جست‌وجوی استاد و پژوهشگر

Wang Mingxiuمشاهده پروفایل

پژوهشگر

• Soil Science
• Hydrology
• Remote Sensing
• +۳ مورد دیگر

Mingxiu Wang is a Researcher at Texas A&M University's College of Agriculture & Life Sciences, affiliated with Texas A&M AgriLife Research. He holds a Ph.D. in Soil Science and focuses on hydrology, remote sensing, and groundwater management. His work emphasizes integrating environmental data for improved water resource forecasting and ecosystem restoration. Education: B.S. Hydrology and Water Resources Engineering, Northwest A&F University (2018) M.S. Hydrology and Water Resources, University of California - Irvine (2020) Ph.D. in Soil Science (details unspecified) Research Interests: Dr. Wang's research combines field monitoring, remote sensing technologies, and statistical modeling to address challenges in groundwater recharge dynamics, land cover change impacts, and seasonal streamflow forecasting. He explores how vegetation shifts affect water cycles and develops methodologies for precise water resource management in agricultural and arid regions. Key Contributions: His work bridges hydrological sciences and applied technologies, with recent studies focusing on: In situ soil moisture monitoring systems Woody encroachment impacts on recharge zones Remote sensing integration for agricultural water forecasting Soil salinity quantification via spectroscopy Affiliations: He contributes to Texas A&M AgriLife's interdisciplinary initiatives and collaborates with institutions in China and the U.S. His office is located at HPCT 543B in College Station, Texas.

Donsub Rimمشاهده پروفایل

استادیار

• Numerical Analysis
• Partial Differential Equations (PDEs)
• Inverse Problems
• +۵ مورد دیگر

Donsub Rim is an Assistant Professor in the Department of Mathematics at Washington University in St. Louis. He holds a visiting position at the University of Washington during the 2024-2025 academic year. Previously, he was a visiting assistant professor at Tohoku University's International Research Institute of Disaster Science (IRIDeS) in 2023 and affiliated with Courant Institute and Columbia University. He earned his Ph.D. from the University of Washington under advisors Randall J. LeVeque and Gunther Uhlmann. Education: Donsub Rim obtained his Ph.D. in Mathematics from the University of Washington, advised by Professors Randall J. LeVeque and Gunther Uhlmann. His academic journey also included affiliations with the Courant Institute and Columbia University before joining Washington University. His research focuses on numerical analysis of partial differential equations (PDEs), model reduction, and inverse problems. Specific interests include low-rank neural representations (LRNRs) for real-time PDE solutions, stability analysis of neural networks in geophysical applications like tsunami early warning systems, and computational methods in geophysics, medical imaging, and plasma physics. He has developed the approximate discrete Radon transform (ADRT) with applications in fast inversion and sparse approximations. In recent work, he has explored machine learning techniques for improving tsunami forecasting and risk evaluation, leveraging sparse data and Bayesian methods. His contributions to model reduction and neural network stability have advanced applications in nonlinear hyperbolic PDEs and plasma instabilities. Collaborations include interdisciplinary projects in disaster science, computational geophysics, and scientific software development. Donsub Rim's work has been published in leading journals and conferences, including SIAM Journal on Scientific Computing, Journal of Geophysical Research, and NeurIPS. His research emphasizes bridging computational mathematics with real-world challenges in geophysics and engineering. He maintains an active GitHub repository for numerical software development, emphasizing open-source contributions in computational mathematics and geophysics. His projects include the ADRT Python package and tools for tsunami modeling.

Dylan Connorمشاهده پروفایل

دانشیار

• Economic Inequality
• Social Mobility
• Urban Planning
• +۵ مورد دیگر

Dylan Connor is an Associate Professor at Arizona State University's School of Geographical Sciences and Urban Planning, and a CASBS Fellow at Stanford University. His research focuses on computational social science, spatial inequality, urban development, and climate hazards. He examines how places shape economic inequality, social mobility, and population health, leveraging large-scale datasets and machine learning frameworks. Key research areas include the geography of intergenerational mobility, 'left behind places' in the U.S., and spatial wealth inequality through the GEOWEALTH-US project. His work bridges historical demography, urban planning, and environmental justice, with a focus on data-driven solutions for equitable communities. Core faculty at the Spatial Analysis Research Center (SPARC), Arizona State University Recipient of the 2023-2024 Faculty Excellence in Mentorship and Teaching Award Publications in Proceedings of the National Academy of Sciences , Science Advances , and Demography His interdisciplinary collaborations address urban climate risks, digital twins, and historical migration patterns. He emphasizes data infrastructure to study long-term demographic and spatial trends, aiming to inform policy and urban sustainability.

MATTHIAS MORZFELDمشاهده پروفایل

دانشیار

• Data Assimilation
• Monte Carlo Sampling
• Numerical Methods
• +۳ مورد دیگر

Matthias Morzfeld is an Associate Professor in the Institute of Geophysics and Planetary Physics (IGPP) at Scripps Institution of Oceanography, UC San Diego. He holds a Diplom in Mechanical Engineering from TU Darmstadt (Germany) and a PhD in Mechanical Engineering from UC Berkeley. His research focuses on interdisciplinary mathematical and stochastic modeling, particularly in data assimilation, Monte Carlo sampling, and numerical methods for inverse problems. He has held positions at UC Berkeley's Math Department, Lawrence Berkeley National Laboratory, and the University of Arizona. His work explores the application of sampling methods in high-dimensional spaces across Earth sciences, with a strong emphasis on geophysical data analysis and uncertainty quantification. He has advised numerous graduate and undergraduate students, collaborating on projects such as geomagnetic field modeling and image deblurring. Morzfeld is a frequent visitor at the Institute de Physique du Globe de Paris (IPGP) and mentors students through the Math Alliance. His teaching spans geophysical data analysis, computational inverse problems, and applied mathematics at both UCSD and the University of Arizona. Awards include the 2016 Sloan Research Fellowship.

Mohammad Farazmandمشاهده پروفایل

استادیار

• Applied Dynamical Systems
• Control Theory
• Optimization
• +۴ مورد دیگر

Mohammad Farazmand is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State). His research focuses on applied dynamical systems, control theory, optimization, and data-driven methods. He holds a PhD in Mathematics from ETH Zurich (2014). His work emphasizes stochastic modeling in epidemiology and environmental systems, fluid dynamics, and the development of numerical methods for partial differential equations (PDEs). Key contributions include enforcing conservation laws in numerical discretizations, quantifying uncertainties in stochastic compartmental models, and predicting extreme events like wildfires using sparse sensor data. He also explores reduced-order modeling and shape-morphing techniques for efficient simulation of complex systems. Recent publications span journals like Nonlinear Dynamics , Journal of Computational Physics , and Proceedings of the Royal Society A , highlighting interdisciplinary applications in fluid mechanics, epidemiology, and wildfire risk assessment. Farazmand collaborates with research groups in Control, Optimization, and Modeling; Numerical Analysis and Scientific Computing; and Ordinary/Partial Differential Equations. His methods bridge deterministic and probabilistic frameworks to address challenges in state estimation and rare event prediction.

Zhen Quمشاهده پروفایل

استادیار

• Atmospheric Chemistry
• Climate Science
• Air Quality Modeling
• +۵ مورد دیگر

Zhen Qu is an Assistant Professor in the Department of Marine, Earth, and Atmospheric Sciences at North Carolina State University. She holds a PhD in Mechanical Engineering (Air Quality Track) from the University of Colorado Boulder (2019) and a B.S. in Physics from Peking University (2014). Her research focuses on atmospheric chemistry, using satellite observations and advanced modeling techniques to study air pollutants and greenhouse gases. Key areas include methane emissions, tropospheric ozone analysis, and Bayesian statistical methods for emission inversion. Dr. Qu’s work integrates data-driven approaches with atmospheric models to address climate and environmental challenges, such as tracing greenhouse gas emissions and understanding climate interactions. She teaches MEA 425/525 (Introduction to Atmospheric Chemistry) and MEA 412 (Atmospheric Physics). Her research group develops tools like the Integrated Methane Inversion (IMI 1.0) to analyze high-resolution methane emissions from satellite data. Recent publications highlight contributions to methane surge attribution, ozone analysis via chemical reanalyses, and wildfire smoke impacts on health. She collaborates with global networks to improve emission inventories and supports policy through transparent data methodologies. Dr. Qu actively seeks motivated graduate students to join her team, emphasizing interdisciplinary approaches to environmental science.

Leonardo Azevedoمشاهده پروفایل

استادیار

• Geostatistics
• Reservoir Geophysics
• Seismic Inversion
• +۴ مورد دیگر

Leonardo Azevedo is an Assistant Professor (with Habilitation since 2021) at the Department of Civil Engineering, Architecture and Georesources, Instituto Superior Técnico, University of Lisbon. His academic journey includes a PhD in Georesources from IST (2013), with visiting periods at Stanford University and Heriot-Watt University, and earlier degrees from Aveiro University. He specializes in geostatistical methods for reservoir characterization and seismic inversion, with applications to carbon sequestration, subsurface modeling, and environmental geophysics. Research interests focus on integrating geostatistics with advanced computational techniques, including machine learning and generative adversarial networks (GANs), to improve subsurface modeling accuracy. Key areas include seismic inversion for reservoir properties, uncertainty quantification, and geophysical data interpretation. He has supervised numerous PhD and MSc students, with topics ranging from geostatistical CO2 plume modeling to seismic oceanography. Awards include the Fulbright Scholarship (2018-2019) and recognition for top-tier SEG conference contributions (2015). His work has been published in journals like Geophysics , Computational Geosciences , and Mathematical Geosciences . He leads the EVOLVE team (2018) and advises student chapters of SEG and SPE. Current funded projects include the SMART project for marine debris monitoring and stochastic rock physics inversion research. His industry experience includes roles at Parallel Geoscience and CGG, bridging academic and applied geophysics.

Ioana Colfescuمشاهده پروفایل

پژوهشگر ارشد

• Climate Dynamics
• Machine Learning for Climate Prediction
• Extreme Events
• +۵ مورد دیگر

Dr. Ioana Colfescu is a Principal Research Fellow in Climate and Machine Learning at the University of St Andrews' School of Earth & Environmental Sciences, with a joint role at the National Centre for Atmospheric Science (NCAS). She leads the NCAS St Andrews research group, specializing in physics-informed machine learning for climate system analysis. Her academic roles include Digital Atmosphere Programme Leader at NCAS and teaching specialized atmospheric science courses. Colfescu holds a PhD in Climate Dynamics from George Mason University and degrees from the University of Bucharest. Her research focuses on large-scale climate dynamics (ENSO/AMO), machine learning applications for climate prediction, extreme events, and mountain meteorology, with notable contributions to North Sea emissions studies and mountain wave modeling. She has collaborated with institutions worldwide, including the National Centre for Atmospheric Research (USA) and universities in Italy, Spain, and Romania. Colfescu's fieldwork spans the Arctic, Iceland, and Italy, and she is a Certified Mission Scientist for airborne research. She actively mentors postgraduate students, emphasizing collaborative, cross-disciplinary approaches. Her work bridges fundamental climate science with applied impacts, supported by industry and international research partnerships.

• Causal Discovery
• Machine Learning
• Statistics
• +۳ مورد دیگر

Emiliano Díaz Salas Porras serves as a Post-doctoral Research Fellow at the Image & Signal Processing group, University of Valencia, focusing on causal discovery and machine learning applications for Earth system sciences, particularly heatwave analysis and pyrocumulonimbus cloud prediction. His academic foundation includes: PhD in Machine Learning (2018-2023), University of Valencia MSc in Statistics (2014-2017), ETH Zurich Bachelor in Applied Mathematics (2004-2014), ITAM, Mexico His research pioneers physics-integrated causal machine learning, developing novel methods for latent confounder estimation, non-additive causal discovery, and domain-specific applications in environmental science. He bridges theoretical advances in kernel methods and large language models with practical Earth observation challenges, emphasizing interpretable causal structures for climate phenomena. Publication analysis reveals three dominant threads: (1) Earth science applications targeting pyrocumulonimbus clouds and carbon fluxes, (2) foundational causal discovery techniques for latent variables and constrained systems, and (3) cross-disciplinary method development merging physics awareness with nonparametric modeling. His work consistently integrates observational data constraints with causal inference frameworks. He actively contributes to NASA Frontier Development Lab initiatives, having co-developed the Pyrocast pipeline for the 2022 Aerosols challenge and currently advancing multi-sensor 3D cloud modeling in the 2024 FDL challenge, demonstrating applied impact in climate risk forecasting.

Andrei Jalbaمشاهده پروفایل

استادیار

• Computer Graphics
• Medical Imaging
• Visualization Algorithms
• +۳ مورد دیگر

Andrei Jalba is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Department of Mathematics and Computer Science. His research focuses on visualization algorithms, medical imaging, computational fluid dynamics, and real-time graphics. He leads groups in Visualization and Visual Analytics, contributing to interdisciplinary projects like physics-based fluid simulations and MRI data processing. Jalba teaches courses such as Simulation in Computer Graphics and Computer Graphics . His work bridges theoretical advancements with practical applications in healthcare and interactive systems. Research Interests: Computer Graphics, Medical Imaging, Data Assimilation, Scientific Visualization. Key Projects: Physics-based MRI denoising, volumetric light transport simulation, and collision response algorithms for deformable models. His publications emphasize efficient algorithms for real-time applications and medical data analysis, with notable contributions to ACM Transactions on Graphics and Computer Graphics Forum. No specific awards or grants are explicitly listed, but his work is funded by initiatives like the ERC TouchDesign project (2019).

• Electromagnetic Engineering
• Magnetic Levitation
• Vibration Isolation
• +۶ مورد دیگر

Dr. William Robertson is a Senior Lecturer in the School of Electrical and Mechanical Engineering at the University of Adelaide, specializing in electromagnetics and biomechanics. His research focuses on advancing magnetic levitation (maglev) systems for vibration isolation, wave energy conversion, and actuator design through the AUMAG research group. He is a proponent of open science, emphasizing tool and data sharing. His work integrates fundamental physics with applied engineering solutions. Key research areas include magnetic force modeling, quasi-zero stiffness mechanisms, and biomechanical gait analysis. He supervises Honours, Masters, and PhD students, fostering innovation in both mechanical systems and human movement studies. The AUMAG group collaborates with industry on projects like maglev elevators and energy harvesting devices. His biomechanics work involves developing low-cost 3D scanning methods for musculoskeletal modeling and sports equipment optimization. Recent projects include non-contact force measurement techniques in maglev systems and gait recognition algorithms using force platforms. His work spans interdisciplinary applications from robotics to environmental modeling, with a focus on precision engineering and data-driven methodologies.

• Water Engineering
• Hydraulic Transients
• Fluid Dynamics
• +۶ مورد دیگر

Professor Martin Lambert is a Professor of Water Engineering and Hydraulics at the University of Adelaide’s School of Civil, Environmental and Mining Engineering. He has served as Head of the School (2009-2012 and 2015-2017) and Associate Dean (Research) in the ECMS Faculty (2006-2009). His research focuses on water engineering, including hydraulic transients for infrastructure assessment, biofilm impacts on pipelines, and stochastic hydrology. Education: Bachelor of Civil Engineering (Honours 1), University of Newcastle, Australia (1988) Research Interests: Inverse transient techniques for pipeline condition assessment Sensor networks for monitoring transient pressures Biofilm dynamics and pipe roughness Rainfall simulation models and flood frequency analysis Awards: University Prize for Excellence in Higher Degree by Research Supervision (2008) Australian Learning & Teaching Council Citation (2009) Grants: Over $4.9 million in research funding from the Australian Research Council, alongside industry collaborations. Labs/Teams: Leads research initiatives in smart water networks, including acoustic and hydraulic transient analysis, through the School of Civil, Environmental and Mining Engineering.

• Exploration Geophysics
• Artificial Intelligence
• Compressed Sensing
• +۳ مورد دیگر

Jianwei Ma is a Professor at the School of Mathematics, Harbin Institute of Technology, leading the Center of Geophysics and the Institute of Artificial Intelligence. His expertise spans exploration geophysics, artificial intelligence, and compressed sensing. Ma holds a Ph.D. in Solid Mechanics from Tsinghua University (2002) and a BS in Engineering Mechanics from Dalian University of Technology (1998). His research focuses on improving seismic exploration through mathematical methods, including sparse transforms, compressed sensing, and deep learning applications. Notable contributions include methodologies for seismic noise reduction, data interpolation, and full waveform inversion. Ma has held visiting positions at institutions like UCLA, UC Los Angeles, and the University of Texas at Austin. He has received prestigious awards such as the NSFC Distinguished Young Scholar (2016) and World's Top 2% Scientists (2020-2024). Ma actively contributes to professional societies, including SEG and IEEE, and has organized international workshops on mathematical geophysics and AI-driven geoscience. His team includes faculty members and over 30 graduate students, reflecting his commitment to training future researchers in geophysics and AI. Collaborations with global leaders in geophysics and mathematics, such as Prof. Stanley Osher (UCLA) and Prof. Gerlind Plonka (University of Goettingen), highlight his interdisciplinary approach. Key publications include advancements in deep learning for geophysical inversion and sparse representation techniques for seismic data processing.

Euan Spenceمشاهده پروفایل

استاد

• Wave Propagation
• Helmholtz Equation
• Numerical Analysis
• +۶ مورد دیگر

Euan Spence is a Professor of Mathematics at the University of Bath, part of the Department of Mathematical Sciences and the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa). His research focuses on the numerical analysis and mathematical analysis of wave propagation problems, particularly involving the Helmholtz equation, Maxwell equations, and boundary integral equations. He holds a PhD from the University of Cambridge (2009) and has held academic positions at Bath since 2009, advancing to Professor in 2020. His work bridges semiclassical analysis and numerical methods, addressing challenges in high-frequency wave scattering and uncertainty quantification. Spence has led or contributed to multiple research projects funded by EPSRC and EU, including the ERC Synergy Grant for phase-space-inspired numerical methods. His awards include the London Mathematical Society Whitehead Prize (2022) and the John Todd Award (2019). He supervises PhD students and postdocs, contributing to areas like domain decomposition methods, finite element analysis, and seismic imaging optimization. His educational background includes a Master’s and PhD from the University of Cambridge, specializing in transform methods for linear PDEs under Thanasis Fokas. He teaches courses on partial differential equations and numerical analysis, emphasizing rigorous mathematical foundations and practical computational skills.

Olga Mulaمشاهده پروفایل

دانشیار

• Numerical Analysis
• Partial Differential Equations
• Scientific Machine Learning
• +۳ مورد دیگر

Olga Mula is an Associate Professor of Mathematics at TU Eindhoven and will transition to a Full Professor position at the University of Vienna starting September 2025. Her research focuses on numerical analysis of Partial Differential Equations (PDEs) combined with data-driven methodologies, including Scientific Machine Learning, High-Dimensional Approximation, and Optimal Transport. She leads the Computational PDEs group and has held roles at institutions like INRIA Paris and Paris Dauphine University. Mula's work addresses challenges in computational science, such as pollution modeling, epidemiology, and nuclear engineering. She currently seeks PhD and Postdoc candidates for projects on Wasserstein Gradient Flows and singularly perturbed PDEs. Education: PhD in Applied Mathematics (Sorbonne University, 2014), French Habilitation (Paris Dauphine University, 2021) Research Interests: Machine Learning for PDEs, Data Assimilation, Numerical Optimal Transport Her contributions include advancements in PINN frameworks, model order reduction, and sensor placement strategies. She teaches courses on Numerical Analysis, Optimization, and Mathematics of Neural Networks at TU Eindhoven.