جست‌وجوی استاد و پژوهشگر

Christopher J. Duffyمشاهده پروفایل

استاد

• Groundwater Flow Modeling
• Solute Transport
• Hydrologic Systems
• +۴ مورد دیگر

Christopher J. Duffy is a Professor in the Department of Civil and Environmental Engineering at Penn State University's College of Engineering. He holds a Ph.D. in Hydrology from New Mexico Institute of Mining and Technology and has been affiliated with Penn State since 1989. Education: Ph.D., M.S., and B.S. from New Mexico Institute of Mining and Technology His research focuses on stochastic and numerical modeling of groundwater flow and solute transport, with emphasis on concentration-discharge dynamics, soil moisture modeling, and hydrogeochemical analysis in complex terrain. His work spans multiscale hydrologic systems, including mountain-front recharge, steep terrain snowmelt response, and macroporous hillslope transport. The 15 most recent publications (1991-2004) address nonlinear groundwater systems, land-surface hydrology, and contaminant transport. Key themes include spectral analysis, dynamical modeling, and climate-hydrology interactions, often employing numerical experiments and theoretical frameworks. Scientific Awards: U.S. Naval Achievement Medal with Combat V Duffy teaches courses in fluid mechanics, engineering hydrology, groundwater modeling, and visualization techniques. His professional memberships include the American Geophysical Union, ASCE, and Sigma Xi. No formal advising or grant information is provided in the scraped text.

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +۳ مورد دیگر

Lisa Hartung is a Full Professor (W3) at Johannes Gutenberg University Mainz's Department of Mathematics since 2025, leading research in stochastic processes. She previously held Associate (2019-2025) and Assistant Professor (2019) positions at the same institution and served as a Courant Instructor at NYU's Courant Institute (2016-2018). Her educational background includes a PhD from Bonn University (2016) under Prof. Anton Bovier in Probability Theory. Key research interests span branching Brownian motion , Gaussian free fields , random energy models , and stochastic processes on evolving networks , with applications in statistical mechanics and mathematical physics. Her work frequently addresses extremal processes, phase transitions, and complex temperature phenomena. Analysis of her recent publications reveals dominant trends in log-correlated random fields, variable-speed branching processes, and hard-wall boundary conditions. Articles consistently explore asymptotic behavior, maximum processes, and structural properties of stochastic systems, with increasing interdisciplinary connections to computer science through data stream modeling. Scientific recognition includes: Förderpreis der Fachgruppe Stochastik (2018) Hartung secures substantial research funding as PI for multiple high-impact projects: the ANR-DFG grant Random Energy Models (2020), DFG's SPP 2265 Random Geometric Systems (2020), TRR146 Multiscale Simulation Methods (2020), and Carl-Zeiss project TOPML (2022). She also serves on the executive board of DMV Fachgruppe Stochastik (since 2023) and coordinates Jugend trainiert Mathematik for grades 8/9. Her research group participates in the DFG-funded network Stochastic Processes on Evolving Networks and collaborates internationally through workshops on interacting particle systems and stochastic population models.

Sharif Rahmanمشاهده پروفایل

استاد

• Uncertainty Quantification
• Stochastic Computational Mechanics
• Multiscale Mechanics
• +۷ مورد دیگر

Sharif Rahman is a Professor in the Department of Mechanical Engineering at the University of Iowa's College of Engineering. He also holds a professorship in Applied Mathematical and Computational Sciences and is a Researcher at the Iowa Technology Institute. His work bridges engineering mechanics, applied mathematics, and computational science. His research focuses on the development of advanced computational methods for uncertainty quantification in complex systems. Key areas include stochastic finite-element and mesh-free methods, multiscale mechanics of heterogeneous materials, random eigenvalue problems, and mathematical modeling of random microstructures. These efforts support reliability analysis and safety assessment in engineering systems. Rahman is an active member of several leading professional societies, including the American Society of Mechanical Engineers (ASME), American Society of Civil Engineers (ASCE), U.S. Association for Computational Mechanics (USACM), and the International Society for Computational Engineering & Sciences. His research is conducted through the Sharif Rahman Research Group at the Iowa Technology Institute. He advises graduate students and contributes to interdisciplinary research initiatives involving high-dimensional stochastic systems. His work has applications in structural safety, material science, and computational engineering. Rahman is supported by institutional affiliations that enable collaborative and cross-disciplinary research. The research group operates from the Iowa Technology Institute, leveraging advanced computational resources and interdisciplinary partnerships to advance predictive modeling under uncertainty. The lab fosters innovation in computational mechanics and promotes rigorous mathematical frameworks for engineering analysis.

Arthur Matsuo Yamashita Rios De Sousaمشاهده پروفایل

استادیار

• Applied Mathematics
• Statistical Physics
• Probability Theory
• +۷ مورد دیگر

Arthur Matsuo Yamashita Rios De Sousa is an Assistant Professor in the School of Computing at the Tokyo Institute of Technology, where he conducts interdisciplinary research bridging applied mathematics, statistical physics, and data science. His work focuses on the modeling and analysis of complex systems through stochastic processes, time series analysis, and network science. His primary research interests include: Applied Mathematics and Probability Theory Statistical Physics and Econophysics Complex Systems and Network Science Time Series Analysis and Forecasting Symbolic Dynamics and Entropy-Based Methods Power-Law and Heavy-Tailed Distributions His recent publications demonstrate a consistent focus on developing quantitative methods for analyzing financial time series, sales data, and other real-world complex systems. The articles span topics such as volatility modeling, network-based market analysis, multiscale entropy, and symbolic dynamics, reflecting a strong integration of theoretical physics and practical data science applications. Notable trends in his research include the use of entropy measures for regime detection, modeling of heavy-tailed phenomena in economics, and the application of complex networks to multivariate systems. These efforts contribute to both fundamental understanding and practical tools in econophysics and data-driven science. There are currently no listed scientific awards or honors in the provided text. Dr. Yamashita advises students in computational and quantitative research, particularly in areas related to data analysis of complex systems. While specific grant funding is not mentioned, his sustained publication record suggests active research support. He likely contributes to collaborative projects involving financial data modeling, nonlinear dynamics, and interdisciplinary applications of statistical physics. He is involved in academic events such as the Econophysics Colloquium and workshops at the Complexity Science Hub, indicating participation in international research networks focused on complex systems science.

Ulrich Achatzمشاهده پروفایل

استاد

• Atmospheric Dynamics
• Climate Modeling
• Gravity Waves
• +۶ مورد دیگر

Prof. Ulrich Achatz is a Professor of Atmospheric Dynamics and Climate at the Institute for Atmospheric and Environmental Sciences, Goethe University Frankfurt. He holds a PhD in Astronomy from the University of Bonn (1992) and completed his Habilitation at the University of Rostock (2006). His research focuses on atmospheric dynamics, particularly the interaction between different scales and layers, with applications to climate models and weather forecasting. He has held positions at the Max Planck Institute, Leibniz Institute, and served as Privatdozent at the University of Rostock before joining Goethe University in 2007. Education: 1982–1984: Physics, Ruprecht-Karls-University Heidelberg 1984–1985: Physics (Fulbright Fellowship), University of Nebraska 1985–1992: Physics and Astronomy, University of Bonn (Diploma 1989, PhD 1992) 2006: Habilitation, University of Rostock Research interests include hierarchical modeling, multiscale asymptotics, stochastic modeling, and numerical simulations. His work emphasizes understanding mechanisms underlying atmospheric processes, with contributions to gravity wave dynamics and climate model efficiency. He authored the textbook Atmospheric Dynamics (Springer Spektrum, 2022) and teaches courses in atmospheric dynamics and stochastic methods at both undergraduate and graduate levels. He is a member of the German Meteorological Society, American Meteorological Society, and other scientific organizations. Publications span over 150 peer-reviewed articles, focusing on gravity wave interactions, climate modeling, and parameterization techniques. His work on the MS-GWaM gravity wave parameterization has advanced atmospheric circulation modeling.

Alex Ferrer Ferreمشاهده پروفایل

دانشیار

• Materials Design
• Aeronautical Engineering
• Computational Mechanics
• +۴ مورد دیگر

Àlex Ferrer Ferre is a Serra-Hunter Associate Professor at the Department of Physics, Universitat Politècnica de Catalunya (UPC) in Barcelona, and an associate researcher at the International Center for Numerical Methods in Engineering (CIMNE). His research focuses on topology optimization, computational mechanics, and advanced material design. He holds a PhD in Computational Mechanics from UPC (2017) and has conducted postdoctoral research at the University of Oxford and École Polytechnique. Key roles include teaching courses like Flight Mechanics and leading the Swan project for topology optimization software. His work integrates academic and industrial collaborations with entities like ESA and INEGI. Research interests span multiscale design, topology optimization for additive manufacturing, and fracture mechanics. Major projects include the TopSat CubeSat chassis project (ESA-funded) and the Flexion project on flexible material design. Awards include the Marie Curie Fellowship (2019) and an Excellent Cum Laude PhD. His labs and teams collaborate globally, emphasizing open-source tools and collaborative research.

Mohsen Zayernouriمشاهده پروفایل

دانشیار

• Numerical Analysis
• Uncertainty Quantification
• Fractional LES Turbulence Modeling
• +۵ مورد دیگر

Mohsen Zayernouri is an Associate Professor of Mechanical Engineering at Michigan State University (MSU), leading the Fractional Mathematics for Anomalous Transport and Hydromechanics (FMATH) group within the College of Engineering. His research focuses on developing advanced computational methods to address challenges in fluid dynamics, materials science, and biomedical engineering, leveraging fractional calculus and machine learning. Education : Ph.D., Applied Mathematics, Brown University (2015) Ph.D., Mechanical Engineering, University of Utah (2010) M.Sc., Applied Mathematics, Brown University (2012) M.Sc., Mechanical Engineering, Tehran Polytechnic (2006) Research Interests : Numerical Analysis, Uncertainty Quantification, Fractional PDEs, Anomalous Transport, Turbulence Modeling, and Multiscale Material Failure. His FMATH group specializes in nonlocal modeling, data-driven algorithms, and high-performance computing. Awards : AFOSR YIP Award, ARO YIP Award, ARO MURI, NSF/DOE grants. Grants & Funding : Supported by ARO, AFOSR, NSF, and DOE. Projects include stochastic phase-field modeling for material failure and fractional LES turbulence modeling. Labs/Teams : Leads the FMATH group at MSU, collaborating with institutions globally on computational fluid dynamics, materials science, and biomedical applications.

Elisa Davoliمشاهده پروفایل

استاد

• Calculus of Variations
• Micromagnetics
• Solid-Solid Phase Transitions
• +۴ مورد دیگر

Elisa Davoli is a Professor at TU Wien, affiliated with the Multiscale Calculus of Variations Research Group (E101-01-3). Her work focuses on calculus of variations, micromagnetics, and material science, with applications in phase transitions, nonlocal models, and stochastic homogenization. She collaborates extensively with researchers such as Irene Fonseca, Manuel Friedrich, and Ulisse Stefanelli. Her recent research includes studies on fractional Cahn-Hilliard systems, sharp-interface limits, and optimal control in nonlocal frameworks. She also investigates stochastic homogenization in micromagnetics and structural changes in nonlocal denoising models through bilevel learning. Key contributions include existence results in large-strain magnetoelasticity, two-scale convergence methods for composite materials, and the derivation of linearized fracture models under non-interpenetration constraints. Her work bridges mathematical analysis with applications in solid mechanics and image processing.

• Computational Mechanics
• Numerical Methods (Meshfree/XFEM/Isogeometric)
• Mixed Variational Methods
• +۳ مورد دیگر

Jack HALE is a Research Scientist at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), Department of Engineering. He joined Prof. Stéphane Bordas' team in 2013, focusing on computational mechanics and numerical methods. His work integrates advanced techniques like meshfree methods, XFEM, and isogeometric analysis to address challenges in solid mechanics and high-performance computing. Education : PhD in Aeronautics, Imperial College London (2009-2013), supervised by Dr. Pedro M. Baiz Villafranca. MEng in Engineering, University of Bristol (2004-2008). Research exchange at Rice University (2006-2007) on cross-flow filtration processes. Research Interests : Implicit boundary methods for medical image-based simulations. Development of scalable meshfree/XFEM/isogeometric analysis frameworks. Mixed variational methods to resolve locking phenomena in solid mechanics. High-performance computing for distributed parallel systems. Publications & Software : His 50+ publications emphasize open-access research via ORBilu, with a focus on FEniCSx-based tools (e.g., DOLFINx, FEniCS-shells). Recent work addresses Bayesian model selection, melt instability identification, and SAR data assimilation in aquifer modeling. Collaborations : Open to academic/industrial partnerships in computational mechanics, material science, and biomedical engineering.

• Strength Engineering Methods
• Fatigue Analysis
• Reliability and Robustness in Product Design
• +۳ مورد دیگر

Professor Mårten Olsson holds the Sverker Sjöström Professorship in Reliable Structures at the Royal Institute of Technology (KTH) within the Department of Materials and Structural Mechanics. He has led the department since 2006 and previously held roles such as Director of PhD Education and Head of Solid State Engineering programs. His research focuses on strength engineering methods to enhance product reliability and robustness through advanced fatigue analysis, multiaxial loading models, and probabilistic failure prediction. Olsson has authored over 60 peer-reviewed articles and supervised 14 completed PhD students, contributing to fields like additive manufacturing, engine block dynamics, and composite material behavior. His work integrates computational modeling with experimental validation to address industry challenges in structural reliability and optimization. Research Interests: Olsson’s research spans fatigue analysis, multiaxial loading effects, reliability-based design optimization, and material degradation mechanisms. Notable areas include probabilistic modeling of standing contact fatigue, fracture mechanics in composites, and vibration analysis in engine systems. Key Contributions: His publications highlight advancements in second-order reliability methods, fatigue probability models for complex geometries, and the integration of deformation effects into structural dynamics. Olsson’s methodologies have been applied to aerospace components (e.g., gas turbine blades) and automotive systems (e.g., engine gaskets). Academic Leadership: Beyond research, Olsson has orchestrated educational programs at KTH, including the Master of Science in Engineering Physics, and pioneered competency specialization tracks in Solid State Engineering. His teaching spans undergraduate through doctoral levels, emphasizing practical industrial applications.

Rebecca Brannonمشاهده پروفایل

استاد

• Computational Solid Mechanics
• Plasticity Modeling
• Poroelasticity
• +۷ مورد دیگر

Rebecca Brannon is a Professor in the Department of Mechanical Engineering at the University of Utah, specializing in computational solid mechanics and constitutive modeling. She holds a PhD in Engineering Mechanics from the University of Wisconsin, Madison (1992), an MS from the same university (1988), and a BS in Mechanical Engineering from the University of New Mexico (1987). Her research focuses on improving the accuracy, stability, and efficiency of plasticity models, damage mechanics, poroelasticity, and advanced numerical methods like the Material Point Method (MPM). Key contributions include mesoscale validation techniques, constitutive model development (e.g., ARENA and Kayenta), and tensor analysis frameworks for engineering applications. Her work bridges theory and application, addressing challenges in shock physics, rock mechanics, and biomedical engineering (e.g., ceramic hip prosthesis wear). She has pioneered methods for uncertainty quantification in damage models, verification frameworks for computational mechanics, and visualization tools for tensor fields. Notable projects include modeling hypervelocity impact, blast effects on geologic materials, and analyzing deformation-induced anisotropy in porous media. Brannon has led interdisciplinary initiatives, such as the ONR MURI project on soil blast modeling, and contributed to software development (e.g., CTH and Uintah-MPM). Her expertise spans experimental validation (using XCT data), numerical algorithm design, and thermodynamic consistency in constitutive equations. Her research underscores the importance of rigorous validation and the integration of micro-scale heterogeneity into macro-scale models.

Min Xuمشاهده پروفایل

استادیار

• Machine Learning
• High-dimensional Statistics
• Network Data Analysis
• +۲ مورد دیگر

Min Xu is an Assistant Professor in the Department of Statistics at Rutgers University – New Brunswick. He is affiliated with the School of Arts and Sciences and focuses his research on theoretical and methodological aspects of machine learning and high-dimensional statistics, with applications in network analysis and nonparametric estimation. Education: Ph.D. in Machine Learning, Carnegie Mellon University (2015) B.S. in Electrical Engineering and Computer Science (with minor in Mathematics), UC Berkeley Research Interests: Min Xu’s research lies at the intersection of machine learning , high-dimensional statistics , and network science . He develops computationally scalable methods with strong theoretical guarantees for complex data structures, particularly in nonparametric estimation , network analysis , and large-scale inference . His work addresses fundamental challenges in estimating high-dimensional distributions and understanding the structure of evolving networks, with applications in economics and social sciences. Grants & Funding: NSF Grant DMS-2113671 NSF Grant DMS-2311299 Research Trends: Across his publications, a consistent theme is the development of statistically rigorous methods for high-dimensional and network data. His work spans optimal estimation in stochastic block models, convex M-estimation, and inference on dynamic network structures, with a strong emphasis on theoretical guarantees and practical scalability. Affiliations: Previously, Min Xu served as a departmental postdoctoral researcher in the Statistics Department at the Wharton School, University of Pennsylvania. He is currently based at Hill Center, Rutgers University.

Herbie Leeمشاهده پروفایل

استاد

• Bayesian statistics
• Computer models
• Spatial inverse problems
• +۷ مورد دیگر

Herbie Lee is a Professor in the Department of Statistics at the Jack Baskin School of Engineering, University of California, Santa Cruz. He also serves as the Interim Dean of Social Sciences. His faculty office is located in 537A Baskin Engineering, and he can be reached at 831-459-1655 (though email is preferred). Lee completed his educational journey with a strong foundation in mathematics and statistics: B.S. in Mathematics from Yale University with a specialty in statistics Ph.D. in Statistics from Carnegie Mellon University (1998), advised by Larry Wasserman Post-doctoral work at Duke University's ISDS department Professor Lee's research focuses on Bayesian statistics, particularly in the areas of computer models, spatial inverse problems, simulator emulation, adaptive experimental design, and optimization. His work bridges theoretical statistics with practical applications across diverse domains including cosmology (dark energy modeling), aircraft damage detection, electronic health records analysis, and environmental monitoring. He has made significant contributions to the understanding of neural networks within a statistical framework. An analysis of his recent publications reveals a strong emphasis on Gaussian process modeling, Bayesian optimization, and nonstationary spatial statistics. His work increasingly addresses complex real-world problems requiring sophisticated statistical approaches for mixed data types, constrained optimization, and real-time decision making. The interdisciplinary nature of his research is evident in collaborations with institutions like Lawrence Livermore National Laboratory and the Naval Postgraduate School. Among his notable achievements: Supervised PhD student Bobby Gramacy won the prestigious Savage Award Authored two books on Bayesian optimization and multiscale modeling Published over 50 peer-reviewed articles in top statistics and machine learning journals Professor Lee has supervised numerous graduate students, with his most recent PhD student graduating in 2024. His research has been supported by various grants, though specific funding sources aren't detailed in the provided text. His teaching portfolio includes courses on Bayesian statistics, design and analysis of computer simulation experiments, and statistical methods for biological, environmental, and health sciences. He is actively involved in several professional statistical societies including the International Society for Bayesian Analysis (ISBA), American Statistical Association (ASA), and Institute of Mathematical Statistics (IMS).

• Mathematical Biology
• Mathematical Cell Biology
• Developmental Biology
• +۷ مورد دیگر

Maria-Veronica Ciocanel is an Assistant Professor of Mathematics (Primary) and Biology (Joint) at Duke University's Trinity College of Arts & Sciences, where she has held appointments since 2020. Her research sits at the intersection of mathematical biology, cell biology, and computational modeling, with a particular focus on intracellular transport mechanisms and protein organization in cells. Her research interests include mathematical cell and developmental biology, emergent dynamics in intracellular transport, multiscale modeling of filamentous protein organization, parameter inference for models of biophysics experiments, applied dynamical systems, and applied topological data analysis. She develops mathematical and computational techniques to uncover the basic functions of intracellular proteins in development and injury, with applications to RNA transport in frog oocytes, radial glial cells, and neurons. Her work combines differential equations, stochastic modeling, and topological data analysis to address fundamental questions in cell biology. Her recent publications demonstrate a consistent focus on mathematical approaches to biological problems, particularly in cytoskeleton dynamics, intracellular transport, and symmetry analysis in developmental systems. She also maintains an active research program in social justice applications, particularly analyzing racial disparities in federal sentencing through mathematical and statistical approaches. Lee A. Segel Prize for the Best Paper in the Bulletin of Mathematical Biology (2025) for 'Parameter identifiability in PDE models of fluorescence recovery after photobleaching' Red Sock Award (SIAG/Dynamical Systems) for best poster presentation Ciocanel actively mentors undergraduate students through various research programs including DOmath, PRUV, and Muser. Two of her former students, Anna and Niny, have gone on to become Assistant Professors at University of New Mexico and San Francisco State University, respectively. She has secured significant research funding including grants from the National Science Foundation and Pennsylvania State University for projects on microtubule behavior and membrane-actin cortex adhesion. She is also involved in the RTG: Training Tomorrow's Workforce in Analysis and Applications program as a Co-Principal Investigator. Her laboratory focuses on mathematical modeling of intracellular transport processes, with particular emphasis on microtubule dynamics in neurons and mRNA transport mechanisms. She collaborates extensively with experimental biologists at institutions including Brown University, Penn State University, and Duke University to ensure her mathematical models remain grounded in biological reality.

Alain Destexheمشاهده پروفایل

استاد پژوهشی

• Computational Neuroscience
• Dynamical Systems
• Electrophysiology
• +۵ مورد دیگر

Dr. Alain Destexhe serves as Research Director (DRCE) and Deputy Director of the NeuroPSI Institute (UMR9197) at CNRS, University of Paris-Saclay, and CEA Saclay Campus in France. As Editor-in-Chief of the Journal of Computational Neuroscience , he leads one of the premier publications in his field. His research program spans multiple international collaborations, with funding from major agencies including the ANR, EC, MRC, NIH, and HFSP. Destexhe's research lies at the interface between physics (dynamical systems) and neuroscience (electrophysiology), focusing on the stochastic activity of the cerebral cortex and its consequences on integrative properties and neuronal coding. His work explores collective dynamics of neuronal networks including oscillations and chaotic states, along with the development of computational methods for electrophysiology such as conductance extraction and analysis of multiple electrode recordings. His laboratory maintains an extensive website ( neurocompute.cnrs.fr ) featuring publications, simulations, and 3D morphological reconstructions. The recent publications reveal a strong emphasis on multiscale approaches connecting molecular mechanisms to large-scale brain activity, with particular focus on thalamocortical systems, slow-wave sleep dynamics, consciousness signatures, and the development of advanced neural network models. His work spans from single neuron dynamics to whole-brain modeling, demonstrating a consistent commitment to bridging different levels of neural organization. Destexhe actively participates in the organization of international conferences and summer schools, contributing to the training of the next generation of computational neuroscientists. His laboratory website provides extensive educational resources including simulation demos, computer-generated animations, and NEURON programs that reproduce figures from published articles.