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MATTHIAS MORZFELDView profile

Associate Professor

• Data Assimilation
• Monte Carlo Sampling
• Numerical Methods
• +3 more

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 FarazmandView profile

Assistant Professor

• Applied Dynamical Systems
• Control Theory
• Optimization
• +4 more

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 QuView profile

Assistant Professor

• Atmospheric Chemistry
• Climate Science
• Air Quality Modeling
• +5 more

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 AzevedoView profile

Assistant Professor

• Geostatistics
• Reservoir Geophysics
• Seismic Inversion
• +4 more

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 ColfescuView profile

Research Fellow

• Climate Dynamics
• Machine Learning for Climate Prediction
• Extreme Events
• +5 more

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
• +3 more

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 JalbaView profile

Assistant Professor

• Computer Graphics
• Medical Imaging
• Visualization Algorithms
• +3 more

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
• +6 more

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
• +6 more

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
• +3 more

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 SpenceView profile

Professor

• Wave Propagation
• Helmholtz Equation
• Numerical Analysis
• +6 more

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 MulaView profile

Associate Professor

• Numerical Analysis
• Partial Differential Equations
• Scientific Machine Learning
• +3 more

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.

Karen Veroy-GreplView profile

Professor

• Numerical Methods for Partial Differential Equations
• Model Order Reduction
• Uncertainty Quantification
• +2 more

Karen Veroy-Grepl is a Full Professor of Computational Science at Eindhoven University of Technology (TU/e), affiliated with the Department of Mathematics and Computer Science and the Institute for Complex Molecular Systems (ICMS). She leads research in numerical methods for PDEs, model order reduction, and uncertainty quantification. Her work focuses on multi-scale simulations and data-driven experimental design. Education: B.S., Ateneo de Manila University (Philippines) S.M. and Ph.D., Massachusetts Institute of Technology (USA) Professional Roles: Editorial Board Member: Journal of Computational Physics, SIAM Journals on Uncertainty Quantification and Scientific Computing Scientific Advisor, International Centre for Numerical Methods in Engineering (CIMNE) Chair, SIAM Activity Group on Uncertainty Quantification Her research bridges computational science and engineering applications, with recent work advancing reduced-order models for complex systems and Bayesian inverse problems. Notable contributions include ERC Consolidator Grant-funded research and collaborations in biomedical modeling for real-time treatment planning.

• Applied Dynamical Systems
• Data Science
• Computational Biology
• +4 more

Björn Sandstede is the Alumni-Alumnae University Professor of Applied Mathematics at Brown University's Division of Applied Mathematics. His research focuses on applied dynamical systems, data science, computational biology, nonlinear waves, and pattern formation. He has held faculty positions at The Ohio State University and the University of Surrey before joining Brown in 2008. Key roles at Brown include Department Chair (nine years) and Director of the Data Science Initiative (two years). He has received prestigious awards such as the SIAM JD Crawford Prize, Royal Society Wolfson Research Merit Award, and teaching excellence awards from Brown. Education: PhD in Mathematics, University of Stuttgart (1993) M.A. in Mathematics, University of Heidelberg (1990) Research Interests: Nonlinear waves and pattern formation Computational and mathematical biology Data science and dynamical systems Applications in mRNA localization, zebrafish stripe formation, and multi-omics data analysis Recent Articles: Focus on nonlinear wave dynamics, optimal transport for multi-omics alignment, and topological data analysis in biological systems. These studies bridge theoretical mathematics with computational and biological applications. Awards: 2025 Fellow of the American Mathematical Society Alfred P. Sloan Research Fellowship (2000–2002) SIAM Outstanding Paper Prize (2007) Grants & Leadership: Leads NSF-funded initiatives such as the Research Training Group in Mathematics of Information and Data Science. Co-founded ICERM, Brown's NSF-funded research institute in computational mathematics. Labs & Teams: Collaborates with interdisciplinary groups on projects involving computational biology, optimal transport, and data-driven modeling.

Insa NeuweilerView profile

Professor

• Fluid Mechanics
• Environmental Engineering
• Geophysical Fluid Dynamics
• +5 more

Prof. Dr. Insa Neuweiler is a Professor in the Department of Fluid Mechanics and Environmental Physics at Leibniz University Hannover. Her research focuses on fluid mechanics, environmental physics, and multiphase flow in porous media, with applications in geothermal systems, urban hydrology, and offshore engineering. She leads several high-profile projects funded by institutions like the German Research Foundation (DFG) and the Federal Ministry of Education and Research (BMBF). Research Projects : Development of early warning systems for urban flash floods (2024–2029, Lower Saxony Ministry) Modeling fluid-structure interactions in offshore megastructures (2021–2025, DFG) Two-phase flow in geological fractures (2021–2025, DFG) Education : Not explicitly stated in provided texts, but her academic rank indicates doctoral and post-doctoral qualifications. Advising & Grants : Supervises PhD/Master’s students including Simon Berkhahn, Arne Reinecke, and Rahul Krishna Secured over €10M in funding for projects on hydrological modeling, climate impacts, and energy systems Labs/Teams : Active in interdisciplinary teams at the Institute of Fluid Mechanics and Environmental Physics, collaborating with experts in computational fluid dynamics and environmental engineering.