Artur W. Dubrawski is an Alumni Research Professor of Computer Science and Director of the Auton Lab at Carnegie Mellon University's School of Computer Science. He leads interdisciplinary research on Artificial Intelligence, Machine Learning, and Robotics with real-world applications in healthcare, nuclear safety, food safety, and counter-human trafficking. His work focuses on bridging gaps between data-driven AI and empirical sciences through probabilistic modeling, predictive analytics, and time-series intelligence. Lab: Auton Lab (founded 1993) Collaborations: Allegheny County Health Department, USDA, CDC, U.S. Army Research Impact: AI for wastewater-based COVID-19 forecasting, radiological inspection systems, and hospital infection detection His students and affiliates include current PhD candidates Angela Chen, Emma Erickson, Cecilia Morales, Willa Potosnak and past researchers like Benedikt Boecking (co-inventor of Interactive Weak Supervision). The lab has spun off startups like Marinus Analytics (IBM XPrize finalists) and developed open-source tools like auton-survival for survival analysis. Key Grants: $10.5M U.S. Army contract for AI-driven predictive maintenance research.
David B. Lindell is an Assistant Professor in the Department of Computer Science at the University of Toronto, with affiliations to the Vector Institute and AXL. He is a founding member of the Toronto Computational Imaging Group. His research focuses on physically based intelligent sensing, integrating physical models, signal processing, and AI to advance sensing systems. Notable projects include imaging around corners, through scattering media, and developing machine learning algorithms for 3D scene reconstruction. Education: Ph.D. in Computational Imaging from Stanford University (advisor: Gordon Wetzstein). Awards include the 2024 Ontario Early Researcher Award and the Best Student Paper at CVPR 2025. His work combines computational imaging with applications in computer graphics and autonomous systems. Research interests span non-line-of-sight imaging, single-photon sensing, and neural representations. Key contributions include the Light-Cone Transform (Nature 2018), confocal diffuse tomography (Nature Communications 2020), and AutoInt (CVPR 2021). His lab develops systems for 3D reconstruction, transient imaging, and photon-efficient sensors. Selected grants and support: NSF CAREER Award, DARPA REVEAL program, and KAUST Visual Computing Center funding. Active collaborations with industry and academic institutions on autonomous driving and medical imaging applications.
Professor Emily So serves as Deputy Head of the School of Arts and Humanities at the University of Cambridge and directs the Cambridge University Centre for Risk in the Built Environment (CURBE). A chartered civil engineer with extensive field experience, she holds leadership roles in the Open-Oxford-Cambridge AHRC Doctoral Training Partnership and chairs the Faculty EDI Committee. Her research focuses on urban risk and resilience , particularly in earthquake-prone regions. Combining structural engineering with epidemiological approaches, she develops innovative casualty estimation models and engages directly with affected communities worldwide. Her work spans seismic safety, disaster epidemiology, and remote sensing applications for rapid damage assessment. Professor So's publication trends reveal strong emphasis on machine learning for disaster risk modeling , with recent work featuring graph neural networks, deep clustering for urban morphology, and LSTM-based population forecasting. Her research bridges engineering, social sciences, and data science to address resilience in developing nations. 2010 Shah Family Innovation Prize (Earthquake Engineering Research Institute) Fellow of the Institution of Civil Engineers (FICE) Scientific Advisory Group for Emergencies (SAGE) member advising UK government As Director of CURBE, she leads interdisciplinary collaborations with EEFIT, Global Earthquake Model (GEM), World Bank, and USGS. Her field investigations following major earthquakes inform practical solutions for vulnerable communities, notably contributing to the 2017 World Building of the Year design in China. Current work includes sabbatical research for 2025-2026 focused on decolonizing architectural approaches to disaster resilience. Professor So maintains active roles in professional organizations and international disaster response frameworks, with her CURBE team developing methodologies now implemented globally for seismic safety improvements.
Andreas Rietbrock is Professor and Director of the Geophysical Institute (GPI) at the Karlsruhe Institute of Technology (KIT) , Germany, where he also serves as Dean of Studies for Geophysics . He is a leading expert in earthquake seismology, seismic tomography, and subduction zone dynamics, with a strong focus on integrating advanced observational techniques and computational methods. Education: While specific degrees are not listed in the provided text, his extensive publication record and leadership roles indicate advanced academic training in geophysics and seismology. Research Interests: His work spans a wide range of topics including: Seismic imaging of subduction zones (e.g., Nazca, Lesser Antilles) Earthquake rupture dynamics and fault mechanics Volcanic seismology and magma transport Full waveform inversion and AI-enhanced seismic analysis Distributed Acoustic Sensing (DAS) applications Induced seismicity and reservoir monitoring Research Trends: His recent publications (2022–2025) emphasize the use of dense seismic arrays, AI-based data processing, and multi-method tomography to study complex tectonic environments. Key themes include high-resolution imaging of slab structures, fluid migration in subduction zones, and the integration of DAS and machine learning for seismic monitoring. Scientific Contributions: Andreas has led major international projects such as the ANTICS Large-N deployment in Albania and the VoiLA project in the Lesser Antilles. He has published extensively in top-tier journals like Nature , Geophysical Research Letters , and Journal of Geophysical Research , with over 200 peer-reviewed articles. Teaching and Supervision: He teaches courses such as "Introduction to Geophysics II", "Seismology", and "Current Topics in Seismology and Risk". While specific student names are not listed, his role as Dean and principal investigator on numerous projects indicates active supervision of graduate students and postdocs. Labs and Teams: He leads the seismology group at GPI, coordinating large-scale deployments of seismic instruments, including ocean-bottom seismometers and fiber-optic DAS systems. His team collaborates globally with institutions in Europe, South America, and Asia.
Yehuda Ben-Zion is a Professor of Earth Sciences at the University of Southern California (USC), affiliated with the Dornsife College of Letters, Arts and Sciences. He serves as Director of the Statewide California Earthquake Center (SCEC). His expertise lies in geophysics and seismology, with a focus on earthquake mechanics, fault dynamics, and seismic hazard assessment. He holds a Ph.D. in Geophysics and Seismology from USC (1990) and a B.S. in Geology and Physics from The Hebrew University of Jerusalem (1982). Research interests include physics of earthquakes and faults, high-resolution fault zone imaging, earthquake source properties, and dynamic rupture processes. Recent work emphasizes multi-scale modeling of rupture zones, seismic velocity monitoring using anthropogenic signals (e.g., train tremors), and probabilistic seismic hazard analysis frameworks like CyberShake. He leads projects such as Quakeworx, an open-source earthquake simulation platform, and investigates fault zone architecture in regions like the San Andreas, San Jacinto, and Marmara faults. His studies address critical questions about large earthquake mechanisms, ground motion prediction, and the interplay between tectonic stress and seismicity patterns. He has pioneered the use of dense seismic arrays and machine learning to analyze seismic data, advancing understanding of fault zone processes and their implications for hazard mitigation.
Eamonn Keogh is a Professor in the Computer Science and Engineering Department at the University of California, Riverside. His pioneering work centers on the Matrix Profile, a transformative approach to time series data mining enabling efficient solutions for motif discovery, anomaly detection, and similarity search. His algorithms (STAMP, STOMP, SCRIMP, DAMP, SCAMP) offer exact, parameter-free, and scalable solutions across domains like seismology, bioinformatics, and industrial IoT. Research areas include: Development of ultra-fast algorithms for time series joins and motif discovery at unprecedented scales (breaking the 100 million barrier) GPU acceleration for time series mining Domain-agnostic methods for semantic segmentation and anomaly detection Novel primitives like Time Series Chains, Snippets, and Consensus Motifs His work is highly cited and recognized by industry and academia, with applications ranging from NASA's Cassini mission to detecting BGP anomalies in computer networks.
Maarten de Hoop is the Simons Chair and Professor of Computational and Applied Mathematics at Rice University, part of the George R. Brown School of Engineering. He holds visiting roles at MIT and the Chinese Academy of Sciences. His research spans seismic wave analysis, inverse problems, deep learning, and planetary seismology. He earned his Ph.D. in Technical Sciences from Delft University of Technology (1992), and earlier degrees from Utrecht University. Notable awards include the 1996 J. Clarence Karcher Award and 2001 Fellowship from the Institute of Physics. His work integrates computational mathematics with geophysics, focusing on extracting signal information from large datasets, developing novel inverse scattering methods, and applying deep learning to geoscience challenges. Recent studies include transformer models for in-context learning, semialgebraic neural networks, and seismic waveform foundation models like SeisLM. He leads the Geo-Mathematical Imaging Group, fostering interdisciplinary projects in planetary missions and data-driven discovery.
John H. Shaw is the Harry C. Dudley Professor of Structural and Economic Geology and Professor of Environmental Science & Engineering at Harvard University's School of Engineering and Applied Sciences (SEAS). He specializes in structural geology, earthquake hazards, and geomechanics, with a focus on thrust fault systems, fault-related folding, and seismic risk assessment in regions like California and China. His research integrates field observations, 3D modeling, and geomechanical simulations to understand fault dynamics and their implications for societal safety. Shaw's work emphasizes quantitative analysis of fault geometry, slip rates, and rupture processes. Key projects include modeling ground deformation during earthquakes, assessing seismic hazards in fold-thrust belts, and investigating reservoir-induced seismicity. He leads the Structural Geology & Earth Resources Group and contributes to collaborative initiatives like the Southern California Earthquake Center (SCEC). His articles highlight advancements in fault system modeling, including 3D structural reconstructions, distinct element method applications, and coupling geomechanical models with fluid flow simulations. Recent studies focus on the Wilmington blind-thrust fault beneath Los Angeles, the Ventura fault system, and tectonic evolution of the Canadian Rockies and Qaidam Basin. Shaw's research also addresses interdisciplinary challenges such as stochastic velocity modeling for earthquake ground motion prediction and developing open-source tools like the SCEC Unified Community Velocity Model (UCVM). His work bridges fundamental structural geology with applied seismic hazard mitigation strategies.
Massachusetts Institute of TechnologyUnited States
Sai Ravela is a Principal Research Scientist in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at the Massachusetts Institute of Technology (MIT). His research focuses on nonlinear stochastic dynamics, coherent fluid systems, uncertainty quantification, and autonomous observing technologies. He specializes in developing data-driven methodologies for natural hazard detection, climate change impacts, and environmental risk assessment. Ravela’s work integrates computational science with geophysical applications, including storm surge modeling, extreme rainfall analysis, and geothermal exploration. He pioneers techniques like neural dynamical systems and adversarial learning to improve predictive accuracy in nonstationary climate regimes. His contributions span environmental monitoring systems, autonomous aircraft resilience frameworks, and policy-informed climate vulnerability assessments. Key research areas include: Coastal flood risk in Bangladesh and Vietnam Dynamic data-driven applications systems (DDDAS) Machine learning for geosciences and environmental systems Uncertainty quantification in complex fluid dynamics He leads interdisciplinary projects at MIT’s Computational Science and Engineering (CSE) program, advancing methods for data assimilation, surrogate modeling, and real-time environmental observatories. His innovations bridge theoretical frameworks with practical solutions for climate adaptation and disaster resilience.
Hank Childs is a Professor in the School of Computer and Data Sciences at the University of Oregon, specializing in scientific visualization and high-performance computing. He leads the Research Group on Computing and Data Understanding at eXtreme Scale (CDUX) and has held leadership roles including Interim Executive Director of the School of Computer and Data Sciences. His educational background includes a Ph.D. (2006) and B.S. (1999) in Computer Science from the University of California at Davis. Prior to academia, he worked for 14 years at Lawrence Livermore and Lawrence Berkeley National Laboratories, where he served as architect of the VisIt open-source visualization tool. Research interests center on visualizing extreme-scale scientific datasets from supercomputers, with a focus on in situ visualization for cosmology, seismology, and fluid dynamics. He has pioneered projects like VTK-m and Ascent, and his work explores power-performance tradeoffs and data-parallel algorithms for GPUs. Recent publications emphasize scalable visualization techniques for exascale computing, with 15 notable works from 2021-2020 covering particle advection, in situ triggering, and power-aware frameworks. His research has been honored with multiple best paper awards at IEEE LDAV, EGPGV, and SC conferences. DOE Early Career Award (2012) University of Oregon Faculty Excellence Award (2018) 4+ million dollars in research funding since 2013 5 Best Paper awards in 2021 alone As an educator, he received four consecutive CIS Best Teacher Awards (2014-2019). He has served as Associate Editor for IEEE Transactions journals and organized numerous visualization workshops including Dagstuhl seminars and Shonan workshops.
Professor Saskia Goes is a Professor of Geophysics at Imperial College London's Department of Earth Science & Engineering within the Faculty of Engineering. She specializes in geodynamics, subduction dynamics, and seismic hazard analysis using numerical modeling and geophysical data interpretation. Her affiliations include the Dynamic Earth and Hazards groups at the Imperial Centre for Geohazards Dynamics. Education: PhD in Geophysics from UC Santa Cruz (1995), Drs (BSc/MSc equivalent) from Utrecht University (1990). Prior roles include SNF Professor of Tectonophysics at ETH Zurich (2003-2005), Visiting Assistant Professor at the University of Michigan (1995-1996), and postdoctoral research at Utrecht University (1996-1999). Research focuses on mantle dynamics, lithosphere structure, and subduction zone processes. Her work integrates seismic imaging, machine learning, and numerical simulations to study phenomena like slab dynamics, mantle plumes, and fluid migration. Key themes include the interplay between tectonic forces and geochemical processes in continental and oceanic settings. Publications emphasize subduction zone processes, seismic tomography, and induced seismicity. She has led projects like the VoiLA initiative studying volatile recycling in the Lesser Antilles. Awards and recognition include invited lectures at leading conferences (AGU, EGU) and universities worldwide. Teaching includes undergraduate geodynamics, geohazards courses, and advanced MSc modeling modules. Active in promoting geohazard research through interdisciplinary collaboration and public engagement.
California Institute of Technology (Caltech)United States
John C. Doyle is the Jean-Lou Chameau Professor of Control and Dynamical Systems, Electrical Engineering, and BioEngineering at the California Institute of Technology (Caltech), where he holds appointments in the Division of Engineering and Applied Science with primary affiliation in the Control and Dynamical Systems Department. His research bridges theoretical foundations with applications across biological, technological, medical, and ecological networks. He earned a BS and MS in Electrical Engineering from MIT (1977) and a PhD in Mathematics from UC Berkeley (1984), followed by consultancy at Honeywell Systems and Research Center (1976-1990). MIT: BS & MS in Electrical Engineering (1977) UC Berkeley: PhD in Mathematics (1984) Doyle's research centers on universal laws and architectures in complex systems, emphasizing robustness-efficiency tradeoffs, speed-accuracy tradeoffs (SATs), diversity-enabled sweet spots (DeSS), bowtie/hourglass structures, and evolvability. His work pioneers System Level Synthesis (SLS) for control systems with sparse, local, saturating, delayed, noisy, quantized, and distributed (SLSDNQD) components, integrating control theory, computation, communication, and machine learning to address challenges from neural networks to infrastructure resilience. Key concepts include virtualization, horizontal transfer, and virality in multiscale systems. Analysis of his publication trends reveals consistent interdisciplinary impact across neuroscience (brain connectivity modeling), systems biology (metabolic oscillations), network science (internet topology), and physics (turbulence, earthquakes), with recurring themes of robust-efficiency limits and architectural principles governing complex networks. His work demonstrates exceptional translation from abstract theory to practical tools like the Matlab Robust Control Toolbox and Systems Biology Markup Language (SBML). His scientific recognition includes: 1990 IEEE Baker Prize (ranked among top 10 most important mathematics papers 1981-1993) Three IEEE Automatic Control Transactions Awards (1998, 1999, 2021) ACM Sigcomm Paper Prize (2004) and Test of Time Award (2016) IEEE Control Systems Field Award (2004) Multiple early-career honors including IEEE Centennial Outstanding Young Engineer (1984) Doyle has mentored generations of students whose contributions include foundational software tools adopted globally. His research has secured sustained funding from NSF, NIH, and other agencies supporting theoretical advances in control frameworks and their applications to biomedical systems, network infrastructure, and environmental modeling. The SBML initiative exemplifies his group's impact in standardizing computational biology research. He leads a highly collaborative research ecosystem at Caltech that integrates engineers, biologists, neuroscientists, and computer scientists to develop universal principles for complex networks. Current efforts focus on translating theoretical insights into health technologies, resilient infrastructure, and climate-responsive systems through the application of robust-efficiency frameworks to emerging challenges in cyber-physical and biological domains.
Prof. Matt Pritchard is a Professor in Earth & Atmospheric Sciences at Cornell University, based at Snee Hall. His research focuses on volcanology, geodesy, and remote sensing, with a particular emphasis on using satellite data to monitor volcanic activity, deformation, and glacial interactions. He leads studies on global volcanic systems, including Indonesia's Semeru and Raung volcanoes, Chile's Cordón Caulle, and Bolivia's Uturuncu, applying techniques like InSAR, SAR, and thermal imaging. Key research interests include volcanic eruption dynamics, magma-hydrothermal systems, and the integration of multi-sensor datasets. He has contributed to developing tools like Hotspotter for automated volcanic thermal feature detection and has explored planetary volcanism (e.g., Venus). His work bridges geophysics, glaciology, and computational methods, addressing both Earth and extraterrestrial systems. Prof. Pritchard has received recognition such as the William Bowie Lecture (2022, 2023). His projects often involve international collaborations, including the CEOS Volcano Demonstrator initiative and the EarthDEM/ArcitcDEM projects. He also engages in geothermal energy research and seismic monitoring in Ithaca, NY.
Matej Varga is a Scientific Assistant and Postdoctoral Researcher at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, working in the Geosensors and Engineering Geodesy group under Prof. Andreas Wieser since 2021. His research spans geometrical geodesy, physical geodesy, and satellite geodesy, with applications in both theoretical and practical domains. Dr. Varga's research interests focus on spatial, temporal and spectral analysis of geodetic data, with particular expertise in geodetic reference systems and frames, gravity and geomagnetic field modeling at all temporal and spatial scales, and multi-GNSS multi-frequency positioning and monitoring. His work integrates geometrical and physical aspects of geodesy to address complex Earth observation challenges, particularly in infrastructure monitoring and geophysical applications. His recent publications demonstrate a strong trend toward high-precision geodetic applications for major scientific infrastructure, most notably the Future Circular Collider project, alongside important contributions to earthquake impact analysis, geomagnetic network development, and gravity field modeling. His research bridges traditional geodetic methods with modern computational approaches, including machine learning applications for point cloud registration. Dr. Varga is actively involved in the GSEG research group at ETH Zurich, contributing to the development of geodetic infrastructure and reference systems. His work has practical applications in infrastructure monitoring, earthquake analysis, and scientific projects requiring extreme geodetic precision.
California Institute of Technology (Caltech)United States
Xiaozhuo Wei is a Postdoctoral Scholar Research Associate in Geophysics at the California Institute of Technology (Caltech), affiliated with the Division of Geological and Planetary Sciences and the Department of Geophysics. His research focuses on geophysical monitoring of volcanic and tectonic processes, employing advanced techniques like fiber-optic geodesy, seismic tomography, and machine learning. He specializes in studying seismicity associated with volcanic eruptions, magma dynamics, and slow slip events in subduction zones, with significant contributions to understanding the 2018 Kīlauea eruption and Iceland's Reykjanes Peninsula eruptions. Key affiliations: Caltech’s Geophysics Department, Division of Geological and Planetary Sciences Research tools: Distributed Acoustic Sensing (DAS), ambient noise tomography, and offshore seismic arrays Field areas: Kīlauea Volcano (Hawaii), Iceland’s Reykjanes Peninsula, and Alaska’s subduction zones Wei’s work integrates multidisciplinary approaches to investigate crustal deformation, magma storage, and seismic hazard assessment. He has contributed to improving earthquake catalogs using offshore data and analyzing post-eruption seismicity patterns. His recent studies explore the spatial-temporal evolution of volcanic systems and the mechanics of dike intrusions using high-resolution geophysical methods. His research emphasizes real-time monitoring of volcanic processes and has advanced understanding of how seismic velocity changes reflect magma movement. Collaborations involve deploying ocean-bottom seismometers and machine learning algorithms to detect slow slip events in subduction zones, enhancing earthquake prediction capabilities. Notable projects include the 2023–2024 Reykjanes eruptions study and the analysis of the 2018 Kīlauea eruption’s seismic aftermath. His work bridges observational seismology with theoretical models of volcanic and tectonic systems, contributing to both fundamental science and practical hazard mitigation strategies.
