric Group Theory
• Rational Billiards
• +۵ مورد دیگر

Alex Eskin is the Arthur Holly Compton Distinguished Service Professor in the Department of Mathematics at the University of Chicago. He has been a faculty member at the University of Chicago since 1999 and is a leading researcher in dynamical systems, geometric group theory, and the theory of moduli spaces. His educational background includes: Undergraduate studies at UCLA PhD from Princeton University (1993) under Peter Sarnak Eskin's research focuses on rational billiards, geometric group theory, and the dynamics of the SL(2,R) action on moduli spaces of translation surfaces. His work bridges several areas of mathematics including ergodic theory, geometry, and number theory. He is particularly known for his breakthrough results on the classification of invariant and stationary measures for the SL(2,R) action on moduli spaces, which led to his Breakthrough Prize in 2020. Eskin's recent publications demonstrate a continued focus on measure rigidity, Lyapunov exponents, and the geometry of moduli spaces. His work often involves deep collaborations with other leading mathematicians such as Maryam Mirzakhani (until her passing), Amir Mohammadi, and others. The research spans from foundational theoretical work to applications in counting problems and effective estimates. His major scientific achievements include: Clay Research Award (2007) for work on quasi-isometric rigidity of solvable groups Fellow of the American Mathematical Society (2012) Election to the National Academy of Sciences (2015) Breakthrough Prize in Mathematics (2020) for classification of P-invariant and stationary measures for the moduli of translation surfaces Eskin has advised several PhD students including Moon Duchin and Simion Filip. His research has been supported by major grants from the National Science Foundation and the Simons Foundation. He has been an influential figure in the field, giving invited talks at the International Congress of Mathematicians in 1998 and 2010. His work has opened new directions in the study of dynamics on moduli spaces and has deep connections to other areas of mathematics. While not explicitly mentioned in the provided texts, Eskin is likely involved in research groups or seminars at the University of Chicago related to geometry, topology, and dynamical systems. His extensive collaboration network suggests active participation in the broader mathematical community through workshops, conferences, and collaborative research projects.

Michael Zaiserمشاهده پروفایل

استاد

• Materials Simulation
• Dislocation Dynamics
• Plasticity
• +۵ مورد دیگر

Professor Michael Zaiser is a distinguished academic at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), where he holds the Chair of Materials Simulation within the Department of Materials Science. Since 2012, he has led research in computational materials science, with prior appointments at the University of Edinburgh where he served as Professor of Mechanics of Materials (2008-2012), Reader (2005-2007), and Lecturer (2001-2005). He maintains significant international collaborations as a Visiting Professor at Imperial College London since 2014 and previously served as Adjunct Professor of Physics at Michigan Technological University (2006-2012). Professor Zaiser's research spans computational materials science with particular expertise in dislocation dynamics, plasticity, fracture mechanics, and hierarchical materials. His work bridges theoretical physics and practical materials engineering, developing innovative computational approaches to understand material behavior at multiple scales. His research group employs advanced simulation techniques including continuum dislocation dynamics, peridynamics, and phase field modeling to investigate fundamental mechanisms of material deformation and failure. Recent work has focused on disordered mechanical metamaterials, hierarchical structures, and the relationship between microstructure and mechanical properties. Analysis of Professor Zaiser's recent publications reveals a strong focus on multiscale modeling approaches that connect atomic-level phenomena with macroscopic material behavior. His work demonstrates increasing integration of machine learning techniques with traditional computational methods, particularly in predicting material failure. The research spans diverse material systems including metals, ceramics, foams, and composites, with consistent emphasis on understanding how microstructural features govern mechanical properties. A notable trend is the investigation of hierarchical and disordered structures to achieve superior mechanical performance. Professor Zaiser leads an active research group at FAU's Department of Materials Science, supervising numerous doctoral students and postdoctoral researchers. His work has been supported by various research grants enabling extensive computational resources and collaborative opportunities with international institutions. His research group maintains strong connections with the Max Planck Society, Fraunhofer Institutes, and Helmholtz Association, reflecting FAU's position as one of Germany's most research-intensive universities. The research laboratory under Professor Zaiser's leadership focuses on computational materials science, with particular emphasis on developing and applying advanced simulation methodologies. The group maintains close collaborations with experimental researchers to validate computational predictions and guide new experimental investigations. Recent work has increasingly incorporated machine learning approaches alongside traditional physics-based modeling to address complex materials challenges.

Yariv Aizenbudمشاهده پروفایل

استادیار

• Statistical Recovery
• Geometric Structures
• Latent Tree Models
• +۴ مورد دیگر

Dr. Yariv Aizenbud is an Assistant Professor in the Department of Applied Mathematics at Tel Aviv University's School of Mathematical Sciences. His academic journey includes a Ph.D. in Applied Mathematics from Tel Aviv University and a Gibbs assistant professorship at Yale University's Applied Math Program. Research Focus: Statistical recovery of geometric structures Applications: Latent tree variable models, Manifold Learning, Randomized Algorithms in Numerical Linear Algebra Academic Roles: Organizes the Applied Math Seminar at Tel Aviv University Contact: Office: 108 Schreiber Building, Department of Mathematics, Tel Aviv University, Israel, 69978.

Niv Buchbinderمشاهده پروفایل

استاد

• Combinatorial Optimization
• Online Algorithms
• Algorithmic Game Theory
• +۲ مورد دیگر

Prof. Niv Buchbinder is a faculty member in the Department of Statistics and Operations Research at the School of Mathematical Sciences, Tel Aviv University. His research centers on algorithmic solutions for combinatorial optimization in offline and online contexts, with significant contributions to primal-dual methodologies and algorithmic game theory. His academic background includes a Ph.D. in Computer Science from the Technion (2008) under Prof. Seffi Naor and an M.Sc. in Computer Science from the Technion (2003) under Prof. Erez Petrank. Key research areas encompass Combinatorial Optimization, Online Algorithms, Algorithmic Game Theory, Primal-Dual Methods, and Submodular Optimization, focusing on competitive analysis for problems like set cover, ad-auctions, and caching. Recent publications (2012-2015) reveal a concentrated effort in submodular optimization and online decision-making, with applications in advertising, resource allocation, and machine learning. These works consistently employ primal-dual frameworks to achieve strong competitive ratios in adversarial settings. Scientific recognition includes: Best Paper Award at ESA 2007 for “Online Primal-Dual Algorithms for Maximizing Ad-Auctions Revenue” Best Paper Award at FOCS 2011 for “A Polylogarithmic Competitive Algorithm for the k-Server Problem” No information is available regarding student advising or research grants. Similarly, details about laboratory facilities, research teams, or future projects are not provided in the source materials.

Oleg Ivriiمشاهده پروفایل

مدرس ارشد

• Complex Analysis
• Conformal Geometry
• Thermodynamic Formalism
• +۳ مورد دیگر

Prof. Oleg Ivrii is a Senior Lecturer in the Department of Theoretical Mathematics at Tel Aviv University's Faculty of Exact Sciences. He earned a B.Sc. in Mathematics from the University of Toronto (2009) and a Ph.D. in Mathematics from Harvard University (2014). Following postdoctoral research positions at the University of Helsinki (2014-2016) and California Institute of Technology (2016-2019), he joined Tel Aviv University in 2019. Research Focus: Complex analysis, conformal geometry, thermodynamic formalism, and geometric function theory. Key Contributions: Studies on analytic mappings of the unit disk, inner functions, quasiconformal homogenization, and Makarov's principle. His work explores critical structures of inner functions, stable convergence, and applications to dynamical systems. He has advised Emanuel Sygal (Master's thesis) and collaborated with Artur Nicolau, Mariusz Urbański, and Vladimir Marković. Ivrii's publications appear in journals like Inventiones Mathematicae , Journal of Differential Geometry , and Analysis & PDE .

Bin Gaoمشاهده پروفایل

دانشیار

• Riemannian Optimization
• Tensor Computation
• Parallel/Distributed Computing
• +۵ مورد دیگر

Bin Gao is an Associate Professor at the Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences. He holds a Ph.D. in Applied Mathematics (2019, University of Chinese Academy of Sciences) and a B.Sc. in Mathematics (2014, Sichuan University). His postdoctoral experience includes positions at UCLouvain (2019-2021) and the University of Münster (2021-2022). Research Interests: Riemannian optimization, tensor computation, parallel/distributed algorithms for orthogonality constraints, machine learning applications. Key Contributions: Development of retraction-free methods on Stiefel manifolds, preconditioned Riemannian algorithms, and geometric frameworks for symplectic eigenvalue problems. Article Trends: Recent work focuses on overcoming the curse of dimensionality via manifold-based optimization, including distributed algorithms for Stiefel manifolds, graph-regularized tensor completion, and second-order methods for symplectic structures. Keywords span numerical analysis, quantum information, and machine learning. Scientific Awards: 2021 Zhong Jiaqing Mathematics Award 2018 Best Student Paper Award (CSIAM) 2018 CAS Special President Scholarship 2017 National Scholarship for Doctoral Students (China) 2016 Honor Student Award (International Workshop on Modern Optimization and Application) Advising & Collaborations: Collaborates with researchers from UCLouvain, University of Münster, and AMSS. Mentors students in Riemannian optimization and tensor computation. Leads the popman research group.

Johannes Krebsمشاهده پروفایل

استاد

• Topological Data Analysis
• Persistent Homology
• Spatial Statistics
• +۳ مورد دیگر

Prof. Dr. Johannes Krebs holds the Chair of Mathematics - Statistics at the Faculty of Mathematics and Geography, Catholic University of Eichstätt-Ingolstadt since 2021. His academic service includes membership on the editorial board of Statistics , the tenure track board, examination committees for B.Sc. Mathematics and Data Science programs, and the faculty council. His research focuses on topological data analysis, persistent homology, and spatial statistics with applications in stochastic geometry. Funded by Deutsche Forschungsgemeinschaft (KR 4977/2-1 and KR 4977/1-1), his work bridges theoretical statistics with computational topology to develop novel methods for analyzing complex data structures. His methodological contributions span wavelet analysis, functional data, and bootstrap techniques for dependent data. Analysis of his recent publications reveals strong emphasis on topological inference (persistent homology, Betti numbers), spatial statistics, and wavelet methods across multiple high-impact journals including Bernoulli , Annals of Statistics , and Stochastic Processes and their Applications . His work demonstrates consistent innovation in applying topological concepts to statistical problems with theoretical guarantees. Academic service highlights include: Editorial Board Member, Statistics journal Member of Tenure Track Board Examination Committee for B.Sc. Mathematics & Data Science Faculty Council Member His research program is supported by significant DFG funding for projects on Topological Data Analysis (2021-2023) and Dynamic Objects on Random Fields (2017-2020), reflecting sustained contribution to advancing statistical methodology through topological frameworks. He maintains active collaborations with researchers at UC Davis, KU Leuven, and Heidelberg University.

Simon Apersمشاهده پروفایل

پژوهشگر

• Quantum Algorithms
• Theoretical Computer Science
• Random Walks
• +۴ مورد دیگر

Simon Apers is a CNRS researcher at IRIF (Institut de Recherche en Informatique Fondamentale) at Université Paris Cité. He focuses on quantum algorithms with broader interests in theoretical computer science, including random walks, graph theory, and combinatorial optimization. His primary research interests span quantum computing, theoretical computer science, and algorithms. Apers has made significant contributions to quantum walks, quantum property testing, graph algorithms, and computational complexity. His work often bridges quantum information with classical theoretical computer science, exploring how quantum techniques can improve classical algorithms and solve problems more efficiently. He has developed quantum speedups for various computational tasks including sampling, optimization, and graph problems. Simon Apers serves as a program committee member for prestigious conferences including TQC'25, QIP'25, TQC'23, SODA'23, and ESA '21. He also serves as an editor for the journal Quantum. His teaching activities include courses at Sorbonne University (2022-present) on Advanced Quantum Algorithms, MPRI (2021-present) on Quantum Algorithms and Complexity, and Bad Honnef (2022). His publication record shows a strong trend toward quantum algorithms for graph problems, quantum walks, and connections between quantum computing and classical theoretical computer science. He frequently collaborates with researchers from various institutions across Europe and has published in top venues including FOCS, STACS, ESA, PRL, and JMLR. Simon Apers actively mentors students and has open positions for PhD students and postdocs in quantum algorithms at IRIF. His research group is involved in cutting-edge work at the intersection of quantum computing and theoretical computer science, with significant contributions to quantum walks, property testing, and computational complexity.