Weiwen Jiang is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University (GMU), affiliated with the College of Engineering and Computing (CEC). He leads the JQub lab, focusing on hardware/software co-design for computing systems, spanning classical (FPGAs, ASICs) and quantum computing applications in AI-driven fields like medical imaging and geophysics. Prior to GMU, he held a postdoctoral position at the University of Notre Dame and earned his PhD in Computer Science from Chongqing University with a joint PhD in Electrical and Computer Engineering from the University of Pittsburgh. His research emphasizes quantum computing, AI accelerators, and domain-specific computing. Notable achievements include the 2025 NSF CAREER Award, ACM Sigda Meritorious Service Award (2024), and IEEE QuantumWeek Best Paper Award (2023). His work is funded by NSF, DoE, ARO, Meta, and Leidos. He co-chaired IEEE QuantumWeek (2023–2025) and created workshops like StableQ at ESWEEK 2023. Key contributions include developing frameworks like QuPAD for quantum learning and JQub's AI-driven geophysical and medical imaging tools. His lab graduated Dr. Yi Sheng (now at University of South Florida) and Dr. Zhepeng Wang (Amazon Applied Scientist). Current research explores quantum machine learning, noise mitigation, and fairness in AI for edge devices.
Lisa Grant Ludwig is a Professor in the Department of Population Health and Disease Prevention at the University of California, Irvine (UCI). She is a nationally recognized expert in earthquake science, focusing on translating geophysical research into policy for disaster risk reduction. Her work bridges seismology, public health, and policy implementation. PhD in Geology with Geophysics minor (Caltech) MS in Environmental Engineering Science (Caltech) BS in Applied Environmental Earth Science (Stanford) Dr. Ludwig's research centers on earthquake dynamics, particularly along the San Andreas Fault, advancing disaster resilience through innovative nowcasting techniques using AI and machine learning. Her publications demonstrate expertise in seismic hazard modeling, geodetic imaging, and community preparedness studies. Recent publications highlight AI-enhanced earthquake prediction (QuakeGPT), temporal-spatial nowcasting models, and applications of geodetic data for crustal deformation analysis. These works integrate machine learning with traditional seismological methods to improve hazard forecasting. President of Seismological Society of America NASA 2012 Software of the Year Medal Featured on Science magazine cover Congressional Testimony provider Active Federal Advisory Committee member Her interdisciplinary approach combines geophysics, public health policy, and computational science. Current projects focus on earthquake nowcasting, fault zone analysis, and developing accessible geospatial tools like GeoGateway for disaster response.
Sudip Vhaduri is an Assistant Professor in the School of Applied and Creative Computing at Purdue University, where he serves as Director of the mobile Artificial Intelligence (mAI) Laboratory. He holds appointments with several Purdue research centers including the Applied AI Research Center (AARC), Center for Education to Research in Information Assurance and Security (CERIAS), Purdue Institute of Inflammation, Immunology and Infectious Disease (PI4D), and the Institute for a Sustainable Future (ISF). Dr. Vhaduri received his educational training from: Ph.D. in Computer Science and Engineering from University of Notre Dame M.Sc. in Computer Science from University of Memphis B.Sc. in Computer Science and Engineering from Bangladesh University of Engineering and Technology His research focuses on the intersection of artificial intelligence and mobile computing, with particular emphasis on machine learning, deep learning, and federated machine learning approaches. Dr. Vhaduri's work integrates three major application areas: health informatics leveraging smartphone and wearable sensing, continuous user authentication using physiological and behavioral biometrics, and reliable discovery of places of interest using alternative sensor data. His research has been featured in prestigious outlets such as Forbes Magazine. Dr. Vhaduri collaborates extensively with interdisciplinary researchers from medical and nursing schools at institutions including University of Cincinnati and Indiana University, as well as industrial research institutions like IBM Research. He is an active member of multiple IEEE societies including IEEE Computer Society, IEEE Signal Processing Society, IEEE Geoscience and Remote Sensing Society, and IEEE Engineering in Medicine and Biology Society. He teaches courses including Machine Learning for Smart Sensing (F'21-F'24), Data Fusion for Machine Learning (S'23-S'24), and Database Fundamentals (S'22-present). Dr. Vhaduri actively seeks highly motivated PhD students for research in ML/DL/FedML, IoT, and Mobile & Wearable Computing, with multiple funded positions available.
Jost-Diedrich Graf Von Hardenberg is a Full Professor in the Department of Environmental, Land and Infrastructure Engineering (DIATI) at the Polytechnic University of Turin. He serves as the Energy and Climate Change Area Coordinator and is a Scientific Advisor of the HPC-AI Advisory Council. His research focuses on climate science and geophysical fluid dynamics, with significant contributions to understanding climate change impacts and Earth system modeling. Professor Von Hardenberg's research interests span multiple areas of climate science, including climate dynamics, geophysical fluid mechanics, hydrological cycle analysis, numerical climate modeling, and precipitation downscaling. His work particularly emphasizes climate tipping points and extremes, Rayleigh-Bénard convection in geophysical contexts, high-resolution Earth-system climate modeling, and stochastic approaches to precipitation downscaling. His research integrates theoretical, computational, and observational approaches to address fundamental questions about climate system behavior and change. His recent publications demonstrate a strong focus on Atlantic Meridional Overturning Circulation (AMOC) dynamics, climate extremes in Alpine regions, urban climate effects, and interdisciplinary applications of climate science to ecological and conservation challenges. His work bridges fundamental climate dynamics with practical applications for climate adaptation and environmental management. Among his notable recognitions are the Research in Paris award from the Maire de Paris (2009) and Fellowships at the London School of Economics and Political Science (2001-2002, 2004-2006). He has participated in numerous research networks including the European Geosciences Union section 'Nonlinear Processes in Geophysics' (2004-2012), the EC-Earth Consortium (2012-present), and COST Action ES0805 TERRABITES 'The terrestrial biosphere in the earth system' (2010-2014). Professor Von Hardenberg actively supervises PhD students including Marianna Albanese, Maria Clara Corda, Sara Filippini, and Jacopo Grassi, among others. He leads multiple significant research projects such as ROTurb (Resolving Ocean Macroscale Turbulence), LocClima (Impact of LOCal conditions on Italian microCLIMAtes), CRAWL (Carbon Release in A Warming cLimate), and CliMOC (Climate Impacts of the Atlantic Meridional Overturning Circulation), with funding from EU, national programs, and commercial contracts. He is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, where he contributes expertise in climate data analysis and modeling. His collaborative work extends to multiple institutions through non-commercial agreements with the National Research Council and various international research consortia focused on climate system understanding.
Professor Gary Hampson is a Professor of Sedimentary Geology at the Department of Earth Science & Engineering, Faculty of Engineering, Imperial College London. His roles include Director of Undergraduate Studies (2023–present) and former Director of the Petroleum Geoscience MSc course. He holds affiliations with the Energy Futures Lab, Grantham Institute, and NORMS research groups. Hampson's research focuses on depositional systems, stratigraphy, and subsurface reservoir characterization, with applications to CO2 storage and fluid flow dynamics. Education: PhD in Sedimentology and Sequence Stratigraphy, University of Liverpool (1991–1995) BA in Natural Sciences (Geology), University of Cambridge (1988–1991) Research Interests: His work integrates sedimentology, stratigraphy, and reservoir modeling to understand subsurface fluid dynamics. Key areas include sediment dispersal patterns, stratigraphic architecture, and the impact of heterogeneity on reservoir performance. Recent studies emphasize CO2 storage potential in sedimentary systems and the application of advanced modeling techniques. Articles Trends: Recent publications highlight CO2 storage mechanisms, sedimentological heterogeneity in reservoirs, and the use of machine learning in geological analysis. His work bridges theoretical stratigraphy with applied reservoir engineering, particularly in carbonates and fluvio-deltaic systems. Awards: 2020 IAMG Best Paper Award 2016 AAPG Bennison Lecturer 2010 SEPM Excellence Awards Advising & Grants: Hampson has advised numerous students and led projects on reservoir characterization. His editorial roles include co-chief editor of the Journal of Sedimentary Research and guest editorships in Petroleum Geoscience. Labs/Teams: Active in the Energy Futures Lab and NORMS initiative, focusing on novel reservoir modeling and simulation for subsurface energy systems.
Professor Weimin Huang is a full Professor in the Faculty of Engineering and Applied Science at Memorial University of Newfoundland, where he has served since 2010 and became a full professor in 2019. He held the position of Department Deputy Head from 2020 to 2023. Education: BSc in Radio Physics (Radio Wave Propagation and Antennas), Wuhan University, 1995 MSc in Radio Physics (Radio Wave Propagation and Antennas), Wuhan University, 1997 PhD in Space Physics, Wuhan University, 2001 MEng in Electrical and Computer Engineering, Memorial University of Newfoundland, 2004 Postdoctoral Fellowship in Electrical and Computer Engineering, Memorial University of Newfoundland, 2007 Research Focus: Huang specializes in radar-based ocean remote sensing , with core expertise in high-frequency ground wave radar (HF radar) , GNSS Reflectometry , and synthetic aperture radar (SAR) . His work targets ocean surface parameter mapping including wind speed, oil spills, ship detection, and sea ice monitoring through advanced digital image processing and applied electromagnetics . Recent innovations integrate deep learning (CNNs, physics-informed models) with radar data for enhanced environmental monitoring. Publication Trends: His 2025 publications reveal a strong shift toward AI-driven solutions in remote sensing, with 5 high-impact papers in IEEE TGRS and Remote Sensing focusing on wind speed estimation (using GNSS-R and wavelet-CNN hybrids), oil spill mapping via SAR, ship detection with HF radar, and climate change analysis. These works demonstrate cross-disciplinary integration of machine learning with geophysical remote sensing. Scientific Awards: No awards were documented in the source material. Advising & Collaboration: With 358 co-authors including Bahram Salehi and Biyang Wen, Huang maintains a robust global research network. While specific student supervision isn't listed, his leadership role and publication volume indicate active graduate mentoring. The text mentions no grant details. Research Infrastructure: His work operates within Memorial University's engineering faculty, leveraging radar facilities for ocean sensing. Collaborations span institutions including Wuhan University and SUNY, suggesting participation in international radar remote sensing consortia focused on maritime applications.
Gyula Mate Kovács is a Research Fellow (Postdoctoral Researcher) at the Department of Geosciences and Natural Resource Management, Faculty of Science, University of Copenhagen. His research is funded by the Novo Nordisk Foundation through the Global Wetland Center. Education Ph.D. in Remote Sensing of Wetlands, University of Copenhagen (2020–2024) M.Sc. in Geography and Geoinformatics, University of Copenhagen (2017–2019) B.Sc. in Environmental Management, Birkbeck University of London (2013–2017) Research Focus Dr. Kovács specializes in AI-driven remote sensing for wetland ecosystem analysis. His work integrates machine learning, deep learning, and satellite data fusion to quantify natural/anthropogenic impacts on wetlands at global scales. Key methodologies include time series analysis, cloud computing, and convolutional neural networks for applications like carbon mapping, water body detection, and land-use impact assessment. Publication Trends His 7 recent publications demonstrate a strong focus on wetland dynamics using satellite remote sensing, with themes spanning deep learning applications (CNN U-Net algorithms), greenhouse gas emissions in croplands, continental-scale wetland inventories, and ecosystem change detection. Research consistently employs advanced AI techniques to address environmental challenges in diverse regions like the Sahel and Europe. Funding & Affiliation Supported by the Novo Nordisk Foundation via the Global Wetland Center, his work advances wetland monitoring capabilities. He collaborates with international teams on projects involving satellite data processing and ecological modeling.
Elias Willberg is a Postdoctoral Researcher at the Department of Geosciences and Geography, University of Helsinki. He is affiliated with the Helsinki Institute of Urban and Regional Studies (Urbaria), the Helsinki Institute of Sustainability Science (HELSUS), and the Digital Geography Lab. His work focuses on urban sustainability, transportation planning, and environmental exposure during active mobility.
Fraser King is an incoming Assistant Professor in the Department of Atmospheric and Oceanic Sciences (AOS) at the University of Wisconsin–Madison, starting in Winter 2026. He holds a PhD in Machine Learning and Remote Sensing of Precipitation from the University of Waterloo (2022) and is currently a postdoctoral research associate at NASA Goddard Space Flight Center. His research integrates machine learning with atmospheric physics to advance precipitation and snowfall retrieval, cloud microphysics, and climate modeling. He has held research positions at the University of Michigan and NASA Jet Propulsion Laboratory. His research interests include: Climate and Climate Change Radiation and Remote Sensing Synoptic Meteorology Atmospheric and Cloud Physics Large Scale Dynamics Machine Learning and Model Interpretability Arctic Snowfall Prediction His recent publications reflect a strong trend in applying deep learning (e.g., U-Net, CNNs) and unsupervised methods (PCA, t-SNE, UMAP) to radar and satellite data for precipitation and snow microphysics. Key themes include radar gap inpainting, melting layer detection, and dimensionality reduction for physical interpretation. His work bridges geoscience and AI, aiming for interpretable models that enhance physical understanding. Scientific awards and professional service include: Finalist for the 2023 Governor General's Gold Medal, University of Waterloo Associate Editor, Journal of Atmospheric and Oceanic Technology (AMS) Member, AMS Committee on Artificial Intelligence Applications to Environmental Science Executive Council Member, AGU Precipitation Technical Committee Executive Member, Eastern Snow Conference Research Board Fraser King has mentored students through research projects and led educational initiatives such as a 12-week course on machine learning for land cover classification. He has secured research experience through internships at Aquanty Inc. and multiple NASA-affiliated institutions. He founded MapsByFraser, a company combining cartography and satellite data, and has collaborated with Google's Quantum AI team. His technical skills span Python, deep learning frameworks, and high-performance computing platforms. He leads several major research projects: Towards Interpretable Physical Models : Using sparse autoencoders and nonlinear dimensionality reduction to interpret geoscience models. Microphysical Dimensionality Reduction : Applying PCA, t-SNE, and UMAP to identify physical modes in precipitation data. BlindPaint : A U-Net for radar gap inpainting in spaceborne systems. DeepPrecip : A deep learning model for surface precipitation retrieval. iPhone LiDAR : Using consumer smartphones for snow depth measurement via drones. NRCan Machine Learning Land Cover Classifier : Training ML models on Sentinel-2 data. Climate Model Calibration : Using ML to correct biases in snow-related climate variables. CloudSat Snowfall Validation : Validating high-latitude snowfall estimates. Snow Modelling : A Rust-based physical/temperature-index snow model.
Thorsten W. Becker is a Professor of Earth and Planetary Sciences at the Jackson School of Geosciences, University of Texas at Austin, holding the Shell Companies Foundation Distinguished Chair in Geophysics. He is also a Senior Research Scientist at the Institute for Geophysics and a Faculty Associate at the Institute for Computational Engineering and Sciences. His research focuses on the co-evolution of planetary interiors and surface systems, integrating geodynamic modeling, seismology, and field data to study processes like plate tectonics, mantle convection, and seismic anisotropy. Becker earned a Diplom in Physics from Goethe University (Frankfurt) and a Ph.D. in Geophysics from Harvard University. He has held academic positions at the University of Southern California and Scripps Institution of Oceanography. His honors include the Augustus Love Medal (EGU), Evgueni Burov Medal (IUGG), and Fellow of the American Geophysical Union. His teaching interests span Tectonic Geodynamics, Natural Hazards, and Numerical Modeling. He chairs the US National Academies’ Standing Committee on Solid Earth Geophysics and has led major initiatives like the NSF-funded Megathrust Modeling Framework. His work bridges computational geoscience, tectonics, and geodynamic theory, with over 160 peer-reviewed publications. Recent research emphasizes mantle flow dynamics, slab interactions, and AI-driven seismic analysis. Key contributions include models of subduction termination, slab-induced cratonic thinning, and the role of mechanical anisotropy in tectonic processes.
Ameya Jagtap is an Assistant Professor (Tenure-Track) in the Department of Aerospace Engineering at Worcester Polytechnic Institute (WPI), USA. Prior to this, he served as an Assistant Professor of Applied Mathematics (Research) at Brown University from 2021 to 2024. He holds a Ph.D. and M.E. in Aerospace Engineering from the Indian Institute of Science (IISc), and completed postdoctoral research at TIFR-CAM (India) and Brown University's Division of Applied Mathematics. His research bridges mechanical/aerospace engineering, applied mathematics, and computation, focusing on scientific machine learning algorithms that integrate data and physics. Key areas include physics-driven deep learning, uncertainty quantification, multi-scale simulations, and novel neural network architectures like quantum and graph networks. He serves on editorial boards for Neural Networks , Neurocomputing , and others. His work emphasizes interpretable neural operators for PDE solutions, domain decomposition methods, and adaptive activation functions to enhance PINN convergence. Notable contributions include XPINNs (extended physics-informed neural networks) and causal sweeping frameworks for PDEs. His research has been widely cited, particularly for PINN applications in supersonic flows and high-dimensional PDEs. Jagtap has delivered invited talks at institutions like Los Alamos National Laboratory, Tsinghua University, and the Alan Turing Institute. He is also recognized as a Top 2% World Scientist by Stanford University.
Jim Tørresen is a Professor of Computer Science at the Department of Informatics, University of Oslo, where he has been employed since 1999 (Associate Professor 1999-2005, Professor since 2006). He serves as group leader for the Robotics and Intelligent Systems (ROBIN) research group and is also a Principal Investigator at the Centre for Interdisciplinary Studies in Rhythm, Time and Motion (RITMO). His academic career includes visiting positions at Cornell University's Creative Machines Lab (2010-2011) and Kyoto University in Japan (1993-1994). His educational background includes a Dr.ing. (Ph.D.) in Computer Architecture from the Norwegian University of Science and Technology (1996) and an M.Sc. in Computer Architecture from the same institution (1991). Before his academic career, he worked in industry at Navia Aviation (1998-1999) and NERA Telecommunications (1996-1998). Tørresen's research spans artificial intelligence, robotics, and bio-inspired computing. His work focuses on biology-inspired algorithms, programmable logic (FPGA), robotics (simulation, prototyping, control), and human-robot interaction. He has made significant contributions to areas including evolutionary computing, reconfigurable hardware, and adaptive systems. His research often bridges theoretical computer science with practical applications in healthcare, music, and industrial settings. His recent publications demonstrate a strong focus on human-robot interaction, particularly in healthcare contexts for elderly care, as well as applications in sports science, musical robotics, and geological engineering. His work shows a consistent pattern of interdisciplinary research that combines machine learning techniques with domain-specific challenges. Tørresen has also authored a popular science book on artificial intelligence in the "what is" series by Universitetsforlaget, which discusses fundamental concepts, methods, future perspectives, and ethical aspects of AI. He has been active in academic leadership, serving as General Chair for the 22nd International Conference on Field Programmable Logic and Applications (FPL) in 2012 and the 9th Joint IEEE International Conference of Developmental Learning and Epigenetic Robotics in 2019. As group leader of ROBIN, he oversees research on intelligent systems that operate in dynamic environments requiring runtime adaptation. The group works at both fundamental and applied levels, using evolutionary algorithms for robot learning and machine learning techniques for classification and recognition tasks in various application domains.
Dr. Jiju Poovvancheri is an Associate Professor in the Department of Math & Computing Science at Saint Mary’s University, Halifax, Canada. He holds affiliations with the Graphics & Spatial Computing Lab and previously held postdoctoral positions at the University of Victoria and University of Calgary. His research focuses on computer graphics, 3D vision, and machine learning, with applications in virtual/augmented reality, autonomous robotics, urban planning, and bio-mechanical studies. Key research areas include point cloud processing, semantic surface reconstruction, spatial data structures, and geometric deep learning. Education: PhD from Indian Institute of Technology Madras (2011–2014), supervised by Prof. Ramanathan Muthuganapathy. Postdoctoral work at University of Calgary (EYES-HIGH Fellowship, 2015–2017) and University of Victoria (MITACS Elevate Fellowship, 2018). Research & Awards: Winner of MITACS Elevate Fellowship (2018), EYES-HIGH Fellowship (2015–2017), and multiple grants including NSERC DG (2019–2026). His work has been supported by NVIDIA GPU hardware, NSERC, CFI, and industry partners like Modest Tree Media and Caterpillar. Professional Activities: Associate Editor for IEEE Access , Guest Editor for special issues in Remote Sensing and Sensors , and reviewer for top conferences like CVPR, ICCV, and ECCV. Member of ACM SIGGRAPH, Solid Modeling Association, and IEEE Geoscience & Remote Sensing Society. Lab & Collaborations: Leads the Graphics & Spatial Computing Lab, collaborating with institutions globally. Current projects include "Interaction and navigation in virtual spaces" and industry partnerships with Modest Tree Media for real-time object recognition. Advising: Supervised over 20 graduate and undergraduate students, with notable alumni advancing to roles at ReelData AI, Royal Canadian Air Force, and academic institutions like Dalhousie University.
Professor Joao Porto de Albuquerque is a Professor in Urban Analytics at the University of Glasgow's School of Social and Political Sciences, serving as Deputy Director of the Urban Big Data Centre (UBDC). He holds affiliate roles at the Centre for Research & Development in Adult and Lifelong Learning (CR&DALL) and previously led the Institute for Global Sustainable Development at the University of Warwick. His interdisciplinary work bridges geography and computer science, focusing on urban sustainability, participatory data methods, and addressing global inequities through community-driven approaches. Education: PhD in Computer Science (University of Campinas, 2006), postdoctoral research at University of Hamburg (2006-2008), and prior academic roles at University of São Paulo and Heidelberg University. Research emphasizes participatory urban analytics, citizen science, and AI ethics in disaster resilience, funded by major grants (e.g., £5.5M+ as PI from ESRC, Gates Foundation). Research interests include urban resilience, climate adaptation, participatory GIS, and data equity in marginalized communities. Notable projects include the Waterproofing Data Project (flood resilience in Brazil), IDEAMAPS (deprivation mapping in Africa), and co-creation of Earth-observation AI systems for slum areas. Publications span over 120 peer-reviewed articles, focusing on spatial data analysis, participatory methods, and urban sustainability. Awards include the ESRC Impact Prize for societal contributions to flood resilience. Active in global collaborations, his work integrates social sciences with environmental and technical disciplines.
Andrea Scott is an Associate Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, Faculty of Engineering. Her research focuses on fluid dynamics, remote sensing, and machine learning applications in environmental systems. She holds a Doctorate in Mechanical Engineering from the University of Waterloo (2008) and has taught courses such as ME 351 (Fluid Mechanics) and SYDE 621 (Numerical Methods). Notable awards include the 2022 Outstanding Performance Award and 2021 Distinguished Performance Award from the University of Waterloo. Research interests span turbulence modeling, data-driven approaches, and physically inspired neural networks. Her work includes developing algorithms for sea ice concentration estimation, SAR imagery analysis, and fluid flow simulations. She collaborates with groups like the Vision and Image Processing Lab and the Remote Sensing of Environmental Change group. Current projects involve small object detection in remote sensing and graph neural networks for unstructured grid problems. Education: PhD, Mechanical Engineering, University of Waterloo, Canada (2008) MASc, Mechanical Engineering, McMaster University, Canada (2001) BASc, Mechanical Engineering, University of Waterloo, Canada (1999) Teaching responsibilities include undergraduate and graduate courses in fluid mechanics and systems engineering. She actively mentors students through the IEEE GRSS Women-to-Women Mentorship program and serves as an Associate Editor for the AGU Journal of Machine Learning and Computation. Her lab oversees over 20 current and past graduate students, focusing on topics like space debris tracking, AI-driven environmental modeling, and sea ice dynamics. Research outputs include over 40 publications in journals such as Physical Review Fluids and IEEE Transactions on Geoscience and Remote Sensing .
