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Zichong Chen is a Researcher in the Atmospheric Chemistry Modeling Group at Harvard University, working under Prof. Daniel Jacob. His research focuses on quantifying greenhouse gas (GHG) and air pollutant emissions using satellite data, with a particular emphasis on methane emissions, carbon-climate interactions, and high-resolution inversion techniques. He develops tools like the Integrated Methane Inversion (IMI) 2.0 to provide actionable insights for climate stakeholders. His work integrates satellite observations (e.g., TROPOMI, GOSAT) with data assimilation and atmospheric modeling to construct frameworks for climate action. Research interests include global carbon cycle variability, methane emission quantifications across regions (China, U.S., Middle East, Africa), and agricultural ecosystem GHG emissions. He advances methodologies to link satellite data (e.g., Landsat) with environmental drivers for emissions estimation. Key contributions include high-resolution methane inventories (e.g., Global Rice Paddy Inventory) and studies on tropospheric OH variability. His work spans climate policy applications, such as improving national emission inventories via satellite data, and regional analyses of methane emissions in countries like Colombia, Oman, and South American nations. He collaborates globally to address climate mitigation through data-driven approaches.

• Carbon-Climate Interactions
• Methane Emission Quantification
• Satellite Remote Sensing
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Tim Tang is a Lecturer in Fluid Mechanics at the University of Manchester's Mechanical and Aerospace Engineering department. He joined in 2025 as an Assistant Professor in Fluids Simulation and Digital Twins. Previously, he held positions at the University of Oxford, including an Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship. His research focuses on extreme fluid events, machine learning applications in fluid mechanics, and oceanic infrastructure interactions. He actively supervises PhD/MSc students and leads projects on rogue waves, nonlinear hydrodynamics, and AI-driven models for marine energy. Education BEng in Mechanical Engineering, University of Nottingham (2013–2017) DPhil (PhD) in Engineering Science, University of Oxford (2017–2021) Research Interests His team applies cutting-edge machine learning techniques (e.g., Physics-Informed Neural Networks, Gaussian Processes) to study extreme waves, wave-structure interactions, and marine energy systems. Recent work includes BBC Science Focus-featured research on rogue waves and AI models for offshore renewable energy. Grants & Awards Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship (2023) First place, Osborne Reynolds Day Competition (2022) Advising & Collaborations Current PhD/MSc students: Chengyuan Hao, Jialun Chen, Xinyu Liu, Thomas Monahan, Zhenhao Li Collaborations span global institutions like MIT and ZheJiang University.

• Extreme Waves
• Nonlinear Wave Dynamics
• Machine Learning in Fluid Mechanics
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Ralf Engbert is a Professor of Experimental and Biological Psychology at the University of Potsdam's Faculty of Human Sciences, where he leads research in eye movement control, attention, and dynamical modeling of cognitive processes. He serves as a member of the Executive Committee of the Research Focus 'Cognitive Sciences' at the university, demonstrating his leadership role in interdisciplinary research. His work bridges psychology, neuroscience, and mathematical modeling to understand fundamental cognitive mechanisms through rigorous empirical and computational approaches. Engbert received his Dr. rer. nat. in 1998 from the Department of Physics at the University of Potsdam and completed his Dipl.-Physiker in 1994 at RWTH Aachen University. His physics background has profoundly shaped his research methodology, emphasizing quantitative and mathematical approaches to psychological phenomena. This interdisciplinary foundation enables him to develop sophisticated computational models that capture the dynamic nature of cognitive processes. Professor Engbert's research program centers on the dynamical systems approach to cognition, with particular emphasis on attention and eye movements. His work integrates biological and mathematical psychology to create computational models explaining visual information processing during reading and scene viewing. Key contributions include advancing understanding of microsaccades, peripheral vision in scene perception, and Bayesian approaches to modeling eye movement control. His research frequently involves cross-disciplinary collaborations with mathematicians, computer scientists, and linguists, reflecting the integrative nature of modern cognitive science. Analysis of his recent publications reveals a clear progression toward more sophisticated dynamical modeling of eye movements, with increasing incorporation of Bayesian statistical methods. His work spans multiple domains including reading research, scene perception, and infant cognition, with a notable trend toward integrating computational modeling with empirical eye-tracking data to develop comprehensive theories of cognitive processing. This evolution reflects both methodological advances and expanding theoretical scope in his research program. Project B03 of CRC 1294: Parameter inference and model comparison in dynamical cognitive models (with Prof. Sebastian Reich) Project B05 of CRC 1294: Attention selection and recognition in scene viewing (with Prof. Tobias Scheffer) Project B03 of CRC 1287: Modeling the interaction between eye-movement control and parsing processes (with Prof. Shravan Vasishth) Mobile eye tracking: Generalizability and limitations of the static scene viewing paradigm (Lead PI: Dr. Hans A. Trukenbrod) Professor Engbert maintains an active research group within the Department of Experimental and Biological Psychology, contributing to the Potsdam Eye-Movement Corpus and other shared research resources. His laboratory combines experimental eye-tracking facilities with computational modeling expertise, supporting a pipeline from data collection to theoretical development. The group's work has significant implications for understanding fundamental cognitive mechanisms and developing applications in human-computer interaction, educational technology, and clinical assessment.

• Dynamical Systems Approach to Cognition
• Attention and Eye Movements
• Biological and Mathematical Psychology
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Christine Liang is a Scientist at the Department of Monitoring and Exploration Technologies within the Helmholtz Centre for Environmental Research - UFZ in Leipzig, Germany. Her career spans roles as Founder and Lead Scientist of the Micro-Investigators citizen science program in New Zealand (2020-2022), Programme Manager and Lecturer at the Southern Institute of Technology (2018-2022), and Research Assistant at the University of Auckland (2016-2018). With a PhD in Environmental Sciences (2018) and a Master of Environmental Science (2011) from the University of Toronto , her work bridges environmental monitoring, computational modeling, and citizen science. Education : PhD (Environmental Sciences, University of Auckland, 2018); MEnvSc (University of Toronto, 2011); BScH (Ecology and Environmental Science, University of Toronto, 2010) Research Interests : Christine focuses on urban climate resilience through citizen science frameworks , coastal geomorphology in the Maldives, and environmental risk modeling . Her work integrates geospatial analytics with stakeholder engagement to address complex environmental challenges. Key Publications (2023-2024) explore: (1) civic engagement in climate services , (2) sediment dynamics under sea level rise , and (3) prototype development for participatory climate monitoring . Her research spans Frontiers in Environmental Science , Nature Communications , and Geomorphology journals. Projects : Critical contributions to CityCLIM (urban climate resilience, 2022-present) and ICEBERG (UFZ), alongside leadership in the nationwide Micro-Investigators program (2020-2022) examining microplastics in waterways.

• Environmental Informatics
• Citizen Science
• Climate Resilience
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Mathieu Morlighem is a Professor in the Department of Earth Sciences at Dartmouth College, holding the Evans Family Distinguished Professorship. His research focuses on ice sheet physics, climate system interactions, and sea level rise projections. Expertise in polar climate change Specialization in ice sheet numerical modeling Leadership in data assimilation techniques Development of Physics-Informed Neural Networks for ice dynamics His work combines high-resolution numerical models with remote sensing data to understand glacier behavior in Greenland and Antarctica. Recent publications emphasize subglacial topography estimation, calving parameterizations, and ice-hydrology coupling mechanisms. Current projects involve the Ice-sheet and Sea-level System Model (ISSM) and collaborations with institutions across Europe and the US. He teaches courses in Earth system modeling and mathematical geosciences.

• Polar Climate Change
• Ice Sheet Dynamics
• Sea Level Rise
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Hélène Seroussi is an Associate Professor of Engineering at Thayer School of Engineering, Dartmouth College, and serves as Program Area Lead for Mechanical, Operations & Systems Engineering. She is affiliated with the Department of Earth Sciences and leads the Ice and Sea Lab, focusing on ice sheet dynamics and climate impacts. Education : PhD (Mechanical Engineering, Ecole Centrale Paris, 2011); MS (Mechanical Engineering, Ecole Centrale Paris, 2008); MS (Structural Dynamics and Coupled Systems, Ecole Centrale Paris, 2008) Her research spans glaciology, ice sheet modeling, climate science, and data assimilation techniques. She develops numerical models to study ice-ocean interactions, Antarctic and Greenland ice sheet evolution, and sea level rise projections under climate change. Her recent articles emphasize improving ice sheet models through observational data integration, quantifying uncertainty in projections, and understanding marine ice sheet instability. Key collaborations include ISMIP6 Antarctica and studies on Jakobshavn Isbræ and Thwaites Glacier. Scientific Awards : Rising Star of Science Award (Research.com, 2022); AGU Cryosphere Early Career Award (2020); NASA Early Career Public Achievement Medal (2019) Professor Seroussi leads international initiatives like the Ice-sheet and Sea-level System Model (ISSM) Workshop and participates in NASA’s Earth Surface and Interior Program. Her work bridges engineering, geosciences, and climate policy.

• Glaciology
• Ice sheet modeling
• Ice sheet contribution to sea level
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Dr. Jungang 'Gordon' Chen is a Postdoctoral Fellow at the Bureau of Economic Geology within The University of Texas at Austin. His primary research interests span energy systems, geological carbon sequestration, reservoir characterization, and environmental geology. Email: jungang.chen@beg.utexas.edu Research focuses: CO2 storage optimization, unconventional resource recovery, induced seismicity, and coastal environmental dynamics Technical expertise: Deep learning applications for subsurface modeling, enhanced oil recovery, and seismic hazard assessment Recent publications demonstrate strong contributions to: Carbon capture utilization and storage (CCUS) through innovative injection techniques Understanding fluid-rock interactions in mobile shale systems Coastal geomorphology and groundwater resource management Geophysical modeling of sediment transport and reservoir quality

• Energy Research
• Geological Research
• Environmental Geology
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Gabriel Pasquet is a Postdoctoral Fellow at the Bureau of Economic Geology, University of Texas at Austin. His work focuses on carbon sequestration, enhanced oil recovery, and seismicity, with a strong emphasis on Permian Basin studies and the energy-water nexus. Current affiliation: Bureau of Economic Geology, University of Texas at Austin Position: Research Fellow Pasquet's research spans multiple disciplines, including: Carbon storage security and CO2 injection methodologies Seismic risk assessment from fluid injection activities Hydrological modeling integrating satellite data and machine learning Basin tectonics and stress field characterization Permafrost dynamics in Arctic environments Coastal system modifications and sediment transport Recent publications highlight his expertise in: Geological CO2 sequestration optimization Seismotectonic analysis of Midland Basin Formate-based oil recovery techniques Environmental impacts of energy development His work frequently intersects with: Energy economics Geophysical monitoring Reservoir characterization Machine learning applications in hydrology

• Carbon Sequestration
• Enhanced Oil Recovery
• Seismicity
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Dr. Yongjing Mao serves as an Associate Lecturer at the Water Research Laboratory within the School of Civil and Environmental Engineering at the University of New South Wales (UNSW). His academic journey includes a PhD in Remote Sensing and Coastal Geomorphology from The University of Queensland (2022), an MSc in Coastal and Marine Engineering and Management from Delft University of Technology (2018), and a BSc in Harbour, Coastal and Offshore Engineering from Hohai University (2016). Mao specializes in integrating coastal morphology with advanced remote sensing and machine learning techniques to address critical shoreline monitoring challenges. His research focuses on developing SAR-Optical satellite image fusion methods for cloud-contaminated imagery, implementing data assimilation for shoreline modeling, and creating deep learning spatio-temporal models (such as ConvLSTM and PredRNN) for predicting future shoreline changes at regional to global scales. His work significantly contributes to understanding coastal responses to sea-level rise and improving satellite-derived shoreline mapping accuracy. Mao's publication portfolio demonstrates consistent high-impact contributions to coastal remote sensing, with recent work benchmarking shoreline prediction models and developing innovative fusion techniques. His research bridges theoretical advancements with practical applications for coastal management, particularly in vulnerable regions like the Great Barrier Reef catchments. UQ Dean's Award for Outstanding HDR Theses (2022) Erasmus Mundus Partner Country Scholarship (€49,000) (2016) Mao actively contributes to coastal research through the Water Research Laboratory at UNSW, where he applies cutting-edge computational methods to real-world coastal challenges. His teaching responsibilities include CVEN9620 Rivers, Estuaries and Wetlands and ENGG2500 Fluid Mechanics for Engineers, where he integrates his research expertise into curriculum development. Current projects focus on spatio-temporal deep learning applications for shoreline change prediction and satellite-derived environmental monitoring systems.

• Remote Sensing
• Coastal Geomorphology
• Shoreline Monitoring
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Christopher S. Edwards is an Associate Professor of Planetary Science in the Department of Physics and Astronomy at Northern Arizona University. He serves as a science team member and instrument scientist for the Emirates Mars Infrared Spectrometer on the Emirates Mars Mission "Hope", a participating scientist on the Mars Science Laboratory Curiosity Rover, a participating scientist on the OSIRIS-Rex mission to Bennu, and a Co-Investigator on the Lunar Trailblazer mission. His research spans multiple planetary missions including the 2001 Mars Odyssey Thermal Emission Imaging System, Mars Global Surveyor Thermal Emission Spectrometer, and the Mars Reconnaissance Orbiter Compact Reconnaissance Imaging Spectrometer for Mars. Dr. Edwards' research program focuses on the composition, physical properties and processes, and morphology of planetary surfaces, with an emphasis on rocky bodies including Earth. His work spans: Infrared remote sensing instrument design and development Planetary surface composition analysis using spectroscopy Geologic field observations at terrestrial analog sites Numerical modeling of surface processes His research interests include: Planetary Geology and surface processes Spectroscopy of planetary surfaces Remote sensing instrument development Volcanic processes on terrestrial planets Dr. Edwards' recent publications reveal a strong focus on Mars atmospheric studies using data from the Emirates Mars Mission, particularly examining water ice clouds, surface dust properties, and thermal characteristics. His work also extends to lunar exploration with the Lunar Trailblazer mission and asteroid studies through the OSIRIS-Rex mission, demonstrating a comprehensive approach to understanding planetary surfaces across the solar system. His scientific contributions include: Studies of Martian paleo-hydrology and surface evolution Analysis of volcanic processes on Mars and Earth Development of novel methods for thermal inertia measurements Investigations of planetary surface composition using infrared spectroscopy Dr. Edwards has conducted field research at multiple terrestrial analog sites including the western U.S., Yellowstone, Hawaii, Spain, and the Himalayan mountains of Bhutan, connecting Earth-based observations to planetary science questions through the Planetary Instrumentation eXperimentation and Exploration Laboratory (PIXEL) at NAU.

• Planetary Geology
• Spectroscopy
• Remote Sensing
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Xavier Escandell is a Professor in the Sociology Department at Grinnell College, where he also directs the DASIL (Data Analysis and Social Inquiry Lab). His work focuses on immigration, transnationalism, and race/ethnic relations in Europe and Latin America. Prior to joining Grinnell, he earned his PhD from the University of Illinois at Urbana-Champaign (2005) and was a fellow at the University of California, San Diego’s Center for Comparative Immigration Studies. Education: PhD in Sociology, University of Illinois at Urbana-Champaign (2005) Fellowship: Center for Comparative Immigration Studies, UC San Diego His research examines socio-cultural integration of Bolivian migrants in Spain, transnational parenting practices, and the rise of xenophobia in Western Europe. Current projects include studying the Xuetes of Mallorca, a historically marginalized Jewish community recently recognized in the 21st century. His ethnographic work bridges macro-institutional contexts with micro-level cultural processes. Articles highlight themes such as immigrant deservingness in Europe, return migration dynamics, and public attitudes toward citizenship rights. His scholarship critiques how demographic shifts and institutional frameworks shape perceptions of migrants and fuel anti-immigrant sentiment. Escandell also analyzes transnational families’ strategies for maintaining cultural ties while navigating host-country environments. As DASIL director, he supports students in quantitative and qualitative social research methods. Teaching focuses on global ethnography and transnational migration studies.

• Immigration Studies
• Transnationalism
• Race/Ethnic Relations
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Anna Andersen is a Kildin Sámi historian and Postdoctoral Fellow at UiT The Arctic University of Norway. She works within the research project Urban Transformation in a Warming Arctic and focuses on forced resettlements of Indigenous peoples in the Arctic, particularly the Sámi of the Kola Peninsula and Greenlandic Inuit. Her research emphasizes Indigenous oral histories, colonial education policies, and community-driven approaches to historical trauma. Education: PhD in History (UiT), MA in Indigenous Studies (UiT) Research: Forced resettlements, residential schooling, Indigenous language revitalization, Arctic urbanization Collaborations: Ilisimatusarfik (Greenland), Sámi teacher education program (V1POM series courses) Her work spans historical analysis, policy critique, and educational development, with a focus on decolonizing methodologies and fostering Indigenous-led research collaborations across the Arctic.

• Sámi History
• Forced Resettlement Policies
• Indigenous Oral Histories
• +3 more

Dr. Hamed Moftakhari is an Associate Professor in the Department of Civil, Construction and Environmental Engineering at The University of Alabama, where he contributes to the College of Engineering and conducts interdisciplinary research through the Center for Complex Hydrosystems Research and the Center for Sustainable Infrastructure. His research focuses on coastal hydrology, particularly the dynamics of estuarine systems, compound flooding, nuisance flooding, and the impacts of sea level rise on low-lying coastal communities. He investigates how interactions between inland hydrologic and coastal oceanic processes affect water, food, and energy resources, and how communities respond to these challenges. His work integrates artificial intelligence, geospatial analysis, data analytics, and climate modeling to improve flood resilience and risk assessment. The recent publications highlight a strong trend in developing advanced computational models for coastal hazard estimation, with emphasis on Bayesian methods, deep learning, data assimilation, and remote sensing. These works span applications across the U.S. Gulf Coast and focus on both extreme events and chronic flooding, reflecting a dual focus on acute disasters and long-term climate adaptation. NSF CAREER Award (2023) Early-Career Research Fellowship, National Academies (2020–2022) Cronin Award – Early Achievement, Coastal and Estuarine Research Federation (2021) Early Career Research Excellence and Innovation Award, College of Engineering, UA (2023) Faculty Excellence in Graduate Education Award, CCHE Department (2023) Early-Career Faculty Fellowship, Alabama Water Institute (2022–2025) Maseeh Graduate Fellowship (2012) Dr. Moftakhari advises graduate students and leads the Coastal Hydrology Lab (CHL), where he mentors researchers in hydrodynamic modeling and coastal resilience. His research is supported by grants from the National Science Foundation (NSF) and the National Oceanic and Atmospheric Administration (NOAA), including the prestigious NSF CAREER award. He also serves as an Associate Editor for the Journal of Hydrologic Engineering (ASCE) and on the editorial board of Scientific Reports (Nature). The Coastal Hydrology Lab (CHL) at UA focuses on improving scientific understanding of freshwater-influenced coastal systems such as estuaries, wetlands, deltas, and harbors. The lab utilizes emerging technologies and diverse data sources to study sediment supply, salinity intrusion, ecosystem responses, morphologic evolution under sea level rise, and compounding hydroclimate extremes like hurricanes.

• Coastal Hydrology
• Hydrologic Modeling
• Compound Flooding
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Sebastian Otte is a Professor at the Institute for Robotics and Cognitive Systems at the University of Lübeck, where he leads the Adaptive AI research group. Prior to this, he was a postdoctoral researcher and substitute professor at the University of Tübingen, contributing significantly to the Cognitive Modeling and Distributed Intelligence groups. University of Lübeck, Professor (since 2023) University of Tübingen, Postdoc and Substitute Professor (2016–2023) Centrum Wiskunde & Informatica (CWI), Humboldt Fellow (2022–2023) His research focuses on recurrent and spiking neural networks, bio-inspired computing, efficient learning, and adaptive AI systems. He explores how neural models can perform online learning, handle multiple time scales, and solve complex cognitive tasks such as binding, prediction, and motor control. His work bridges machine learning with cognitive science and robotics. The recent publications show a strong trend toward physics-informed neural networks, finite volume methods for PDE modeling, and explainable AI via counterfactual reasoning. His work integrates deep learning with scientific computing, emphasizing robust, interpretable, and efficient models for real-world applications. Scientific awards include: Best Paper Award at ICANN 2019 Humboldt Research Fellowship Editor's Highlight in Water Resources Research He has supervised over 70 bachelor’s and master’s theses and actively mentors students in areas such as spiking neural networks, reservoir computing, and robotics. His teaching includes core computer science and advanced neural network courses. He has been involved in research projects with industry partners like Daimler AG and Mercedes-Benz AG. Otte leads the Adaptive AI research group, which focuses on developing next-generation AI systems that learn efficiently, adapt dynamically, and model complex cognitive and physical processes using biologically inspired architectures.

• Artificial Intelligence
• Machine Learning
• Recurrent Neural Networks
• +12 more

Begum Kilic Ararat is a Research Associate at the University of Bath, affiliated with the Management Made Smarter Innovation: Centre for People-Led Digitalisation, within the Faculty of Engineering & Design and the Department of Mechanical Engineering. Her work bridges engineering systems and organizational behavior in the context of digital transformation in UK manufacturing and logistics. Research Interests: Digitalization and Industry 4.0 Robotics and Automation in Manufacturing Smart Warehousing and Logistics Systems Technology Adoption and Implementation Human Factors and Organizational Readiness Decision-Making in Digital Transformation Her recent publications reveal a strong focus on understanding the drivers, challenges, and outcomes of digitalization in industrial settings, particularly emphasizing people-led approaches. Using mixed-methods including case studies, semistructured interviews, and analytical frameworks like AHP, she investigates how firms adopt automation technologies and measure digital success. Her work consistently highlights the interplay between technological capabilities, human ergonomics, and organizational value creation. Scientific Contributions: Co-authored multiple reports and peer-reviewed conference papers on robotics adoption in UK manufacturing. Developed insights into digitalization outcome metrics and socio-technical factors influencing automation decisions. Contributed to public understanding through blog posts on digital strategy and expert decision-making. Begum has been actively involved in two major EPSRC-funded projects: Made Smarter Innovation: People-Led Digitalisation and its earlier phase. These projects aim to accelerate digital transformation in UK industry by integrating human-centered design and value chain optimization. She completed her PhD in March 2025 on the assimilation of automation in warehouse operations, positioning her as an emerging expert in smart logistics. While no formal advisees are listed, her supervisory experience during her doctoral work suggests future mentoring potential. Laboratories and Research Teams: Centre for People-Led Digitalisation, University of Bath Made Smarter Innovation Network Collaborative research group with Prof. Lynne Newnes, Dr. Vasilis Giannikas, and others in advanced manufacturing systems

• Digitalization
• Technology Adoption
• Robotics and Automation
• +7 more