جست‌وجوی استاد و پژوهشگر

• Optimal Transport
• Gradient Flows
• Ricci Curvature
• +۷ مورد دیگر

Prof. Dr. Matthias Erbar is a faculty member in the Faculty of Mathematics at Bielefeld University, where he leads the research group AG Erbar. His work is centered on mathematical analysis, with a focus on optimal transport, gradient flows, and discrete geometric analysis. He is affiliated with the Department of Mathematics and maintains an active research profile with numerous publications in top-tier journals. His research interests lie at the intersection of analysis, probability, and geometry. He investigates optimal transport on discrete and continuous spaces, gradient flow structures in PDEs and stochastic processes, Ricci curvature on metric measure spaces and graphs, and functional inequalities such as Poincaré and logarithmic Sobolev inequalities. His work often employs entropy-based methods and explores the geometric implications of curvature conditions in non-smooth settings. The recent publications show a consistent trend toward understanding geometric and analytic properties of discrete systems, including graphs, Markov chains, and configuration spaces. There is a strong emphasis on formulating continuous concepts like Ricci curvature and gradient flows in discrete settings, enabling applications in probability, statistical mechanics, and numerical analysis. The work frequently involves collaboration with leading researchers in the field. Scientific Awards: No awards explicitly mentioned in the provided text. Prof. Erbar advises doctoral students and leads an active research group (AG Erbar), suggesting engagement in graduate supervision and collaborative research. While specific grants are not listed, his publication output and collaborations imply sustained research funding. He has co-authored papers with researchers across Europe, indicating international collaboration and academic leadership. He is the principal investigator of AG Erbar , a research group within the Faculty of Mathematics at Bielefeld University. The group focuses on analysis and probability, particularly in the context of optimal transport and discrete geometry. The group likely includes PhD students and postdoctoral researchers, contributing to the broader research environment in mathematical analysis at Bielefeld.

• High Performance Computing
• Computational Mechanics
• Materials Science
• +۵ مورد دیگر

Luisa Silva is a Researcher and Professor at Centrale Nantes, affiliated with the GeM Research Institute. She holds a mechanical engineering diploma from Portugal, a Master's in Computational Mechanics, and a PhD from École des Mines de Paris. Her research focuses on high-performance computing (HPC) for computational mechanics, spanning materials science (e.g., fibrous suspensions) and biomedical applications (e.g., heart simulations, tumor growth). She collaborates with institutions like IRT Jules Verne and industries such as Michelin, integrating AI with HPC to develop predictive tools. Her work leverages Centrale Nantes' supercomputing platforms and contributes to regional initiatives like the Connect Talent project. Silva is also involved in education, co-advising PhD students and leading a new joint degree in Big Data and Management with Audencia Business School. Research Interests: High-performance computing, computational mechanics, fluid-solid interactions, numerical methods, and industrial/materials science applications. Her work bridges theoretical advancements with practical implementations in health and engineering sectors. Collaborations & Infrastructure: Works with INRIA, CHU Nantes, and the IRT Jules Verne. Manages a supercomputing platform at Centrale Nantes, part of France's top five HPC facilities. Engages in cutting-edge projects like quantum computing integration and mesh adaptation techniques. Future Directions: Expanding access to supercomputing resources, exploring quantum platforms, and advancing training programs in data science and engineering.

• Mathematical Physics
• Statistical Mechanics
• Quantum Mechanics
• +۳ مورد دیگر

Jerome K. Percus is a Professor of Mathematics and Physics at New York University's Courant Institute of Mathematical Sciences. His office is located in Warren Weaver Hall, Room 501. He holds a Ph.D. in Physics (Columbia University, 1954), an M.A. in Mathematics (1948), and a B.S. in Electrical Engineering (1947), all from Columbia University. His research focuses on interdisciplinary mathematical and physical problems, including exact inequalities for electron systems, dimensional reduction in nanoscale systems, and stochastic processes like self-interacting random walks. These studies bridge high- and low-resolution problem-solving across disciplines. Publications highlight contributions to computational chemistry, nanotechnology, and probability theory. No scientific awards are listed in the provided text. No information on advising or grants is available. Percus's work is anchored at the Courant Institute, though specific lab affiliations are not mentioned.

• Computational Complexity
• Cryptography
• Zero-Knowledge Proofs
• +۵ مورد دیگر

Boaz Barak is a Professor at the Weizmann Institute of Science in the Department of Computer Science, Faculty of Mathematics and Computer Science. With an h-index of 64 and over 17,301 citations, he is a leading researcher in theoretical computer science with significant contributions spanning computational complexity, cryptography, and machine learning theory. His research interests include: Computational Complexity Cryptography Zero-Knowledge Proofs Program Obfuscation Interactive Proofs Privacy-Preserving Computation Machine Learning Theory Barak's publication record reveals a trajectory from foundational work in theoretical computer science to contemporary research at the intersection of theory and practice. His early work established impossibility results for program obfuscation and advanced techniques for zero-knowledge proofs beyond black-box simulation. His influential textbook "Computational Complexity: A Modern Approach" has become a standard reference in the field. More recently, his research has expanded into machine learning phenomena like double descent and scaling laws for language models, demonstrating the evolving nature of his theoretical contributions. His work consistently bridges deep theoretical insights with practical implications for computing. His notable collaborations include extensive work with Sanjeev Arora (77 publications, 6,879 citations), David Steurer (95 publications, 4,945 citations), and Oded Goldreich, among others. Professor Barak leads a research group at the Weizmann Institute focused on theoretical aspects of computer security and complexity theory. His work has been consistently supported by major research funding, enabling significant contributions to the theoretical foundations of computer science. He maintains an active research program with publications spanning over two decades, demonstrating sustained impact in multiple subfields of theoretical computer science.

Wei-Biao Wuمشاهده پروفایل

استاد

• Statistics
• High-Dimensional Inference
• Dependent Data Analysis
• +۱ مورد دیگر

Professor Wei-Biao Wu is a faculty member in the Department of Statistics at the University of Chicago. His research focuses on advancing statistical theory and methodology for high-dimensional data under dependence, addressing critical problems in covariance matrix estimation, regression, and multiple testing where traditional assumptions of independence or light tails fail. Research Focus Establishing theoretical frameworks for high-dimensional inference with dependent observations Developing tools to account for dependence in statistical models Studying deviation and concentration inequalities for dependent random variables Investigating deep Gaussian approximation problems Awards & Recognition Faculty Award for Excellence in Graduate Teaching (2022) Alexander von Humboldt Foundation Research Award (2019) He maintains an active research program and contributes to graduate education at the University of Chicago.

• theoretical computer science
• theoretical foundations of big data analysis
• sublinear algorithms
• +۶ مورد دیگر

Michael Kapralov is an Associate Professor in the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he is part of the Theory Group. His research focuses on theoretical computer science with emphasis on the theoretical foundations of big data analysis. Dr. Kapralov completed his PhD at Stanford iCME under the supervision of Ashish Goel. Following his doctoral studies, he spent two years as a postdoc at the Theory of Computation Group at MIT CSAIL working with Piotr Indyk, and then a year at the IBM T. J. Watson Research Center as a Herman Goldstine Postdoctoral Fellow. His research primarily centers on sublinear algorithms, with specific directions including streaming algorithms, sketching techniques, sparse recovery, and Fourier sampling. Kapralov's work addresses fundamental questions in the theoretical computer science of big data, developing algorithms that can process massive datasets efficiently with limited computational resources. His approach often combines deep theoretical insights with practical considerations for real-world applications of theoretical computer science principles. Analysis of Kapralov's recent publications reveals a strong focus on advancing the state of the art in sublinear-time algorithms, particularly for graph problems and kernel methods. His work demonstrates a consistent trajectory toward developing more efficient algorithms for fundamental computational problems while establishing tight theoretical bounds on what's possible in streaming and sublinear settings. There's a notable emphasis on bridging theoretical computer science with practical machine learning applications, particularly through kernel methods and Fourier analysis techniques. ERC Starting Grant SUBLINEAR (2018-2023) Gene H. Golub Dissertation Award Best paper award in CT at Fully3D 2007 Institute of Physics (IoP) Select article Professor Kapralov has advised numerous PhD students and postdocs who have gone on to successful careers in both academia and industry, including several who now hold faculty positions at prestigious institutions worldwide. He has also received significant research funding, most notably the European Research Council Starting Grant that supported his SUBLINEAR project from 2018 to 2023. Within the EPFL academic community, Kapralov is actively involved in several initiatives including organizing the Turing Course for high school students, leading a reading group on the Foundations of Deep Learning, and participating in the EPFL Theory Seminar series. He also contributes to the broader theoretical computer science community through program committee service for major conferences including SOSA 2023 and STOC 2022.

Guillaume Rabusseauمشاهده پروفایل

دانشیار

• Machine Learning
• Multilinear Algebra
• Tensor Networks
• +۴ مورد دیگر

Guillaume Rabusseau is an Associate Professor at Mila and the Department of Computer Science and Operations Research (DIRO) at Université de Montréal , holding a Canada CIFAR AI Chair since 2019. His research spans machine learning, theoretical computer science, and multilinear algebra. Education : PhD in Computer Science (2016) from Aix-Marseille Université , MSc in Fundamental Computer Science from AMU, BSc in Computer Science (distance learning) from AMU. Research Interests : Tensor methods for machine learning, spectral learning algorithms, connections between weighted automata, tensor networks, and RNNs, low-rank regression, and nonlinear computational models on structured data. Publication Trends : Recent work focuses on tensor train decomposition, temporal graph benchmarks, quantum-inspired ML, spectral regularization, and formal methods for sequence modeling. Collaborative papers address dynamic graphs, foundational models for molecular learning, and high-order pooling in GNNs. Scientific Awards : Canada CIFAR AI Chair (2019–present, renewed) Advising : Supervises PhD students like Maude Lizaire and Pascal Tikeng Notsawo, and MSc students such as Soroush Omranpour. Past advisees include Andy Huang (now at Oxford) and Tianyu Li (Samsung).

Peter Oswaldمشاهده پروفایل

استاد

• Approximation Theory
• Function Spaces
• Applied Harmonic Analysis
• +۷ مورد دیگر

Peter Oswald is a Professor and holds a Bonn Research Chair at the Hausdorff Center for Mathematics, affiliated with the Institute for Numerical Simulation at the University of Bonn. His research lies at the intersection of approximation theory, function spaces, and numerical methods for partial differential equations, with a focus on wavelets, splines, and multiscale computational techniques. University: University of Bonn School: Hausdorff Center for Mathematics Department: Institute for Numerical Simulation Email: oswald@ins.uni-bonn.de His research interests include Approximation Theory, Function Spaces and Applied Harmonic Analysis, Multiscale Methods in Scientific Computing, Numerical Methods for PDEs, Finite Elements, Splines, Wavelets, and Mathematical Modelling. These areas reflect a deep commitment to both theoretical foundations and computational applications in modern applied mathematics. The analysis of his recent publications reveals a consistent focus on iterative and subspace correction methods, preconditioning in sparse grid contexts, function space theory (especially Besov and Hilbert spaces), and the mathematical underpinnings of finite element and wavelet-based discretizations. His work often bridges pure and computational mathematics, with increasing exploration of stochastic and randomized algorithms in numerical linear algebra and high-dimensional problems. Peter Oswald has not been mentioned as having formal advisees in the provided texts, and no scientific awards are listed. However, his extensive collaboration with Michael Griebel and publication in prestigious journals and book series (e.g., Springer Series in Computational Mathematics) underscores his active and influential role in the mathematical community. He is involved in advanced research on space splittings, iterative solvers, and high-dimensional function approximation, contributing to both theoretical developments and practical computational frameworks. His work supports broader efforts in scientific computing, uncertainty quantification, and the numerical solution of complex physical models.

• Statistics
• Probability Theory
• Stochastic Modeling
• +۳ مورد دیگر

María Del Rosario Rodríguez Griñolo is a Full Professor in the Department of Economics, Quantitative Methods and Economic History at Pablo de Olavide University in Spain. Her academic work focuses on Statistics and Operations Research within the School of Economics. Dr. Rodríguez Griñolo earned her PhD from the University of Seville in 2010 with a thesis titled "A new approach to multivariate lifetime distributions based on the excess-wealth concept: an application in tumor growth," supervised by Dr. Franco Pellerey and Dr. José María Fernández Ponce. Her research interests span multiple areas of statistical theory and applications, with particular expertise in stochastic ordering, multivariate analysis, reliability theory, and risk assessment. She has developed significant contributions to the understanding of multivariate aging concepts, excess-wealth functions, and orthant convex-type stochastic orders. Her work demonstrates both theoretical depth and practical applications across diverse fields including financial risk management, cultural heritage preservation, and biomedical research. Analysis of her recent publications reveals a strong focus on advancing theoretical statistical concepts while applying them to real-world problems. Her work shows consistent development in multivariate stochastic orders, with increasing applications in risk assessment, biomedical research, and educational methodologies. The interdisciplinary nature of her research is evident through collaborations across statistics, economics, biology, and cultural heritage fields. Dr. Rodríguez Griñolo has contributed to educational innovation in statistics teaching, particularly through developing web-based resources for secondary students and implementing new assessment methodologies for statistics courses in environmental sciences. Her academic activities include significant contributions to statistics education and public outreach, including the design of educational web resources for secondary students and the development of science communication initiatives related to cultural heritage preservation.

Omri Weinsteinمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Data Structures
• Information Theory
• +۱۲ مورد دیگر

Omri Weinstein is an Assistant Professor in the Department of Computer Science at Columbia University. His research bridges Information Theory, Data Structures, and Optimization, focusing on dynamic data structures and dimensionality-reduction techniques to accelerate optimization and search. He received his PhD from Princeton University and was a Simons Society Junior Fellow at the Courant Institute (NYU). Education: PhD in Computer Science, Princeton University Simons Society Junior Fellowship, Courant Institute (NYU) His work explores the theoretical foundations of data structure lower bounds, communication complexity, and secure computation. Recent research includes advancements in dynamic matrix inversion for linear programming, oblivious near-neighbor search, and the interplay between matrix rigidity and data structure efficiency. Key trends in his publications include: Proving polynomial and super-logarithmic lower bounds for static and dynamic data structures Developing novel techniques in information complexity and protocol compression Applications in parallel algorithms, compressed data structures, and algorithmic game theory Scientific Awards: NSF CAREER Award Simons Society Junior Fellow Best Paper Award at CSR '13 Advising and Grants: Advised PhD students Hengjie Zhang and Shunhua Jiang MsC student Victor Lecomte and postdoc Alexander Golovnev Research funded by NSF CAREER Award on data structure lower bounds Labs and Teams: Omri is affiliated with the Theoretical Computer Science Group at Columbia and leads the Data-Structure Lower Bounds Reading Group.

Edgar Dobribanمشاهده پروفایل

دانشیار

• Statistics
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Edgar Dobriban is an Associate Professor of Statistics and Data Science at the University of Pennsylvania's Wharton School, with a secondary appointment in Computer and Information Science. He leads a research group focused on problems at the interface of statistics, machine learning, and AI. Education PhD in Statistics, Stanford University (2017) BA in Mathematics, Princeton University (2012, Summa cum Laude/with Highest Honors) Research Focus His work spans uncertainty quantification, AI safety, robustness, high-dimensional asymptotic statistics, distributed learning, fairness, and COVID-19 testing methodologies. Current projects include developing conformal prediction methods, jailbreaking robustness benchmarks (JailbreakBench), and safety alignment techniques for large language models. Publication Trends Recent papers predominantly address AI safety and reliability, featuring novel methods for uncertainty quantification in language models (calibration, conformal prediction), adversarial robustness (jailbreaking defenses), and distribution shift adaptation. Theoretical foundations blend with practical applications in high-dimensional statistics. Awards and Honors Peter Gavin Hall IMS Early Career Prize (2024) Sloan Research Fellowship (2023) ICSA Outstanding Young Researcher Award (2023) NSF CAREER Award (2021) AFOSR/Army Research Office YIP Awards (2024, 2023) COPSS Emerging Leader Award (2023) Research Leadership He leads the Wharton Statistics and Data Science research group, recruiting PhD students through Statistics & Data Science, CIS, and AMCS programs. Current projects involve collaborations with Penn Medicine and the NSF-Simons Mathematical and Scientific Foundations of Deep Learning initiative. He co-founded the ASA StatsUpAI Special Interest Group and co-organized the Shenzhen Conference on Random Matrix Theory (2023).

Dmitry Savinمشاهده پروفایل

مدرس ارشد

• Applied Mathematics
• Mathematical Physics
• Random Matrix Theory (RMT)
• +۵ مورد دیگر

Dmitry Savin is a Senior Lecturer in the Department of Mathematics at Brunel University, affiliated with the College of Engineering, Design and Physical Sciences. His research focuses on applied mathematics and mathematical physics , particularly random matrix theory (RMT) applied to quantum and wave chaos in open systems . His work investigates statistical methods for quantifying chaotic behavior in complex systems, with applications in scattering theory , quantum transport , and electromagnetic experiments . Recent publications analyze resonance widths , time-delay matrices , and non-universal features in chaotic cavities . As a PhD supervisor, he explores topics like universal response in open wave systems and RMT applications in digital communications . He collaborates with the BURST research group and contributes to understanding interference effects and collective excitations in disordered media.

KIMURA Taroمشاهده پروفایل

دانشیار

• Mathematical Physics
• Quantum Field Theory
• Algebraic Geometry
• +۳ مورد دیگر

KIMURA Taro is an Associate Professor at the Institute of Mathematics of Burgundy (IMB), University of Burgundy, with a focus on Mathematical Physics and Theoretical Physics. He holds a tenure-track position under the ISITE-BFC initiative and leads research at the intersection of algebraic geometry, quantum field theory, and topological materials. His academic roles include teaching advanced mathematics and physics courses in the International Master in Mathematical Physics (Math4Phys) program. Education: PhD in Physics from the University of Tokyo (2012), MA and BA from the University of Tokyo. Research: Explores quantum field theory, random matrix models, quiver gauge theories, and their applications to topological phases and integrability. Article Trends: His recent work bridges Mathematical Physics with High Energy Physics , emphasizing random partitions , quiver W-algebras , supergroup gauge theories , and topological materials . Publications reveal a focus on quantum integrability, non-perturbative methods, and algebraic structures in gauge theories. Scientific Awards: International Junior Fellowship (2019–2022) JSPS Research Fellowships (2013–2015, 2011–2013) Students & Collaborators: Mentors PhD and Master's students in projects related to quantum field theory and topological materials. Collaborates with researchers at institutions like RIKEN, Keio University, and the University of Tokyo. Leads the Mathematics-Physics team at IMB. Grants: Principal Investigator (PI) for projects including AQGQ (MITI CNRS 2024–2025), CQAG (ANR 2019–2022), and co-PI for SupToPhAG (EUR-EIPHI 2021–2024).

Rémi Monassonمشاهده پروفایل

پژوهشگر

• Statistical Physics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Rémi Monasson is a Researcher at the Laboratoire de physique de l’ENS (LPENS) within the Department of Physics at École normale supérieure (ENS-PSL) in Paris, France. His work bridges Statistical Physics , Machine Learning , Biophysics , and Computational Biology . Active in interdisciplinary research, he contributes to understanding complex systems through theoretical modeling and data-driven approaches. University: École normale supérieure (ENS-PSL) Department: Department of Physics Research Interests: Statistical Physics, Machine Learning, Biophysics, Computational Biology, Neural Networks, Protein Structure Prediction Email: remi.monasson@phys.ens.fr Rémi Monasson's recent publications focus on leveraging Restricted Boltzmann Machines for molecular design, protein sequence modeling , and neural network dynamics . His work spans applications in evolutionary biology , immunology , and biological sequence analysis , often integrating statistical mechanics with machine learning methodologies.

• Noise and Vibration Control
• Acoustic Materials
• Porous Materials
• +۶ مورد دیگر

Raymond Panneton is a Full Professor in the Department of Mechanical Engineering at the University of Sherbrooke, where he has been a faculty member since 1997. He served as Department Director from 2008-2010 and has progressed through the academic ranks from Lecturer to Assistant Professor, Associate Professor, and finally Full Professor. His research focuses on noise and vibration control through advanced material design and characterization. Dr. Panneton earned his B.Eng. (1990), M.Sc.A. (1993), Ph.D. (1996), and Postdoctoral studies (1998), all from the University of Sherbrooke. His educational background in mechanical engineering provided the foundation for his specialization in acoustics and vibration. Dr. Panneton's research interests center on the characterization, modeling, and development of materials for noise and vibration control of mechanical systems in alignment with sustainable development principles. His work spans acoustic materials, porous media, acoustic metamaterials, multi-scale modeling, microtomography, and transfer matrix methods. He has made significant contributions to understanding how material microstructures affect acoustic properties, particularly for sustainable and eco-friendly materials. His recent publications demonstrate a strong focus on acoustic metamaterials, with particular attention to low-frequency noise control, transfer matrix methods, and inverse characterization techniques. His work bridges fundamental acoustic theory with practical applications in aerospace, automotive, and building industries, showing a consistent trend toward increasingly sophisticated material designs for specific noise control challenges. Dr. Panneton has received several prestigious awards including: Best graduate student paper award from the Association canadienne d'acoustique Best graduate student paper award from the American Society of America Médaille des gouverneurs from the University of Sherbrooke Prix Eckel from the Association Canadienne d'Acoustique Dr. Panneton has secured substantial research funding from multiple sources including the Natural Sciences and Engineering Research Council of Canada (NSERC), Fonds de recherche du Québec - Nature et technologies (FRQNT), and industry partners like 3M Canada, Venmar Ventilation, and Bombardier. His current research portfolio includes projects on nonlinear vibrational behaviors of metastructures, characterization of advanced materials' mechanical and acoustic properties, and noise reduction in domestic air intake systems. He has supervised numerous graduate students, with several earning awards for their research. Dr. Panneton is actively involved with the Acoustic Group of the University of Sherbrooke (GAUS) and the Acoustic-Signal-Human Research Center (CRASH-UdeS), fostering interdisciplinary collaboration in acoustics research. His international collaborations include projects with German institutions through the CARIC-ENV-1648 program, focusing on acoustic metamaterials for aerospace applications.