جست‌وجوی استاد و پژوهشگر

Hugo Duminil-Copinمشاهده پروفایل

استاد

• Mathematical Physics
• Combinatorics
• Probability Theory

Hugo Duminil-Copin is a Full Professor at the University of Geneva and a permanent professor at the Institut des Hautes Études Scientifiques (IHES) since 2016. His research focuses on Mathematical Physics , Combinatorics , and Probability Theory . Education : École Normale Supérieure (ENS) Paris, University of Paris-Saclay Awards : 2022 Fields Medal for work in statistical physics Research Trends : Probabilistic aspects of lattice models Phase transitions and critical phenomena Conformal invariance and percolation theory Collaborations : Active collaborations with researchers such as R. Panis, S. Goswami, I. Manolescu, and others. Teaching : Offers courses in mathematical physics and probability at the University of Geneva. His recent publications emphasize critical models, random-cluster models, and Gaussian free fields, with applications in planar and high-dimensional systems. He supervises doctoral students including Emile Averous , Aman Markar , and Tiancheng He .

Tobias Müllerمشاهده پروفایل

استاد

• Combinatorics
• Probability Theory
• Random Graphs
• +۵ مورد دیگر

Prof. Tobias Müller is a Professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen. His academic journey includes previous positions at Utrecht University, CWI (Centrum Wiskunde & Informatica), Tel Aviv University, and Eindhoven University of Technology, with a doctorate from the University of Oxford under Colin McDiarmid. His research focuses on combinatorics, probability theory, random graphs, percolation, discrete and stochastic geometry, and combinatorial game theory. He has contributed extensively to understanding complex networks, hyperbolic models, and geometric random structures. Research Interests: Random Graphs and Percolation Theory Discrete and Stochastic Geometry Hyperbolic Network Models Probabilistic Combinatorics Geometric Probability Graph Algorithms and Connectivity Notable Contributions: Analysis of Voronoi and Poisson-Voronoi percolation in hyperbolic planes. Studies on Mallows random permutations and their cycle structures. Research on component games and logical limit laws in graph theory. Investigations into the geometry and properties of random geometric graphs. Grants & Collaborations: Active in organizing workshops and conferences on random graphs and geometric networks, including the BIRS Workshop on Random Geometric Graphs and the STAR Workshops series. Labs/Teams: Member of the Bernoulli Institute’s research groups, focusing on stochastic studies, combinatorics, and algorithmic methods.

• Computational Mathematics
• Geophysical Fluid Dynamics
• Numerical Software
• +۳ مورد دیگر

Prof. David Ham is a Professor of Computational Mathematics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on high-level abstractions for scientific computation, particularly in geophysical fluids and numerical software. He leads the Firedrake project and co-developed the dolfin-adjoint framework, which received the 2015 Wilkinson Prize for Numerical Software. Ham holds a BSc (Mathematics) and LLB from The Australian National University, and a PhD from TU Delft. His career includes roles as a NERC Independent Research Fellow and Grantham Research Fellow at Imperial College. He is affiliated with the Grantham Institute, Mathematics of Planet Earth, and Software Performance Optimisation groups. His research spans computational science, including finite element methods, adjoint-based inversion, and parallel computing. Recent work emphasizes differentiable programming integration with machine learning and geophysical modeling. Ham has contributed to numerous grants and projects, including EPSRC and NERC-funded initiatives. He leads development of software tools like Firedrake and Thetis, advancing computational methods for oceanography and geodynamics.

• Numerical Analysis
• Scientific Computing
• Computational Mathematics
• +۹ مورد دیگر

Dr. Fengyan Li is a Professor in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute (RPI). She holds a PhD in Applied Mathematics from Brown University (2004) and previously held a postdoc at the University of South Carolina. Her research focuses on numerical analysis and scientific computing, particularly discontinuous Galerkin methods for applications in wave propagation, fluid dynamics, plasma physics, and nonlinear optics. She has received prestigious awards including the NSF-CAREER Award (2009) and Alfred P. Sloan Fellowship (2008). Dr. Li serves on editorial boards of journals like SIAM Journal of Numerical Analysis and IMA Journal of Numerical Analysis. Education: PhD in Applied Mathematics (Brown University, 2004); MS & BS in Computational Mathematics (Peking University, 2000 & 1997). Research interests emphasize multi-scale simulations, reduced-order modeling, and high-order methods. Her work addresses challenges in kinetic transport, nonlinear optics, and plasma dynamics. She has delivered plenary talks at major conferences, including ICOSAHOM (2018) and NAHOMCon (2022). Professional service includes leadership roles in the Association for Women in Mathematics (AWM), co-organizing symposiums, and mentoring. She is a 2025 AWM Fellow and advises RPI's AWM Student Chapter.

Xinfeng Gaoمشاهده پروفایل

استاد

• Computational Fluid Dynamics (CFD)
• High-Performance Computing (HPC)
• Shock Waves
• +۷ مورد دیگر

Xinfeng Gao is a Professor of Mechanical & Aerospace Engineering at the University of Virginia, leading the CFD & Propulsion Laboratory. She specializes in high-performance computing (HPC) algorithms for fluid dynamics, combustion, and plasma systems. Her work integrates numerical methods, parallel computing, and data analytics to address complex engineering challenges. Prior to UVA, she held a professorship at Colorado State University from 2011 to 2023, establishing the CFD and Propulsion Lab there. She earned her PhD in Aerospace Engineering from the University of Toronto in 2008, followed by postdoctoral research at Lawrence Berkeley National Laboratory (LBNL). Her research focuses on three core areas: high-order CFD methods for high-speed flows, parallel adaptive algorithms for spatial and temporal domains, and HPC combined with data analytics for aerospace design optimizations. Applications include reduced-order models for turbulence, propulsion device innovation, and quantum computing for fluid simulations. She collaborates with national labs (LLNL, LBNL), aerospace industries (Boeing), and software companies to translate research into practical solutions. Her recent grants include the NSF Mid-Career Advancement Award (2022–2025) for CFD+DA integration in commercial tools and UVA’s RIG Award (2025–2026) for gas-surface material studies under extreme conditions. She teaches MAE 6720 (Computational Fluid Dynamics) and MAE 3420 (Computational Methods). Key awards include the 2023 University of Virginia Research Achievement Award and the 2022 NSF MCA Award. Her work emphasizes cross-disciplinary innovation, blending computational science with experimental validation through initiatives like the Gas-Surface-Materials RIG project, involving experts from MAE, MSE, Chemistry, and Physics.

Marie-Christine Dükerمشاهده پروفایل

استادیار

• high-dimensional statistics
• time series analysis
• functional data analysis
• +۱۱ مورد دیگر

Marie-Christine Düker is an Assistant Professor in the Department of Statistics and Data Science at Friedrich-Alexander University (Germany). Her research focuses on high-dimensional statistics, time series analysis, functional data analysis, and extreme value theory with applications in economics, psychology, chemistry, and ecology. Previously, she was a postdoctoral associate at Cornell University's Department of Statistics and Data Science under David Matteson. She earned her PhD in Mathematics from Ruhr-University Bochum under Herold Dehling and spent part of her doctoral studies at the University of North Carolina at Chapel Hill with Vladas Pipiras. Current Position: Assistant Professor, Department of Statistics and Data Science, Friedrich-Alexander University Previous Academic Affiliation: Postdoctoral Associate, Cornell University Education: PhD in Mathematics, Ruhr-University Bochum; Part-time research at University of North Carolina Research Interests: Her work spans high-dimensional time series under long-range dependence and nonstationarity, discrete data modeling, nonlinear dynamics, dimension reduction, and change-point analysis. Applications include econometrics, neuroscience, chemical data analysis, and ecological forecasting. Recent Publications: Her 2025-2024 work covers Hilbert space-valued linear processes, kernel estimation for nonlinear dynamics, confidence interval approximations, and latent Gaussian count time series. Earlier papers address simultaneous diagonalization, long-run variance matrices, and transition rate estimation challenges. Contact: marie.dueker@fau.de

Sergio Gómez Jiménezمشاهده پروفایل

دانشیار

• Complex Networks
• Complex Systems
• Neural Networks
• +۴ مورد دیگر

Sergio Gómez Jiménez is an Associate Professor in the Department of Computer Engineering and Mathematics at Rovira i Virgili University (URV), Tarragona, Spain. He joined URV in 1995 and has held his current position since 1997. He obtained degrees in Physics (1990) and Mathematics (1995) and a PhD in Physics (1994) from the Universitat de Barcelona. His research focuses on complex networks, including community structure analysis, epidemic spreading, urban congestion, and applications to biology, medicine, and social systems. He has authored over 100 publications in high-impact journals like Nature Methods and Physical Review Letters. He coordinates the interuniversity Master's in Biomedical Data Science and the PhD Program in Bioinformatics. His editorial roles include Associate Editor of Complexity and Review Editor of Frontiers in Physics. Notable awards include the American Physical Society's Outstanding Referee (2015) and the Web Science Trust's Test of Time Award (2024). His work on modeling the spatiotemporal spread of epidemics, such as the 2020 COVID-19 pandemic, has received significant attention. He also contributed to urban traffic congestion analysis and developed algorithms for hierarchical clustering (e.g., MultiDendrograms). Collaborations span institutions like the University of Oxford and CERN, reflecting his interdisciplinary approach to complex systems.

Cody Scarboroughمشاهده پروفایل

استادیار

• Electromagnetics
• Nonlinear Electromagnetics
• Metamaterials
• +۴ مورد دیگر

Cody Scarborough is an Assistant Professor in the Department of Electrical, Computer, and Energy Engineering at the University of Colorado Boulder. He earned his B.S. in Electrical and Computer Engineering from the University of Texas at Austin (2017) and his Ph.D. from the University of Michigan (2022). His research focuses on space-time modulated electromagnetic metamaterials, non-linear electromagnetics, and reconfigurable intelligent surfaces. He leads the Electromagnetic Metamaterials Research Group (EMRG), advancing next-generation communication systems and energy-harvesting technologies. Scarborough has pioneered novel boundary conditions like the interpath relation, reducing computational demands for analyzing space-time periodic structures. His work has earned best student paper awards at EuCAP 2021 and Metamaterials 2021. He teaches courses such as Electromagnetic Fields I and Electromagnetic Metamaterials, mentoring over 100 students. His research group explores parametric amplification, frequency conversion, and bandwidth enhancement through active metamaterial control. Education: B.S. UT Austin (2017), Ph.D. University of Michigan (2022) Labs/Teams: Electromagnetic Metamaterials Research Group (EMRG) Key Contributions: Space-time modulated antennas, N-path network analysis, interpath relation theory He holds provisional patents on parametric time-modulated antennas and efficient computational methods for metamaterial design. Beyond academia, he enjoys music, hiking, and skiing in Boulder's Flatirons.

Erik Van Vleckمشاهده پروفایل

استاد

• Applied Mathematics
• Numerical Analysis
• Numerical Methods for PDEs
• +۳ مورد دیگر

Erik Van Vleck is a Professor of Mathematics at the University of Kansas, affiliated with the College of Liberal Arts & Sciences. His research focuses on applied mathematics, numerical analysis, and numerical methods for partial differential equations (PDEs), with particular emphasis on data assimilation and reaction-diffusion systems. His work bridges theoretical advancements and practical applications in computational dynamics, environmental modeling, and ecological systems. Van Vleck’s research interests include multi-scale data assimilation techniques, adaptive mesh methods, and the numerical analysis of traveling wave solutions in discrete and continuous systems. He has contributed to understanding pattern formation in ecological models, particularly in savanna ecosystems and fire-driven dynamics. His methodological innovations span numerical methods for PDEs, reduced-order modeling, and stochastic filtering approaches for complex systems. His recent work highlights trends in developing robust adaptive frameworks for data assimilation, integrating advanced numerical techniques with real-world applications in geophysics and environmental science. This includes projects on metric tensor-based mesh adaptation and projected data assimilation using sliding window methods. His contributions to theoretical foundations include spectral stability analyses for discrete dynamical systems and bifurcation studies in reaction-diffusion equations. Van Vleck has collaborated extensively across disciplines, applying mathematical tools to ecological, climatological, and biomedical systems. His research often addresses computational challenges in high-dimensional spaces, emphasizing efficiency and accuracy in modeling complex phenomena. Despite the breadth of his contributions, no specific grants or advising roles are detailed in the provided materials.

• Econometrics
• Hypothesis Testing
• Multivariate Models
• +۳ مورد دیگر

Professor Grant Hillier is a Professor of Econometrics within the Department of Economics at the University of Southampton. He is responsible for Senior Recruitment and serves as a Fellow of CeMMAP (Centre for Microdata Methods and Practice), a leading research center in Microeconometrics. Born in Adelaide, Australia, he earned his first degree there before completing his doctoral research at the University of Pennsylvania under a Fulbright Scholarship. His research focuses on inference in structural models, hypothesis testing theory, multivariate models, and financial econometrics, with recent work exploring properties of Wishart matrices. He has held the role of Associate Editor of Econometric Theory (1987–2012) and received the Econometric Theory Plura Scripsit Award in 2008 for significant contributions to econometric theory. His academic career includes extensive contributions to econometric methodology, spatial autoregressive models, and nonparametric testing. He collaborates with researchers such as Raymond Kan and Federico Martellosio on topics like matrix properties and spatial econometrics. His work bridges theoretical advancements and practical applications in econometric analysis. Education: B.A. (Adelaide, Australia), Ph.D. (University of Pennsylvania, USA). Key Roles: CeMMAP Fellow, Editor of Econometric Theory . Awards: Econometric Theory Plura Scripsit Award (2008). Research Areas: Wishart matrices, hypothesis testing, spatial econometrics, structural models. His publications span high-impact journals like Journal of Econometrics and Econometric Theory , addressing foundational econometric challenges and methodological innovations.

Graham Cormodeمشاهده پروفایل

استاد

• Data Sketching
• Streaming Algorithms
• Differential Privacy
• +۷ مورد دیگر

Graham Cormode is a Professor in the Department of Computer Science at the University of Warwick. He has held research positions at Bell Laboratories and AT&T Labs-Research and leads major research projects in algorithms for big data. He is affiliated with the Centre for Discrete Mathematics and its Applications (DIMAP), the Warwick Institute for the Science of Cities (WISC), and the Alan Turing Institute (ATI). His research focuses on the data lifecycle , particularly data sketching , streaming algorithms , and privacy-preserving data analysis . He develops efficient summaries for massive datasets, enabling scalable analytics under constraints of space, time, and privacy. His work spans theoretical algorithm design and practical deployment, especially in differential privacy and distributed systems. His recent publications show a strong trend in private data release , quantile estimation , and interactive proofs for outsourced computation. He frequently publishes in top venues like PODS, VLDB, and STOC, demonstrating sustained impact in database theory and algorithms. Adams Prize, University of Cambridge (2017–2022) Royal Society Wolfson Research Merit Award - Small Summaries for Big Data (2014–2019) Cormode supervises PhD students and postdoctoral researchers, including Pavel Veselý and Michael Shekelyan. He has secured substantial funding from EPSRC, ERC, GCHQ, and industry partners like Microsoft and AT&T. His projects include Small Summaries for Big Data (ERC), FAIR (EPSRC), and ComPaTrIoTs . He is actively involved in organizing workshops and giving keynote talks, such as at PODC and DISC. He leads a research group focused on algorithms for big data , particularly within the ERC-funded “Small Summaries for Big Data” project. The team works on sketching, streaming, and privacy-preserving techniques, often in collaboration with DIMAP, WISC, and the Alan Turing Institute.

• Integrated Nonlinear Optics
• Microwave Photonics
• Neuromorphic Computing
• +۳ مورد دیگر

Susan Sun is a Research Fellow at Swinburne University of Technology's School of Science, Computing and Emerging Technologies. Her research focuses on integrated nonlinear optics, microwave photonics, and neuromorphic computing, with a PhD from Swinburne (2020–present) and prior degrees from Beijing Institute of Technology (Bachelor, 2012–2016) and Beijing University of Posts and Telecommunications (Master, 2016–2019). She has authored 13 SCI journal papers, including high-impact works in Advances in Optics and Photonics and IEEE Journal of Selected Topics in Quantum Electronics , with over 600 citations and an H-index of 9. Her work emphasizes optical microcomb applications in high-speed signal processing, feedback control systems, and neuromorphic computing architectures. Recent contributions include microcomb-based microwave photonic transversal filters, graphene oxide-enhanced nonlinear optics, and ultra-high bandwidth optical neural networks. She is a member of Optica and IEEE, and serves as a reviewer for Optics Continuum . Key research themes include optimizing microcomb-based systems for precision and scalability, exploring novel materials (e.g., graphene oxide) for integrated photonics, and advancing optical solutions for real-time data processing and AI applications. Her lab integrates theoretical modeling with experimental validation, targeting innovations in both discrete and integrated photonic platforms.

• Numerical Probability
• Stochastic PDEs
• Multiscale Modeling
• +۲ مورد دیگر

Charles-Edouard Bréhier is a Professor at the University of Pau and the Pays de l'Adour, specializing in numerical methods for stochastic systems. His research spans stochastic PDEs, multiscale modeling, metastable processes, and geometric numerical integration. He leads investigations into error analysis and computational efficiency for complex stochastic systems. Bréhier's work develops advanced numerical schemes for high-dimensional problems, including splitting integrators, Galerkin approximations, and structure-preserving algorithms. His recent publications focus on error bounds for SPDE discretizations, turbulence modeling, and stochastic geometric mechanics. Articles consistently demonstrate rigorous mathematical foundations for computational methods, with applications spanning fluid dynamics, plasma physics, and optimization. The research provides tools for simulating complex systems with inherent randomness across multiple temporal and spatial scales.

Iris Yoonمشاهده پروفایل

استادیار

• Topological Data Analysis
• Algebraic Topology
• Network Theory
• +۵ مورد دیگر

Iris Yoon is an Assistant Professor of Mathematics at Wesleyan University, specializing in topological data analysis with applications in biological systems. Her research bridges algebraic topology, network theory, and machine learning to address complex problems in neuroscience, cancer research, and genomics. Her research interests focus on developing novel topological methods for analyzing biological data. She creates mathematical frameworks that extract meaningful patterns from high-dimensional datasets, particularly in tumor microenvironments and neural systems. Her work combines persistent homology, relational topology, and multiscale analysis to reveal hidden structures in biological systems. Yoon's publication record shows consistent contributions to computational topology with increasing biological applications since 2018. Her recent work (2023-2025) demonstrates sophisticated applications of topological methods to tumor-immune interactions, neural manifolds, and cellular spatial patterns, reflecting an evolving research trajectory toward complex biological systems. Outside traditional academic publications, Yoon has developed practical applications including a classical musician recommendation system based on collaboration networks using node2vec embeddings, and a COVID-19 hospital bed shortage prediction model. These demonstrate her commitment to applying topological methods to real-world data science problems.

Anne Opschoorمشاهده پروفایل

دانشیار

• Financial Econometrics
• Time Series Econometrics
• Risk Management
• +۳ مورد دیگر

Dr. Anne Opschoor is an Associate Professor in the Department of Finance at Vrije Universiteit Amsterdam and a research fellow at Tinbergen Institute. She holds a PhD from the Tinbergen Institute/Econometric Institute at Erasmus University Rotterdam (2014) and a master's degree in financial econometrics from Erasmus University Rotterdam. Her research focuses on financial econometrics, time series analysis, risk management, and copula models. She has received notable awards, including the NWO VIDI Grant (2021) and the 2014 Journal of Applied Econometrics Dissertation Prize. Her teaching includes courses such as Empirical Finance, Quantitative Research Methods, and Mathematics for Finance. She has supervised PhD theses and contributes to grants like the NWO VIDI-funded project on extreme risks in high dimensions. Her work frequently addresses volatility modeling, tail risk, and multivariate financial dependencies, leveraging computational tools like the R package MitISEM. She has published extensively in journals such as the Journal of Financial Econometrics and Journal of Applied Econometrics.