جست‌وجوی استاد و پژوهشگر

• Arithmetic Geometry
• Algebraic Geometry
• Representation Theory
• +۴ مورد دیگر

Prof. Peter Scholze is a leading mathematician at the University of Bonn and the Max Planck Institute for Mathematics (MPIM). He holds a professorship in the Mathematical Institute at the University of Bonn and is affiliated with MPIM as a principal investigator. His research focuses on arithmetic geometry, representation theory, and p-adic geometry, with notable contributions to perfectoid spaces, prismatic cohomology, and the geometrization of the local Langlands correspondence. Scholze has organized several advanced seminars (ARGOS) and taught courses on topics like condensed mathematics, p-adic geometry, and algebraic geometry. He collaborates widely with researchers such as Laurent Fargues and Bhargav Bhatt. His recent work includes groundbreaking papers on wild Betti sheaves, motivic geometrization of the local Langlands correspondence, and the Habiro ring of number fields. Scholze has pioneered the use of prismatic cohomology to unify p-adic Hodge theories and has developed foundational frameworks in derived algebraic geometry. His research also intersects with representation theory and topological cyclic homology. Affiliations: University of Bonn (Mathematical Institute), Max Planck Institute for Mathematics (MPIM) Research Groups: Arbeitsgruppe Arithmetische Geometrie und Darstellungstheorie Teaching: Recent courses include 'Selected Topics in Algebraic Geometry' and 'Six-Functor Formalisms'. His work frequently explores connections between algebraic geometry and number theory, with a focus on geometric approaches to arithmetic problems. Scholze is a key figure in modern arithmetic geometry, influencing both foundational theory and applied areas like the Langlands program.

• Applied Topology
• Dynamical Systems
• Algebraic Topology
• +۵ مورد دیگر

Robert Ghrist is the Andrea Mitchell University Professor at the University of Pennsylvania with dual appointments in the Department of Mathematics and the Department of Electrical and Systems Engineering. He serves as Associate Dean for Undergraduate Education for Penn Engineering. His educational background includes a B.S. in Mechanical Engineering from the University of Toledo (1991), and M.S. and Ph.D. degrees in Applied Mathematics from Cornell University (1994, 1995). Ghrist's research bridges pure and applied mathematics, focusing on applied algebraic topology , dynamical systems , and geometric methods in data science. His work extends to network theory, topological data analysis, and computational geometry, with applications spanning robotics, neuroscience, and social dynamics. Key innovations include developing sheaf-theoretic approaches for networked systems and persistence homology techniques for high-dimensional data. Analysis of his recent publications reveals a strong emphasis on lattice-theoretic frameworks , topological robotics , and network dynamics , with emerging applications in neural data interpretation and geometric computing. His research consistently integrates category theory with real-world engineering challenges. Significant scientific recognition includes: Presidential Early Career Award (PECASE, 2004) Scientific American 'Top 50' Research Leader (2007) Mathematical Association of America's Chauvenet Prize (2013) University of Pennsylvania Lindback Award for Distinguished Teaching (2015) DoD National Security Science and Engineering Faculty Fellowship (NSSEFF, 2015) Ghrist leads multiple federally funded research initiatives supported by AFOSR, DARPA, NSF, and ONR. He directs the development of educational tools including the Calculus BLUE/GREEN Project video series and custom GPTs for mathematical pedagogy. His open online courses have reached over 100,000 learners globally.

Johan Taflinمشاهده پروفایل

مدرس

• Complex Dynamics
• Pluripotential Theory
• Dynamical Systems
• +۲ مورد دیگر

Johan Taflin is a Lecturer at the University of Burgundy, affiliated with the Institute of Mathematics of Burgundy (IMB) under the Faculty of Science. His research focuses on complex dynamics in several variables and pluripotential theory, with significant contributions to attracting sets, equidistribution, and moduli spaces of dynamical systems. He has led the ANR DynAtrois project since 2025 and oversees the new Master of Mathematics program launching in 2025. Research Interests: Complex dynamics in projective spaces and several variables Pluripotential theory and its applications Dynamical systems, attractors, and moduli spaces Interdisciplinary links with mathematical physics and algebraic geometry Publications Trends: His work spans complex dynamics, including studies on blenders, postcritically finite maps, and equidistribution toward Green currents. Key subfields include holomorphic endomorphisms, algebraic geometry, and mathematical physics. Leadership: He directs the ANR DynAtrois project (2025–2030) and manages the Dijon Master of Mathematics program, which emphasizes fundamental and applied mathematics with ties to artificial intelligence.

• Topological Data Analysis
• Data Visualization
• Computational Topology
• +۵ مورد دیگر

Bei Wang Phillips is an Associate Professor in the School of Computing and a faculty member at the Scientific Computing and Imaging (SCI) Institute at the University of Utah. She holds a Ph.D. in Computer Science from Duke University and an undergraduate degree from the University of Bridgeport. Her research focuses on Topological Data Analysis (TDA), data visualization, computational topology, and machine learning, with applications in scientific data exploration and analysis. She has received prestigious awards including the NSF CAREER Award (2022) and the PECASE Award (2025). Her work spans projects funded by NSF, NIH, and DOE, including multiparameter TDA and topology-aware data compression. She advises numerous students and collaborates on interdisciplinary initiatives in astrophysics, climate science, and AI fairness. Education: Ph.D. in Computer Science, Duke University (2010) B.S. in Computer Science and Mathematics, University of Bridgeport (2003) Research Interests: Topological techniques for large-scale data analysis Integration of topological, geometric, and machine learning methods Applications in visualization, bioinformatics, and network analysis Key Projects: NSF-funded TDA research (DMS-2301361, OAC-2313124) DOE project on topology-preserving data compression Collaborations with NASA, Argonne National Lab, and Carnegie Institution of Washington Awards: Presidential Early Career Award for Scientists and Engineers (2025) NSF CAREER Award (2022) DOE Early Career Research Program (2020) Advising and Grants: Mentored over 30 students and postdocs Recipient of multiple NSF and DOE grants totaling millions

• General Relativity
• Theoretical Physics
• Black Hole Mechanics
• +۶ مورد دیگر

Harvey Reall is a Professor of Theoretical Physics at the University of Cambridge , affiliated with the Department of Applied Mathematics and Theoretical Physics (DAMTP) and a Fellow of Trinity College . His research focuses on General Relativity and Effective Field Theory , particularly in the context of black hole mechanics , higher-dimensional gravity , and cosmic censorship . He has held prestigious positions including a Royal Society University Research Fellowship from 2005 to 2013. Education: PhD from DAMTP, University of Cambridge. Previous Appointments: Lecturer at the University of Nottingham (2005-2007); Postdoctoral positions at the Kavli Institute (2003-2005), Queen Mary University of London (2000-2003), and University of California, Santa Barbara (2003-2005). Reall's work explores the uniqueness and stability of black holes , causality in gravitational theories , and effective field theory approaches to gravity . His recent publications address nonperturbative second law formulations , event horizon dynamics , and axisymmetry theorems in extended theories of gravity. He has supervised numerous researchers including Aidan McSharry (2025-) , Maxime Gadioux (2022-) , and Iain Davies (2020-24) , contributing to the training of the next generation of physicists. Scientific Awards: Royal Society University Research Fellow (2005-2013)

Jeffrey F. Brockمشاهده پروفایل

استاد

• Low-dimensional geometry
• Hyperbolic geometry
• Topology
• +۳ مورد دیگر

Jeffrey F. Brock is the Dean of the School of Engineering & Applied Science and the William S. Massey Professor of Mathematics at Yale University. He holds the Zhao and Ji Chair in Mathematics. His research focuses on low-dimensional geometry and topology, particularly hyperbolic geometry and its applications to data analysis. He completed his undergraduate studies at Yale and earned his Ph.D. from UC Berkeley. He held positions at Stanford, the University of Chicago, and Brown University, where he chaired the Mathematics Department from 2013 to 2017 and founded Brown’s Data Science Initiative in 2016. He joined Yale in 2018, serving as inaugural Dean of Science in the Faculty of Arts and Sciences until assuming his current role in 2022. He is a Guggenheim Fellow and Fellow of the American Mathematical Society. His research spans hyperbolic 3-manifolds, Teichmüller dynamics, and geometric methods in data science. Notable contributions include work on Thurston’s geometrization program, classification of hyperbolic manifolds, and applications of geometric topology to complex datasets. He co-authored foundational papers on ending laminations, Weil-Petersson geometry, and renormalized volume. His recent work bridges pure mathematics with applied challenges, such as algorithmic detection of medical imaging patterns. Awarded the Guggenheim Fellowship (2008) and AMS Fellow (2017), Brock has also led interdisciplinary initiatives at Brown and Yale. His administrative roles include overseeing engineering, natural sciences, and data science programs. Beyond academia, he co-founded the Vijay Iyer Trio, showcasing his passion for music performance and creativity.

• Quantum Information Theory
• Quantum Many-Body Systems
• Quantum Dissipative Evolutions
• +۲ مورد دیگر

Angela Capel Cuevas is an Assistant Professor of Quantum Information Theory/Theoretical Quantum Computation at the University of Cambridge's Department of Applied Mathematics and Theoretical Physics (DAMTP). Previously, she held a Junior Professorship at Eberhard Karls Universität Tübingen (2021-2024) and was an MCQST Distinguished PostDoc at TU München (2020-2021). Her research focuses on the intersection of quantum information theory and quantum many-body systems, particularly studying thermalization dynamics via quantum functional inequalities. She is a recipient of the Simons Emmy Noether Fellowship and Forbes 30 Under 30 (Spain 2023). Education: PhD in Mathematics, Universidad Autónoma de Madrid/ICMAT (2015-2019) Master in Computational Engineering and Mathematics, URV/UOC (2016-2018) Bachelor in Mathematics, Universidad de Granada (2009-2014) Research: Angela's work applies analytic and geometric tools to quantum systems, emphasizing decay of correlations in Gibbs states and quantum dissipative evolutions. Key topics include entropy inequalities, modified logarithmic Sobolev inequalities, and thermalization rates in spin chains. Her recent work bridges quantum functional analysis with practical implications for quantum computing and thermalization phenomena. Grants & Roles: PI of CRC TRR 352 'Mathematics of Many-Body Quantum Systems' (7M€) Co-PI of QuantERA project TouQan (1.25M€) Organized workshops at BIRS and Tübingen (2023-2024) Awards: Simons Emmy Noether Fellowship (2023) Forbes 30 Under 30 (2023) Vicent Caselles RSME-FBBVA Award (2022) Labs/Teams: Leads a group studying quantum dissipative systems and thermalization at DAMTP. Collaborates with institutions globally on quantum functional inequalities and Gibbs state properties.

Joan Brunaمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Signal Processing
• +۵ مورد دیگر

Joan Bruna is a Full Professor of Computer Science, Data Science, and affiliated Mathematics at New York University's Courant Institute and Center for Data Science. He leads the CILVR group and co-founded the MaD group. His research focuses on mathematical foundations of machine learning, deep learning, signal processing, and their applications in computational science, climate modeling, and geophysics. He holds a Ph.D. in Applied Mathematics from École Polytechnique and has held roles at UC Berkeley, the Institute for Advanced Study, and the Flatiron Institute. Education: Ph.D. (2013) and M.Sc. (2005) in Applied Mathematics, École Polytechnique and ENS Cachan; dual B.Sc./M.Sc. in Telecommunications and Mathematics from UPC Barcelona (2002–2004). Awards include the NSF CAREER Award (2019) and Alfred P. Sloan Fellowship (2018). Research interests span theoretical aspects of deep learning, optimization, and statistical methods, with applications to climate science and inverse problems. He has advised numerous PhD students and postdocs, contributing to seminal work on scattering networks, geometric deep learning, and neural ODEs. Professional service includes editorial roles at TMLR, JMLR, and TPAMI, plus program chairs for major conferences. His work bridges mathematics and machine learning, emphasizing rigorous analysis and real-world impact.

Jelena Grbicمشاهده پروفایل

استاد

• Topology
• Algebra
• Combinatorics
• +۶ مورد دیگر

Professor Jelena Grbic is a distinguished mathematician serving as Professor of Mathematics within the School of Mathematical Sciences at the University of Southampton since 2012. Her academic journey began with a B.Sc. in Mathematics from the University of Belgrade, Serbia in 1997, followed by a Ph.D. in Algebraic Topology from the University of Aberdeen in 2004. Prior to her current position, she held academic appointments at the University of Manchester (2007-2012) as Lecturer and Senior Lecturer, and at the University of Aberdeen (2004-2006) as Lecturer. Professor Grbic's research spans multiple interconnected domains of pure mathematics, with a primary focus on modern homotopy theory, particularly unstable homotopy theory, and its applications across topology, algebra, and geometry. Her work centers on decompositions and exponent problems in homotopy theory, homotopy aspects of Toric Topology, Hopf algebras, and geometric problems related to cobordisms and string topology. This research bridges abstract mathematical theory with potential applications in data science and computational topology. Analysis of her recent publications (2020-2025) reveals a consistent research trajectory in algebraic topology with increasing interdisciplinary connections. Her work demonstrates sophisticated mathematical techniques applied to complex topological structures, particularly moment-angle complexes and polyhedral products. Notably, her 2022 paper 'Aspects of topological approaches for data science' indicates growing interest in applying topological methods to contemporary data analysis problems, suggesting an expanding research horizon beyond pure mathematics. Professor Grbic actively supervises PhD students, including current student Salvatore Elia in Mathematical Sciences, and teaches modules covering algebraic topology and homotopy theory. She serves as a reviewer for prestigious journals including Homology, Homotopy and Application (2019), Transactions of the London Mathematical Society (2021), and The LMS Newsletter (2017), contributing to the scholarly community through peer review and academic service.

Anush Tserunyanمشاهده پروفایل

استاد

• Mathematics
• Ergodic Theory
• Combinatorics
• +۳ مورد دیگر

Anush Tserunyan is a Professor of Mathematics at McGill University, Canada, and a Visiting Professor at the Unit of Pure and Applied Mathematics (UMPA) at École normale supérieure de Lyon from December 1, 2024, to January 31, 2025. She holds a bachelor's and master's in computer science and applied mathematics from Yerevan State University (2005–2007) and a Ph.D. in Mathematics from UCLA (2013), focusing on finite generators for group actions, equivalence relations, and recursive program complexity. Research Interests: Anush Tserunyan specializes in Ergodic Theory , Combinatorics , and Group Actions , with notable contributions to graph theory, hypergraphs, and Ramsey theory. Her work bridges combinatorial structures with analytic methods, particularly in dynamical systems and descriptive set theory. Collaborations: During her visit to UMPA, she collaborates with teams in Geometry, Groups and Dynamics , and Number Theory , alongside researchers like Benjamin Schraen and Sophie Morel. Her stay includes seminars on GGD, Number Theory, and the 'Actions!' working group. Publications: Her research spans topics like ergodic theorems, disjoint matchings in graphs, and algebraic hypergraphs, reflecting a focus on foundational mathematical structures and their applications.

David Bindelمشاهده پروفایل

دانشیار

• Applied Numerical Linear Algebra
• Eigenvalue Problems
• Network Analysis
• +۵ مورد دیگر

David Bindel is an Associate Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences, College of Engineering, and Cornell Ann S. Bowers College of Computing and Information Science. He earned his Ph.D. in Mathematics from the University of California, Berkeley in 2006. His research focuses on applied numerical linear algebra, eigenvalue problems, and their applications in plasma physics, network analysis, and nonlinear systems. He develops methods for analyzing complex systems, including magnetic confinement in stellarators, stability of MHD systems, and community detection in networks. His work bridges theoretical foundations with practical computational tools, such as formal verification of linear algebra algorithms and scalable Gaussian process models. Bindel’s research explores the interplay between structure and computation, leveraging eigenvalue analysis to address challenges in computer vision, opinion dynamics, and engineering design. He has contributed to advancements in numerical methods for large-scale systems, including iterative solvers, spectral approximation techniques, and stochastic optimization. His interdisciplinary approach spans applied mathematics, computer science, and physics, with applications in fusion energy, machine learning, and network science. Recent work highlights include high-order expansions for magnetic confinement, adaptive filtering for dynamical systems, and Bayesian optimization strategies. His publications emphasize rigorous analysis alongside computational scalability, addressing both theoretical and practical aspects of modern scientific computing. Despite no explicitly listed awards, his contributions reflect significant impact in his fields.

Amir Ali Ahmadiمشاهده پروفایل

استاد

• Optimization Theory
• Control Theory
• Dynamical Systems
• +۷ مورد دیگر

Amir Ali Ahmadi is a Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE), with affiliations across multiple disciplines including PACM, Computer Science, Mechanical & Aerospace Engineering, Electrical Engineering, and the Center for Statistics and Machine Learning. He serves as Director of Princeton's Optimization and Quantitative Decision Science Certificate Program and has taken temporary roles at Citadel GQS (2021-2022) and Google Brain (2020-2021). His research bridges optimization theory , dynamical systems , and control theory , focusing on scalable algorithms for complex problems in robotics, autonomous systems, and machine learning. He has pioneered DSOS/SDSOS relaxations as alternatives to traditional sum-of-squares methods, enabling faster solutions through linear/second-order cone programming. Recent publications explore: Higher-order Newton methods for socially responsible investment Data-efficient learning of dynamical systems Computational complexity of local minima Robust-to-dynamics optimization frameworks Award highlights include: 2024 Egon Balas Prize in Optimization 2024 Princeton Engineering Council Teaching Award 2023 Distinguished Teaching Award (Princeton SEAS) 2019 NSF CAREER Award 2017 DARPA Young Faculty Award 2017 Sloan Fellowship in Computer Science He advises prominent researchers like Georgina Hall (Tucker Prize finalist) and Bachir El Khadir (Goldstine Fellow), and leads the Princeton Optimization Seminar and MURI project on Control-Oriented Learning on the Fly.

• Mathematics
• Algebra
• Calculus
• +۴ مورد دیگر

Ivan Melendez is a Professor in the Department of Mathematics at Florida SouthWestern State College. He can be contacted via email at Ivan.Melendez@fsw.edu or by phone at (239) 489-9063 extension 11063, located in building LEE U112K. Research Interests: As a Mathematics professor, his work likely spans foundational and applied mathematical disciplines, including Algebra, Calculus, Geometry, and Statistics. His expertise may also extend to theoretical and computational mathematics.

Paul Larsonمشاهده پروفایل

استاد

• Set Theory
• Topology
• Model Theory
• +۳ مورد دیگر

Paul Larson is a Professor of Mathematics at Miami University. His research focuses on set theory, topology, and model theory, with particular expertise in forcing axioms, descriptive set theory, and infinitary logic. He holds a Ph.D. in Mathematics from the University of California, Berkeley. His work bridges foundational mathematical logic with applications in topology and combinatorics. Key contributions include studies on canonical models under fragments of the Axiom of Choice, polar forcings, and cardinal characteristics. Larson has collaborated extensively with leading researchers such as Saharon Shelah and Jindřich Zapletal. His publications span prestigious journals like the Annals of Pure and Applied Logic and Transactions of the American Mathematical Society. Beyond research, he contributes to the academic community through editorial work and expository writings on historical developments in determinacy theory. Education: Ph.D., Mathematics, University of California, Berkeley Research interests emphasize foundational questions in set theory with applications to topology and model theory. His recent work explores advanced forcing techniques, square principles in Pmax extensions, and combinatorial properties of cardinal invariants. Publications reflect interdisciplinary engagement, including crystal structure prediction in high-pressure chemistry and operator theory in functional analysis. Despite an extensive publication record, no specific scientific awards are documented here. His advising and grant activities remain unspecified in the provided texts. Collaborations span international institutions, reflecting his role as a central figure in contemporary set theory research.

Brett Kolesnikمشاهده پروفایل

پژوهشگر ارشد

• Probability Theory
• Random Structures
• Bootstrap Percolation
• +۴ مورد دیگر

Brett Kolesnik is a Research Fellow in the Department of Statistics at the University of Warwick. His research focuses on probability theory, random structures, bootstrap percolation, and interactions with combinatorics. He has held postdoctoral fellowships at UC Berkeley, San Diego, and the University of Oxford, and was a Senior Demy at Magdalen College. His work includes organizing workshops on bootstrap percolation and collaborating with leading researchers in probability and combinatorics. Education: PhD in Mathematics from the University of British Columbia (advised by Omer Angel). Notable awards include the NSERC Postdoctoral Fellowship and the Florence Nightingale Bicentennial Fellowship in Statistics. Research interests span bootstrap percolation models, random graph dynamics, and stochastic processes. Recent work includes studies on Brownian map geometry, tournament score sequences, and Coxeter group structures. Selected articles explore topics such as critical beta-splitting processes, Catalan percolation, and random walks on algebraic structures. His publications appear in top journals like Electronic Journal of Probability and Annals of Applied Probability . Awards include the Florence Nightingale Fellowship and NSERC Postdoctoral Fellowship. Professional involvement includes organizing the 2024 BIRS workshop on Bootstrap Percolation and contributing to interdisciplinary collaborations in probability and combinatorics.