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Fangxin FangView profile

Research Fellow

• Predictive Modelling
• Machine Learning
• Environmental Modelling
• +3 more

Fangxin Fang is Senior Research Fellow in the Department of Earth Science and Engineering at Imperial College London. His research develops predictive modeling frameworks combining machine learning, data assimilation, and computational fluid dynamics for environmental and engineering applications. Research focuses on: AI-driven air pollution forecasting systems Machine learning for multi-scale environmental modeling Adaptive mesh methods for flood prediction High-performance computing implementations Personalized health impact assessments Fang's work integrates computational mathematics with practical applications in urban air quality management, flood risk assessment, climate modeling, and renewable energy optimization. His publications demonstrate innovations in model reduction techniques, generative adversarial networks for fluid flow modeling, and adaptive mesh refinement methods. He leads the EPSRC Open Fellowship project on AI techniques for urban environmental prediction and contributes to initiatives on climate-health interactions. Fang collaborates internationally on machine learning applications in earth system science and sustainable engineering.

• Data Assimilation
• Machine Learning
• Climate Modeling
• +1 more

Rossella Arcucci is Senior Lecturer in Data Science and Machine Learning at Imperial College London. She leads the Data Learning research group, developing hybrid data assimilation and machine learning methods for environmental applications including air pollution forecasting, wildfire prediction, and climate modeling.

Charikleia StouraView profile

Researcher

• Structural Health Monitoring
• Railway Infrastructure
• Dynamical Systems
• +3 more

Charikleia Stoura is a Researcher at the Chair of Structural Mechanics and Monitoring within the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich . Her work focuses on leveraging data from traversing trains to monitor railway infrastructure, including tracks and bridges. Diploma : Civil Engineering, National Technical University of Athens (2015) Ph.D. : Civil and Environmental Engineering, Hong Kong University of Science and Technology (2021) Postdoctoral Fellowships : ETH Zürich (2022), Marie Skłodowska-Curie Global (2024) Her research integrates Bayesian filtering , data-driven modeling , and on-board sensor networks to develop cost-effective solutions for continuous structural health monitoring (SHM) of transport infrastructure. This includes applications for rail roughness profiling and bridge modal identification using vibration data from trains. Key publication themes include: Imitation learning from observation for input estimation Vehicle-bridge interaction analysis under seismic loads Markov-Chain Monte Carlo Bayesian parameter tuning Virtual sensing techniques for railway infrastructure Reduced-order modeling for rail roughness identification Notable scientific awards include: ETH Zürich Postdoctoral Fellowship Marie Skłodowska-Curie Global Postdoctoral Fellowship

• Bayesian statistics
• Spatio-temporal modeling
• Environmental science
• +4 more

Edward Cripps is an Associate Professor in the School of Physics, Maths and Computing at The University of Western Australia, specializing in Mathematics and Statistics. He holds degrees including a BEc from UWA and a PhD in Statistics from UNSW. His research focuses on Bayesian longitudinal analysis, spatio-temporal models, and integrating physical models with probabilistic approaches for environmental, meteorological, and oceanographic applications. He leads major grants including the ARC Industrial Transformation Research Hub for Transforming Energy Infrastructure (2021-2025) and the DARE Training Centre (2021-2025). Collaborations include industry partners like INPEX, Shell, Woodside, and governmental bodies such as the Bureau of Meteorology. He teaches undergraduate courses in probability and applied statistics. Grants & Awards: ARC Hub ($10M) as Chief Investigator/Data Science Leader (2021-2025) MinEx CRC 2022-2023 publication prize (2023) Research Interests: His work addresses model-data mismatch in complex systems, leveraging high-quality data for decision-making in resource management and engineering. Recent projects include geophysical feature discovery, spectral estimation bias correction, and offshore wind turbine structural analysis.

Amir TaghvaeiView profile

Assistant Professor

• Control Theory
• Machine Learning
• Stochastic Thermodynamics
• +3 more

Amir Taghvaei is an Assistant Professor in the William E. Boeing Department of Aeronautics and Astronautics at the University of Washington, Seattle. His research focuses on control theory, machine learning, stochastic thermodynamics, and optimal transportation. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and completed a postdoctoral fellowship at the University of California, Irvine. Education: Ph.D. in Mechanical Engineering, University of Illinois at Urbana-Champaign, 2019 M.S. in Mathematics, University of Illinois at Urbana-Champaign, 2017 B.S. in Mechanical Engineering and Physics, Sharif University of Technology, Iran, 2013 Research Interests: Design of scalable algorithms for nonlinear filtering and estimation Intersections of control theory, machine learning, and physics Stochastic thermodynamics and energy harvesting from thermal anisotropy Optimal transport theory applications in control and machine learning Recent Trends in Publications: His work emphasizes data-driven algorithms, optimal transport formulations for filtering, and stochastic control systems. Recent contributions include advancements in nonlinear filtering using Brenier maps and entropy production analysis in thermodynamic engines. Grants & Collaborations: Funded by NSF for projects on optimal transport methods and thermal anisotropy Collaborations with researchers like Tryphon Georgiou, Yongxin Chen, and Bamdad Hosseini Labs & Groups: Leads research in variational optimal transport methods and stochastic control within the University of Washington’s Aeronautics & Astronautics department.

• Reservoir Engineering
• Carbon Capture and Storage (CCS)
• Hydrogen Storage
• +4 more

Dr. Daniel Arnold is an Associate Professor at Heriot-Watt University, affiliated with the School of Energy, Geoscience, Infrastructure and Society and the Institute for GeoEnergy Engineering. His research focuses on reservoir engineering, carbon capture and storage (CCS), hydrogen storage, and the application of AI in energy systems. He contributes to the UN Sustainable Development Goals, particularly through innovative solutions for clean energy and environmental risk mitigation. His work emphasizes reservoir modeling, uncertainty quantification, and fractured reservoir analysis. Recent publications highlight advancements in AI-driven workflows for reservoir characterization, risk assessment of CCS containment in legacy wellbores, and numerical studies on hydrogen storage in sandstone reservoirs. He also explores machine learning techniques like GANs and graph variational autoencoders for geoscience applications. Dr. Arnold has supervised PhD student Hydra Rodrigues and contributed to benchmark datasets such as the COSTA MODEL for carbonate reservoirs and the CAGEO Watt Field case studies. His interdisciplinary approach bridges geoscience, petroleum engineering, and computational methods to address global energy challenges.

Prajvala KurtakotiView profile

Research Fellow

• Physical Oceanography
• Climate Modeling
• Arctic Climate Systems
• +3 more

Prajvala Kurtakoti is a George S. Benton Postdoctoral Fellow at Johns Hopkins University's Morton K. Blaustein Department of Earth & Planetary Sciences. Her research specializes in physical oceanography with focus on Arctic and Southern Ocean processes, particularly ocean-atmosphere-sea ice interactions, meridional heat transport, and climate model development. Her investigations center on polar climate systems, utilizing advanced Earth System Models to study polynya formation, deep convection phenomena, and climate feedback mechanisms. Recent work examines Bjerknes Compensation mechanisms and decadal-scale climate variability across CMIP6 simulations. Kurtakoti's publications demonstrate consistent focus on high-latitude ocean dynamics, model validation, and climate change impacts on oceanic systems. Her methodological approaches combine high-resolution modeling with observational data analysis to understand complex oceanographic processes.

• Atmospheric Science
• Gravity Waves
• Climate Dynamics
• +3 more

Professor Robert Vincent is a distinguished academic at the University of Adelaide, specializing in atmospheric and space sciences. His research focuses on gravity waves, atmospheric tides, and mesosphere-lower thermosphere dynamics, utilizing advanced meteor radar systems and balloon-borne instruments. He has contributed significantly to understanding equatorial atmospheric processes and their global impacts. Key research interests include gravity wave generation mechanisms, stratospheric-tropospheric coupling, and the role of convection in driving atmospheric circulation. His work integrates observational data from Antarctic campaigns, satellite observations, and numerical modeling to improve climate and weather prediction models. Notable achievements include the development of the HWM07 empirical wind model and discovery of phase-locked quasi-two-day waves. His 2009 AGU Fellowship recognizes contributions to advancing atmospheric dynamics research. Current projects involve coordinated radar observations of migrating tides and application of machine learning to spectral analysis of atmospheric phenomena. He has mentored numerous researchers through collaborative international campaigns like Concordiasi and Strateole 2.

Peng ChenView profile

Assistant Professor

• Scientific Machine Learning
• Uncertainty Quantification
• Bayesian Inference
• +5 more

Peng Chen is an Assistant Professor in the School of Computational Science and Engineering (CSE) at Georgia Institute of Technology, part of the College of Computing. His research focuses on Scientific Machine Learning (SciML), Uncertainty Quantification (UQ), and their applications in computational science and engineering. He holds a PhD and Master's from École Polytechnique Fédérale de Lausanne (EPFL) and a Bachelor's from Xi’an Jiaotong University. Education: PhD in Computational Mathematics (EPFL, 2014), Master in Computational Mathematics (EPFL, 2011), Bachelor in Mathematics (Xi’an Jiaotong University, 2009). Research interests include Bayesian inference, stochastic optimization, parallel computing, and scalable algorithms for high-dimensional problems. His work addresses challenges in data-driven modeling of complex systems, such as metamaterial design, plasma fusion optimization, and epidemic modeling. Advising: Supervises PhD and Master’s students in SciML and UQ. Notable students include Jinwoo Go, Yuan Qiu, and Phillip Si. Alumni include Irene Simo Munoz and Leyao Huang. Labs/Teams: Leads the SciML & UQ group at Georgia Tech, developing software like SOUPy and pSVGD for stochastic optimization and Bayesian inference. Collaborates on NSF-funded projects, including a $1.5M award for variational neural networks in physical systems.

Elizabeth CherryView profile

Associate Professor

• Computational Physiology
• Cardiac Electrophysiology
• High-Performance Computing
• +3 more

Elizabeth Cherry is an Associate Professor in the School of Computational Science and Engineering (College of Computing) at Georgia Institute of Technology, and currently serves as the Associate Dean for Graduate Education. She holds a BS in Mathematics from Georgetown University and a PhD in Computer Science from Duke University. Her research focuses on computational modeling of cardiac arrhythmias, leveraging high-performance computing, numerical methods, and data assimilation to understand reentrant wave dynamics in the heart. Key applications include model development, parameter estimation, and integration with experimental data. She has contributed to tools like CardioFit and Unimapper, advancing patient-specific cardiac modeling. Her editorial roles include serving on the editorial board of Chaos and as a review editor for Frontiers in Physiology . She has organized major conferences such as the SIAM Conference on Applications of Dynamical Systems and Dynamics Days. Her work is supported by the NSF and NIH, addressing arrhythmia mechanisms and therapeutic strategies. Research highlights include studying voltage/calcium alternans, fractional diffusion effects, and rotor-mapping algorithms for atrial fibrillation ablation targets. Advising and grants: Her research is funded by NSF and NIH grants. No awards explicitly mentioned in texts. Her lab focuses on interdisciplinary computational cardiology, collaborating on projects like the Cybercardia initiative for implantable device modeling.

Elizabeth QianView profile

Assistant Professor

• Model Reduction
• Scientific Machine Learning
• Uncertainty Quantification
• +1 more

Elizabeth Qian is an Assistant Professor at Georgia Institute of Technology, holding a joint appointment in the Schools of Computational Science and Engineering and Aerospace Engineering within the College of Computing. Her research focuses on developing computational methods for engineering design and decision-making, emphasizing model reduction, scientific machine learning, and multi-fidelity approaches. She earned her SB, SM, and PhD from MIT's Department of Aeronautics & Astronautics and held a postdoctoral position at Caltech. Qian is a Hans Fischer Fellow at TU Munich and has received numerous awards, including the SIAM Student Paper Prize and NSF Graduate Fellowship. Education: PhD (2017), SM (2014), SB (2013) in Aeronautics & Astronautics, MIT Research Interests: Model reduction for Bayesian inference, physics-informed machine learning, multi-fidelity uncertainty quantification, and surrogate models for engineering systems. Her work bridges theory and application, with contributions to PDE-constrained optimization, ensemble Kalman inversion, and scalable computational methods. Recent publications highlight advancements in multifidelity linear regression and inference-oriented model reduction. She actively mentors students and leads the Aerospace Computational Engineering (ACE) Group at Georgia Tech. Awards: 2023 Hans Fischer Fellowship, NAE Frontiers of Engineering Symposium (2023), World Congress Best Presentation (2022), Caltech Teaching Award (2022) Advising & Grants: Supervises PhD, master’s, and undergraduate researchers; collaborates on grants involving TU Munich and NASA applications. ACE Group focuses on scalable computational methods for design and control. Qian also serves as a guest editor for special issues on model reduction and digital twins, furthering interdisciplinary impact in computational science and engineering.

Xiaowei JiaView profile

Assistant Professor

• Artificial Intelligence
• Data Mining
• Machine Learning
• +3 more

Xiaowei Jia is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh, affiliated with the School of Computing and Information. He holds a Ph.D. from the University of Minnesota (supervised by Prof. Vipin Kumar) and B.S./M.S. degrees from the University of Science and Technology of China and SUNY Buffalo. His research focuses on integrating scientific theory with machine learning to address societal challenges in environmental science, hydrology, and climate modeling. Key research interests include physics-guided machine learning, spatio-temporal data mining, and fairness-aware models. He has developed frameworks like Physics-Guided Neural Networks (PGNN) and Spatial-Net for heterogeneous data, addressing issues in stream temperature prediction, crop yield modeling, and climate change projections. His work has been recognized with awards including the Best Dissertation Award (Minnesota) and multiple Best Paper awards in top conferences. Dr. Jia teaches advanced machine learning courses, emphasizing knowledge integration into AI systems. His lab collaborates extensively with domain scientists to advance applications in agriculture, hydrology, and environmental monitoring. Ongoing projects include high-resolution methane emission datasets (X-MethaneWet) and fairness-aware learning for resource distribution.

Michael SchneierView profile

Adjunct Professor

• Computational Mathematics
• Uncertainty Quantification
• Machine Learning
• +3 more

Michael Schneier is an adjunct professor in the Department of Mathematics at the University of Pittsburgh. He holds a Ph.D. in Scientific Computing from Florida State University (2018), advised by Professor Max Gunzburger. His research focuses on computational mathematics, uncertainty quantification, machine learning, reduced order modeling, and computational fluid dynamics, with a particular emphasis on leveraging data to enhance traditional computational fluid dynamics algorithms. Research interests include advancing numerical methods for partial differential equations, turbulence modeling, and the integration of machine learning techniques into fluid dynamics simulations. His work emphasizes improving algorithmic efficiency and accuracy through innovative approaches like reduced-order models and latent diffusion frameworks. Selected publications explore topics such as latent neural PDE solvers, turbulence diagnostics, and ensemble-based numerical methods for fluid flows. His contributions span both theoretical advancements and practical applications in computational science. No scientific awards or grants are explicitly listed in the provided information. He currently advises no students, though his research may involve collaborative projects. His work is centered at the Department of Mathematics within the Dietrich School of Arts and Sciences.

Daniele SchiavazziView profile

Associate Professor

• Uncertainty Quantification
• Computational Mathematics
• Medical Image Processing
• +4 more

Dr. Daniele Schiavazzi is an Associate Professor in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, with a concurrent appointment as Associate Professor in Aerospace and Mechanical Engineering (AME). He holds a Ph.D. and B.Eng. from the University of Padova, Italy (Summa cum Laude), with postdoctoral training at Stanford University and University of California San Diego. His research focuses on uncertainty quantification, computational hemodynamics, and machine learning , with applications in cardiovascular modeling. Key areas include stochastic analysis, numerical modeling, finite element methods, and Bayesian inference for clinical decision-making under uncertainty. He has pioneered frameworks like InVAErt networks and NeurAM for model emulation and uncertainty reduction. Dr. Schiavazzi leads the Multifidelity Uncertainty Quantification (MUQ) Group , developing open-source tools such as SimVascular for image-based cardiovascular modeling. His work integrates physics-informed AI, multi-resolution expansions, and dimensionality reduction to address complex biomedical problems, including coronary artery disease and pulmonary hypertension. Notable contributions include NSF CAREER Award-funded research on Bayesian inference networks and collaborative projects on cardiovascular model ensembles. His lab actively explores privacy-preserving data synthesis and differential privacy for electronic health records.

Wang MingxiuView profile

Researcher

• Soil Science
• Hydrology
• Remote Sensing
• +3 more

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.