جست‌وجوی استاد و پژوهشگر

• Fluid Mechanics
• Mathematical Modeling
• Environmental Science
• +۳ مورد دیگر

Duncan Farrow is an Adjunct Associate Professor at the School of Physics, Mathematics and Computing, University of Western Australia. His research focuses on fluid dynamics, mathematical modeling, environmental science, biomedical engineering, astrophysics, and hydrology. He has an h-index of 17 and has contributed to over 3 citations in his field. Farrow collaborates globally on projects addressing sustainable development goals, particularly in areas impacting environmental and health sciences. Education details are not explicitly provided in the text. Research interests include free surface flows, porous media interactions, retinal hydrogen dispersal models, and galaxy environment studies. His work often bridges theoretical models with practical applications, such as in biomedical systems and environmental fluid mechanics. Key research areas: Fluid Mechanics, Mathematical Modeling, Environmental Science, Biomedical Engineering, Astrophysics, Hydrology While specific grants or advising roles are not detailed, his publications suggest active engagement in collaborative research projects. Recent studies involve fluid flow into line sinks, hydrogen diffusion in biological tissues, and mid-infrared galaxy properties analysis. No labs or teams are explicitly mentioned in the provided text.

Stefano Martinianiمشاهده پروفایل

استادیار

• Computational and Statistical Physics
• Materials Science
• Artificial Intelligence for Science
• +۵ مورد دیگر

Stefano Martiniani is an Assistant Professor of Physics, Chemistry, Mathematics, and Neuroscience at New York University, affiliated with the Center for Soft Matter Research and the Simons Center for Computational Physical Chemistry. His interdisciplinary research explores computational physics of complex systems, including neural circuit theories, non-equilibrium statistical mechanics, and AI-driven materials discovery. He has pioneered methods for analyzing high-dimensional energy landscapes and received prestigious awards like the NSF CAREER Award (2024) and IUPAP Early Career Prize (2023). Education: PhD in Physics (2017), University of Cambridge MPhil in Physics (2013), University of Cambridge BSc in Physics (2012), Imperial College London Research Interests: His work bridges statistical physics and artificial intelligence, focusing on: Engineering disordered materials with tailored spectral properties Quantifying entropy production in active matter Developing open science frameworks like ColabFit for machine learning interatomic potentials Neural circuit models for cortical communication Grants & Collaborations: Funded by NSF, NIH, Chan Zuckerberg Initiative, and Simons Foundation. Leads interdisciplinary teams in computational physics, AI, and materials science. Labs/Initiatives: Core member of NYU's Center for Soft Matter Research; develops software tools like FReSCo and KLIFF-Torch for computational materials science.

Piya Palمشاهده پروفایل

استاد

• High Dimensional Statistical Signal Processing
• Compressive Sensing
• Tensor Methods
• +۴ مورد دیگر

Prof. Piya Pal is a Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego. Her research focuses on high-dimensional statistical signal processing, energy-efficient sampling techniques, and covariance-driven inference. She previously held an Assistant Professor position at the University of Maryland, College Park, and was affiliated with the Institute for Systems Research. Education: Ph.D. in Electrical Engineering from California Institute of Technology (2013). Notable achievements include the NSF CAREER Award (2016) and the 2014 Charles and Ellen Wilts Prize for her thesis on sparse sampling and estimation. Her work emphasizes structured sampling and robust algorithms for undersampled data analysis, with applications in sensor arrays, compressive sensing, and optical imaging. Research Interests: Energy-efficient sparse array design Correlation-aware sparse estimation Covariance compression and statistical inference Tensor methods in machine learning High-resolution imaging systems Publications highlight advancements in sparse array geometries (nested/coprime samplers), Cramér-Rao bound analysis, and hybrid beamforming. Recent work explores super-resolution imaging and millimeter-wave channel sensing with learned empirical priors. Her contributions address fundamental trade-offs between sample size, resolution, and domain knowledge integration. Scientific Awards: NSF CAREER Award (2016) 2014 Charles and Ellen Wilts Prize (Caltech) Advising & Grants: Current research is supported by NSF CAREER funding. Her lab focuses on interdisciplinary projects combining signal processing with medical imaging and wireless communication challenges.

Daniel Trugmanمشاهده پروفایل

استادیار

• Seismology
• Earthquake Rupture Processes
• Seismic Hazard Analysis
• +۴ مورد دیگر

Dr. Daniel Trugman is an Assistant Professor in the Department of Geological Sciences and Engineering at the University of Nevada, Reno (UNR), affiliated with the Mackay School of Earth Sciences and Engineering. He holds a BS in Geophysics from Stanford University and MS/PhD in Earth Sciences from Scripps Institution of Oceanography at UC San Diego. Previously, he was a Richard P. Feynman Postdoctoral Fellow at Los Alamos National Laboratory (2018–2020) and an Assistant Professor at the University of Texas at Austin (2020–2022). His research focuses on earthquake rupture processes, seismic hazards, and leveraging machine learning and big data in seismology. He leads projects at the Nevada Seismological Laboratory, investigating Nevada seismicity, fault interactions, and earthquake early warning systems. Education: • Ph.D., Earth Sciences, UC San Diego (2017) • M.S., Earth Sciences, UC San Diego (2015) • B.S., Geophysics, Stanford University (2013) Research interests include: Nevada seismicity and tectonics Earthquake source properties (stress drop, radiated energy) Seismic hazard analysis Machine learning for glacier dynamics and seismic monitoring Induced seismicity and fracking impacts Awards: Charles F. Richter Early Career Award (2023) Mousel-Feltner Award for Research Excellence (2023) His work integrates high-fidelity physical modeling with data-driven techniques, including studies on glacier basal sliding, ground motion prediction, and fault network complexity. He teaches courses on Python for Earth Sciences and earthquake engineering. Labs/Teams: Active member of the Nevada Seismological Laboratory and collaborates with the Southern California Earthquake Center (SCEC) and USGS.

Philip J. Reidمشاهده پروفایل

استاد

• Spectroscopy
• Physical Chemistry
• Single-Molecule Photophysics
• +۴ مورد دیگر

Philip J. Reid serves as Professor and Vice Provost for Academic & Student Affairs at the University of Washington's Department of Chemistry. With a Ph.D. from the University of California at Berkeley (1992), he maintains an active research program while holding significant administrative responsibilities within the university structure. Professor Reid's research focuses on molecular photophysics at the single-molecule level, particularly investigating fluorescence intermittency (blinking) , charge transfer processes , and guest-host interactions in various materials systems. His laboratory employs advanced confocal microscopy and femtosecond spectroscopy techniques to study phenomena in semiconductor nanocrystals, polymer matrices, and molecular crystals. Key research areas include understanding the nature of non-emissive states that serve as gateways to material decomposition, temperature-dependent photophysics around polymer glass transitions, and proton transfer mechanisms in crystalline environments. Analysis of Professor Reid's recent publications reveals consistent focus on single-molecule spectroscopy applied to nanomaterials and polymers. His work demonstrates how molecular-scale photophysical measurements can provide insights not obtainable through bulk techniques, particularly regarding environmental effects on photostability and emission properties. The research bridges fundamental physical chemistry with practical applications in photonic materials. Professor Reid has advised numerous graduate students and postdoctoral researchers who have gone on to diverse careers in academia, government, and industry. His laboratory collaborates extensively with other research groups, notably the Gamelin Lab at UW and the Kahr Group at New York University, reflecting the interdisciplinary nature of his work. The Reid Lab operates custom-built confocal microscopy systems designed for single-molecule investigations. Research focuses on chromophore-polymer systems and mixed-crystal materials where single molecules are isolated in well-defined environments. This approach allows precise investigation of molecular photophysics while minimizing complications from oxygen permeability and nonradiative relaxation.

Adrian Sheppardمشاهده پروفایل

استاد

• Materials Physics
• Porous Media Analysis
• X-ray Tomography
• +۳ مورد دیگر

Prof Adrian Sheppard is a Professor in the Department of Materials Physics at The Australian National University (ANU). He holds a B.Sc. (Hons) from the University of Adelaide and a Ph.D. from ANU. His research focuses on porous media analysis, X-ray tomography, and carbon sequestration, with expertise in fluid dynamics, geologic storage, and advanced imaging techniques. He leads projects on CO2 geostorage, multiphase flow modeling, and high-resolution tomographic imaging. His work bridges materials physics, environmental science, and engineering, addressing challenges in energy storage and climate mitigation. He has supervised numerous research students and collaborates on initiatives like the ARC Training Centre for Multiscale 3D Imaging. Key research interests include pore-scale fluid dynamics, supercritical CO2 behavior, and X-ray microtomography applications. Over 200 publications highlight his contributions to understanding fluid trapping mechanisms, beam hardening correction in tomography, and multiscale imaging techniques. He has pioneered methods for analyzing heterogeneous materials and improving tomographic image quality. His projects span geologic carbon storage, additive manufacturing inspection, and environmental fluid dynamics. Prof Sheppard has secured 26 research grants, including projects on CO2 sequestration, X-ray source optimization, and advanced manufacturing. He advises on imaging technologies and leads ANU's efforts in multiscale materials characterization. His lab focuses on applying cutting-edge imaging tools to solve real-world problems in energy and environmental science.

Shahram Pezeshkمشاهده پروفایل

استاد

• Engineering Seismology
• Earthquake Engineering
• Ground Motion Prediction
• +۵ مورد دیگر

Shahram Pezeshk is the Chair and Emison Professor of Civil Engineering at The University of Memphis, affiliated with the Herff College of Engineering. His research focuses on engineering seismology, earthquake engineering, ground motion prediction, and structural optimization. He holds prestigious awards including the 2014 Herff College Outstanding Research Award and the Emison Professorship. Education: B.S., Civil Engineering, University of Illinois, Urbana-Champaign (1982) M.S., Civil Engineering, University of California, Berkeley (1983) Ph.D., Civil Engineering, University of Illinois, Urbana-Champaign (1989) Research Interests: Dr. Pezeshk's work spans engineering seismology and seismic hazard analysis, including ground motion prediction equations (GMPEs) used in USGS hazard maps. He also explores structural optimization and probabilistic seismic design. His research has been applied to projects like the 2014 NSHMs and LNG tank station hazard analysis. Grants & Awards: Recipient of multiple research awards from the Herff College and University of Memphis Featured Engineer (2002) and Don P. Smith Professorship (1999-2002) Listed in Who's Who in Science and Engineering Advising & Teams: Dr. Pezeshk supervises a diverse research team, including doctoral students (e.g., Nima Nazemi, Niloufar Alidadi) and postdoctoral researchers (e.g., Behrooz Tavakoli). His lab focuses on seismic hazard assessment, ground motion modeling, and structural resilience.

Moo Chungمشاهده پروفایل

استاد

• Computational Neuroimaging
• Topological Data Analysis
• Brain Networks
• +۵ مورد دیگر

Moo Chung is a Professor of Biostatistics and Medical Informatics at the University of Wisconsin-Madison, affiliated with the School of Medicine and Public Health. He holds additional appointments in the Department of Statistics and the College of Letters and Science. Chung earned his Ph.D. in Mathematics and Statistics from McGill University under Professors Keith J. Worsley and James O. Ramsay. His research focuses on computational neuroimaging, leveraging MRI, fMRI, and DTI to study brain dynamics through topological and geometric methods. Key areas include persistent homology, brain network analysis, and statistical modeling of high-dimensional imaging data. He has pioneered methods like hyper-network construction and exact topological inference for paired brain networks, addressing computational challenges in analyzing large-scale neuroimaging datasets. Chung’s work integrates advanced mathematical techniques such as Hodge Laplacian, spectral graph theory, and Wasserstein distances to analyze brain connectivity. He has secured NIH funding for projects like the Brain Initiative (2017-2020) and current grant MH133614 (2023), focusing on geometric data analysis and topological dynamics. His contributions include three books on brain imaging and network analysis, with ongoing research on topological data analysis applications. Awards: Vilas Associate Award (2013-2014), NIH Brain Initiative Award, Editor's Award (2011) Labs/Teams: Leads brain imaging workshops globally, including Seoul National University (2024) and POSTECH (2024), and organizes conferences like ISBI and MICCAI special sessions. Grants: NIH EB022856 (Brain Initiative), MH133614 (2023), and collaborations with institutions like Vanderbilt University and University of Chicago. Chung actively mentors students and postdocs in biomedical data science, offering fellowships through the CIBM program. His group maintains a Google mailing list for brain image analysis discussions and hosts regular seminars on methodological advancements.

• Precision Agriculture
• IoT in Agriculture
• Agricultural Engineering
• +۴ مورد دیگر

Patrick Noack is a Professor at HSWT, leading the Competence Centre for Digital Agribusiness (KoDA) and serving as Programme Director for Agricultural Engineering and Green Digital Engineering. His research focuses on IoT applications in agriculture, precision farming, and sensor systems. He has pioneered projects like DenimeF (foreign body detection in forage harvesters) and AutoDGB (automated field trial analysis). Key areas include GPS-guided machinery, soil sensors, and UAV-based monitoring. He chairs digitalization committees and collaborates with organizations like ASABE and VDI Round Table GIS. His work emphasizes interdisciplinary education, integrating horticulture and bioinformatics through agile methods. Research interests span environmental technology, geospatial data analysis, and sustainable resource management. Recent publications address spectral imaging for crop yield prediction and non-lethal insect monitoring systems. He advises students like Jan Oehlschläger and leads grants totaling millions in precision agriculture innovation.

• Mathematical Modeling
• Kinetic Theory
• Socio-Economic Dynamics
• +۳ مورد دیگر

Giuseppe Toscani is a Full Professor at the Department of Mathematics of the University of Pavia. His research focuses on mathematical modeling of socio-economic systems, epidemiological dynamics, and kinetic theory. He explores topics such as wealth distribution, opinion formation, and epidemic spread using advanced methods in partial differential equations and statistical mechanics. Key research areas include analysis of inequality indices (Gini Index extensions), multi-agent systems, and nonlinear diffusion processes. His work intersects with applications in public health, economic policy, and social behavior modeling, emphasizing the interplay between theoretical frameworks and real-world phenomena. Education: Not explicitly detailed in provided texts, but his academic role implies a doctoral degree in Mathematics or related field. Labs/Teams: Affiliated with the Methods and Models for Applied Sciences research group. Grants/Awards: No specific grants or awards listed in the text. Recent publications address condensation patterns in opinion dynamics, multidimensional inequality metrics, and pandemic modeling through compartmental systems. His kinetic models provide foundational tools for understanding complex social and biological systems.

José PICHERALمشاهده پروفایل

استاد

• Signal Processing
• Acoustic Imaging
• Array Processing
• +۵ مورد دیگر

José Picheral is a Professor at CentraleSupélec, affiliated with the Laboratory of Signals and Systems (L2S). He holds a PhD (2003) and HDR (2017) in high-resolution signal processing methods and inverse problems. His research focuses on array processing, source localization, acoustic imaging, and vibration analysis, with applications in aeroacoustics, automotive systems, and industrial monitoring. He has supervised multiple PhD students and contributed to projects like Valeo’s smartphone-based car key replacement system. Education: Engineering Degree: Supélec (1999) and Politecnico di Milano (1999, Erasmus-TIME) PhD: Paris Sud University (2003) Habilitation (HDR): Université Paris Sud (2017) Research Interests: High-resolution methods for distributed sources, sparse signal processing, acoustic imaging, asynchronous measurements, and sensor array design. Current projects include spatial source covariance estimation, EEG spectrum analysis, and automotive applications using smartphone localization. Key Contributions: Over 50 publications in top journals/conferences (e.g., IEEE Transactions, ICASSP). Notable work on MUSIC algorithm robustness, DAMAS optimization, and sparse approaches for tip-timing signals. Advising & Collaboration: Supervised 7 PhD students. Collaborations with SAFRAN, Valeo, and academic teams in Bayesian inference and inverse problems. Active in L2S’s Inverse Problems Group and SYCOMORE team. Labs/Teams: Member of L2S’s Signal Processing and Statistics group, leading research in systems and control, telecommunications, and energy systems.

• Differential Geometry
• Geometric Analysis
• CR Manifolds
• +۴ مورد دیگر

John M. Lee is a mathematician whose research focuses on differential geometry and geometric analysis, with significant contributions to CR manifolds, the Yamabe problem, Einstein metrics, and asymptotically hyperbolic manifolds. His work frequently appears in top-tier mathematics journals and involves collaborations with prominent researchers in the field. Research Interests: Lee's primary areas of investigation include geometric partial differential equations, conformal geometry, mathematical general relativity, and complex manifold theory. His research bridges pure differential geometry with applications in theoretical physics, particularly in the study of Einstein's field equations. Publication Trends: Lee's publications demonstrate a consistent focus on geometric structures with asymptotic behavior, boundary regularity problems, and the interplay between conformal geometry and Einstein metrics. Recent work emphasizes Sobolev-class asymptotics and constraint equations in general relativity. Collaborations: Frequent collaborators include Paul T. Allen, James Isenberg, Iva Stavrov Allen, C. Robin Graham, and David Jerison.

• Music Cognition
• Auditory Perception
• Medical Alarm Design
• +۴ مورد دیگر

Dr. Michael Schutz is a Professor of Music Cognition/Percussion at McMaster University's School of the Arts, serving as University Scholar and founding director of the MAPLE Lab. His research bridges music performance/perception, focusing on auditory perception, medical alarm design, and cross-modal sensory integration. Supported by grants from CFI, NSERC, SSHRC, NIH, and others, his work appears in journals like Psychology of Music and British Journal of Anaesthesia . He actively contributes to the Percussive Arts Society and Society for Music Perception and Cognition, co-authoring influential textbooks and leading national percussion networks. Recent studies explore alarm redesign using musical principles and historic shifts in emotional expression across musical eras. His research has been featured on CBC's Quirks & Quarks , On The Nature of Things , and in textbooks including Cognitive Psychology and The Psychology of Music in Multimedia . He directs the McMaster Institute for Music and the Mind, emphasizing interdisciplinary connections between music, mind, and technology. Grants: CFI, NSERC, SSHRC, NIH, Early Researcher Award (Ontario) Key Roles: MAPLE Lab Director, SMPC Board Member (2014–2017) Public Engagement: Over 60 media features, 30+ invited performances, and collaborative research with Vanderbilt University Teaching includes courses on music cognition, percussion ensembles, and laboratory methods. His work has influenced anti-racist practices in music cognition research and ergonomic design of medical interfaces.

• Deep Learning
• Artificial Intelligence
• Graph Neural Networks
• +۳ مورد دیگر

Dr. Hua Mao is an Assistant Professor in Computer and Information Sciences at Northumbria University, holding a PhD in Computing Science from Aalborg University. Previously served as Associate Professor at Sichuan University, China. Research Focus: Specializes in deep learning and artificial intelligence with applications spanning graph representation, multiview clustering, and explainable AI systems. Recent work advances techniques for processing incomplete data and invariant representation learning. Publication Trends: Her 45+ research outputs demonstrate consistent innovation in machine learning fundamentals, particularly graph-based algorithms and interpretable models. Recent articles focus on explainability frameworks and adaptive learning techniques for complex data structures including IP analytics and flood mapping applications.

• Random Matrices
• Limit Theorems
• Mathematical Diffraction Theory
• +۱ مورد دیگر

Prof. Dr. Holger Kösters is affiliated with the Institute of Mathematics at the University of Rostock , where he focuses on probability theory and its intersections with mathematical physics and data science. His research spans random matrix theory, spectral distributions, and diffraction phenomena in stochastic systems. University: University of Rostock School: Faculty of Mathematics and Natural Sciences Department: Institute of Mathematics Email: holger.koesters@uni-rostock.de Research Interests: Random matrices and their applications, limit theorems in high-dimensional statistics, mathematical diffraction theory, and probabilistic methods in data science. His work explores universal patterns in eigenvalue distributions, connections to number theory, and statistical mechanics. Publication Trends: Recent articles emphasize random matrix products, spectral asymptotics, and applications to diffraction theory. Areas include polynomial ensembles, free probability, and probabilistic models for point processes. Academic Role: As a professor, he contributes to research and teaching in probability and mathematical statistics, with co-authorships in journals like Annals of Probability and Communications in Mathematical Physics .