Lauren Neitzke Adamo is an Associate Professor in the Department of Earth and Planetary Sciences at Rutgers University, where she directs the Undergraduate Program (2022–present) and co-leads the Rutgers University Geology Museum (2016–present). Her career includes roles as Assistant Teaching Professor (2016–present), Lecturer (2008–2016), and prior leadership at the museum. Education : Ph.D. (2016), M.S. (2006), and B.S. (2004) from Rutgers University. Her research spans paleoceanography , stable isotope stratigraphy , and innovative geoscience education , focusing on drone-based remote sensing, 3D visualization, and museum outreach. She integrates technology like UAVs and 3D printers to enhance educational experiences and coastal process analysis. Recent publications highlight her work in virtual field trips (Newark Basin), drone-assisted coastal geomorphology, and science communication through comics and interactive museum exhibits. Collaborative projects emphasize democratizing geospatial tools and engaging diverse audiences via digital platforms.
Paul M. Karabinos is the Charles L. MacMillan Professor of Natural Sciences in the Department of Geosciences at Williams College, where he has been a faculty member since 1983. He holds an endowed professorship recognizing his significant contributions to geological sciences and education. His academic career at Williams progressed from Assistant Professor (1983-1990), to Associate Professor (1990-1995), to full Professor (1995-2012), and he served as Department Chair from 2006-2012 before receiving his current named professorship. B.S. from University of Connecticut (1975) Ph.D. from Johns Hopkins University (1981) Postdoctoral Fellow at Harvard University (1981-1983) Visiting Scholar at Doshisha University, Kyoto, Japan (1993) Professor Karabinos specializes in structural geology and Appalachian tectonics, with particular expertise in the Taconic orogeny, metamorphism, and geochronology. His research has significantly advanced understanding of gneiss dome formation, thrust faulting mechanisms, and the tectonic evolution of the New England Appalachians. He has developed innovative approaches to 3D geological visualization using SketchUp, creating interactive educational tools that transform how structural geology concepts are taught. His work combines field mapping, microstructural analysis, and geochronological techniques to unravel complex deformation histories. Analysis of Karabinos' recent publications reveals a strong focus on integrating traditional geological fieldwork with digital visualization technologies. His research spans fundamental tectonic questions about Appalachian evolution while simultaneously developing educational tools that make structural geology concepts more accessible. A significant thread throughout his work involves studying the Chester Dome, Berkshire Massif, and Mount Greylock as natural laboratories for understanding orogenic processes. His publications demonstrate consistent productivity with a notable shift toward developing interactive 3D models for both research and educational purposes in his later career. Professor Karabinos has mentored over 35 undergraduate research students at Williams College, many of whom completed senior theses under his guidance. His dedication to undergraduate education is evident in his development of field-based courses and innovative teaching tools. He has received support for his research through various grants that have enabled sustained field investigations in the Appalachian region and the development of his digital visualization projects. His research involves extensive field work in the Berkshire Massif, Chester Dome, and Mount Greylock areas, often utilizing student researchers. Karabinos has developed the SketchUp Structural Geology Studio, which provides interactive 3D models for teaching structural geology concepts. His work on the Day Mountain thrust sheet and graphite redistribution in pelitic schists represents ongoing field-based research projects that integrate undergraduate students in meaningful research experiences.
Prof. Monika Sester is a distinguished Professor and Executive Director of the Institute of Cartography and Geoinformatics at Leibniz University Hannover, within the Faculty of Civil Engineering and Geodetic Science. She also serves as Spokesperson for the Leibniz Research Center FZ:GEO and holds multiple leadership roles including Faculty Information Officer (FIO) for the Faculty of Civil Engineering and Geodetic Science, Ombudsman for Good Scientific Practice, and Exchange Coordinator for Geodetic Science and Geoinformatics. Her research focuses on the intersection of geospatial information science, cartography, and urban mobility. Prof. Sester's work spans several key areas: Geospatial data processing and analysis Cartographic representation and visualization Urban mobility and transportation systems Spatial data uncertainty and quality Digital mapping technologies and applications Historical map analysis and interpretation Prof. Sester's recent publications demonstrate a strong focus on applying advanced computational techniques to geospatial problems. Her work shows increasing emphasis on machine learning applications for map analysis, urban mobility optimization, and 3D spatial modeling. She has been particularly active in researching applications of deep learning for historical map interpretation, urban mobility patterns, and spatial uncertainty visualization. Her contributions to the field have been recognized through leadership positions in major research initiatives: Executive Director, Institute of Cartography and Geoinformatics Spokesperson, Leibniz Research Center FZ:GEO Faculty Information Officer, Faculty of Civil Engineering and Geodetic Science Ombudsman for Good Scientific Practice Member of multiple academic committees including the Admissions and Examination Board Prof. Sester actively collaborates with students and researchers across multiple projects focused on geospatial information systems, urban mobility, and cartographic visualization. Her leadership extends to guiding research directions within the Leibniz Research Center FZ:GEO, which brings together interdisciplinary expertise to address complex spatial challenges.
Prof. Dr. Stefan Brönnimann serves as a Professor and Unit Leader of Climatology at the Institute of Geography, University of Bern. His extensive research portfolio spans historical climatology, climate dynamics, and atmospheric circulation reconstruction, with particular focus on the past 400 years. He plays a leadership role in major international climate initiatives including the IPCC assessment reports and various climate reanalysis projects. Brönnimann's research centers on reconstructing historical weather and climate patterns through the innovative combination of early instrumental data, proxy records, and climate models. His work examines large-scale climate variability, interannual-to-decadal atmospheric circulation patterns, volcanic eruption effects on climate, and climate-society interactions. His methodologies often involve transforming historical documents into usable climate datasets and applying advanced machine learning techniques to weather reconstruction challenges. Recent publications reveal a strong emphasis on European climate patterns, hydroclimate extremes, and the development of novel datasets and tools for climate research across high-impact journals in climate science, paleoclimatology, and climate informatics. Notable scientific achievements include: Lead author for Chapter 2 of the IPCC Working Group I 5th Assessment Report President of the Commission 'Atmospheric Chemistry and Physics' (ACP) of sc.nat Editorial leadership for multiple prestigious journals including Meteorologische Zeitschrift, Climate of the Past, and Geographica Bernensia Leadership roles at the Oeschger Centre for Climate Change Research Active participation in international initiatives like the Twentieth Century Reanalysis Project and Atmospheric Circulation Reconstructions over the Earth (ACRE) Brönnimann has secured substantial funding through numerous national and international projects including SNF, NCCR Climate, EU FP-7, HORIZON2020, COST, and ERAnet.RUS. He has organized multiple international workshops on weather and climate extremes, atmospheric circulation variability, and historical climate events like the Tambora eruption. His work connects closely with the Oeschger Centre for Climate Change Research at the University of Bern, where he leads Work Package 2 and serves on the Steering Group, demonstrating his significant institutional leadership within Bern's climate research community.
Marc De Benedetti is an Assistant Professor, Teaching Stream in the Department of Computer Science at the University of Toronto's Mississauga campus within the Mathematical and Computational Sciences school. His work bridges computational methods with healthcare analytics, geophysical modeling, and educational innovation. He specializes in applying machine learning to medical datasets (e.g., SEER cancer data) for predictive modeling and has contributed to CAR T-cell therapy efficacy studies in oncology. His geoscience research focuses on spatial-temporal variability in climate systems, particularly in the Congo Basin and UK wind dynamics. De Benedetti also designs supplementary educational programs to support undergraduate STEM learners. His research emphasizes data-driven approaches to address challenges in healthcare, environmental science, and pedagogy. Research interests include: Machine learning applications in healthcare survival prediction Immunotherapy comparative efficacy analysis Spatial variability in climate and hydrology systems Wind energy potential modeling Innovative undergraduate physics/mathematics tutoring strategies His recent articles analyze treatment outcomes for blood cancers using CAR T-cell therapies, explore model resolution impacts on geophysical data accuracy, and develop predictive tools for cancer prognosis via SEER datasets. No scientific awards are explicitly mentioned in the provided materials. While no formal grants or student advisement records are listed, his work demonstrates strong engagement with both academic research and educational development initiatives. His research locations include the Congo Basin for hydroclimatic studies and the UK for wind energy modeling. Collaborations likely span medical institutions for oncology studies and climatological agencies for environmental data analysis.
Dr. Song Shu is an Assistant Professor in the Department of Geography and Planning at Appalachian State University, specializing in remote sensing applications for cryospheric and hydrospheric studies. She holds a Ph.D. from the University of Cincinnati (2013–2019), an M.S. and B.S. from East China Normal University (2010–2013 and 2006–2010). Her research focuses on satellite altimetry, lake hydrology, and climate change impacts using advanced remote sensing techniques. She teaches courses such as GHY 3812: Geographic Information Systems, GHY 3310: Environmental Remote Sensing, and GHY 4810/5810: Digital Image Processing. Her work emphasizes improving satellite altimetry for Earth surface dynamics, retrieving hydrosphere/cryosphere parameters (e.g., snow depth, lake levels), and analyzing climate change effects on lakes. Key contributions include studies on Arctic snow accumulation, Tibetan Plateau vegetation dynamics, and urban heat island mitigation. Dr. Shu collaborates on interdisciplinary projects, including ICESat-2 ice shelf analysis and water quality modeling in inland lakes. Publications span high-impact journals like Remote Sensing of Environment (IF 9.09), IEEE Transactions on Geoscience and Remote Sensing (IF 5.86), and Nature Communications Earth & Environment. Her research integrates multi-source remote sensing data and geospatial methods to address global environmental challenges.
Masoud Yari is a Teaching Professor in the Department of Computer Science and Engineering at Lehigh University, part of the P.C. Rossin College of Engineering and Applied Science. He joined Lehigh in 2022 and contributes to the university's new M.S. program in Data Science. Previously, he worked as a research professor at the Bina Lab and held roles as a professional associate professor at Texas A&M University-Corpus Christi. He earned his Ph.D. in Applied Mathematics from Indiana University in 2008. Research Focus: Yari specializes in Machine Learning, Data Science, Dynamical Systems, Biomathematics, and Remote Sensing. His work bridges computational methods with environmental and geophysical applications, particularly in analyzing ice layers, snow dynamics, and seismic systems. He employs advanced techniques like deep learning, physics-informed neural networks, and multi-scale architectures to address challenges in glaciology, disaster management, and structural engineering. Key Contributions: His research emphasizes environmental monitoring through radar data analysis, including tracing snow and firn layers in polar regions. He has developed algorithms for seismic response evaluation and flood scene understanding using high-resolution aerial imagery. His work integrates machine learning with domain-specific physics to enhance predictive accuracy and data interpretation. Labs and Collaborations: His affiliation with the Bina Lab highlights collaborative efforts in computational science. His articles reflect interdisciplinary engagement across computer science, geophysics, and environmental science, addressing both theoretical and applied challenges in data-driven environmental analysis.
Kuuipo Walsh is the GIScience Program Director and Senior Lecturer I at Oregon State University's College of Earth, Ocean, and Atmospheric Sciences (CEOAS). She oversees the GIScience certificate program, advising over 200 students annually on course selection, career paths, and academic plans. Her research focuses on GIS, metadata standards, digital libraries, and coastal atlases. She teaches advanced undergraduate and graduate courses in GIScience via Ecampus, including GIScience I-III and Geospatial Perspectives on Intelligence. Education: B.S. in Computer Science (California Polytechnic State University, 1993) and M.S. in Marine Resource Management (Oregon State University, 2002). Her publications emphasize spatial data infrastructure, coastal data networks, and usability in geospatial tools, with notable contributions to the Oregon Spatial Data Library and Virtual Oregon projects. She has no listed scientific awards but maintains active engagement in geospatial education and professional advising. Lab/Team Affiliation: Directs the GIScience certificate program and collaborates on geospatial initiatives within CEOAS.
Ryan T. White is an Associate Professor at Florida Institute of Technology in the Department of Mathematics and Systems Engineering within the College of Engineering and Science. He serves as Director of the NEural TransmissionS (NETS) Lab, focusing on deep learning, computer vision, and data science. He is also an Affiliate Faculty member in Electrical Engineering and Computer Science. Ph.D. in Applied Mathematics (2015) from Florida Tech His research bridges deep learning and computer vision with applications in autonomous satellite operations , physics-informed neural networks for biomedical and geoscience problems, and NLP in aerospace domains. Projects include real-time edge computing , stochastic process analysis , and generative AI for synthetic data. The NETS Lab he directs has produced 15+ recent publications in conferences like IEEE Aerospace, AIAA SCITECH, and AAS/AIAA, with funding from the U.S. Space Force, Air Force Research Lab, and NSF. His teaching spans graduate/undergraduate courses in deep learning , machine learning , probability , and honors calculus . Current advisees include Ph.D. candidates and M.S. students working on topics like 3D object detection , information-theoretic neural analysis , and geophysical signal processing . The lab’s scientific contributions include real-time satellite feature detection , physics-guided neural networks for blood flow modeling, and entropy-based visual explanations for AI interpretability. Collaborations span Georgia Tech , Mulitscale Cardiovascular Fluids Laboratory , and Engage-AI for global development projects analyzing UNDP Sustainable Development Goals.
Dr Kelvin Ng is a Postdoctoral Research Fellow at the University of Birmingham's School of Geography, Earth and Environmental Sciences. His research focuses on meteorological and climatological extremes, particularly tropical cyclones and their impacts. He holds an MSci in Physics from Imperial College London and a PhD in Atmospheric Sciences from the University of Hong Kong. Key roles include contributions to projects such as INPAIS (NERC-funded collaborative research with Swiss Re and Beijing Normal University), PRE-CAX (Newton Fund-supported projects with the University of Reading), Ex-Storms (NERC-funded European windstorm predictability study), and HURACAN (UK-US consortium exploring cyclone risks). He is also involved in initiatives like the Met Office Summer Testbed 2023 and SR-Hazards (Swiss Re-funded). Ng's research interests span tropical cyclone intensity dynamics, extreme rainfall prediction, and climate model assessment (e.g., CMIP6). He is a member of the Royal Meteorological Society (RMetS) and European Geosciences Union (EGU). Recent publications highlight advancements in Mei-yu front analysis, storm surge impact modeling, and causal-guided statistical approaches for extreme weather prediction. His work bridges academic research with industry collaboration, aiming to improve parametric insurance thresholds for typhoons and enhance climate change adaptation strategies for regions like China and Europe.
Brad G. Peter is an Assistant Professor in the Department of Geosciences at the University of Arkansas, specializing in environmental remote sensing and sustainable agricultural systems . As director of the Environmental GIS & Cartography Lab , his work integrates satellite/sUAS data, Google Earth Engine analytics, and geovisualization to address global challenges in agricultural land suitability, climate-smart crops, and hydrological dynamics. Ph.D. in Geography, Michigan State University B.A. in Geography, University of Texas at Austin Former postdoctoral researcher at University of Alabama FAA Certified Drone Pilot Research Foci: • Global agricultural land suitability mapping using cloud-based geospatial tools • Climate-smart crop scaling in Sub-Saharan Africa and Southeast Asia • Flood mapping and hydrological analysis via sUAS and satellite systems • Ecological niche modeling for sustainable farming systems • Multi-scale precision agriculture for smallholder farms Scientific Awards & Recognitions: Robert C. and Sandra Connor Faculty Fellowship Wally Cordes Teaching Rapport Award ASPRS Robert N. Colwell Memorial Fellow Owen Gregg Climate Change Research Award USGIF Doctoral Scholarship MSU Climate-Food-Energy-Water Research Fellow Research Collaborations & Tools: • Co-PI on WaterServ hydrological cyberinfrastructure • Collaborator in NASA LCLUC-funded Southeast Asia land transition studies • Expert in Google Earth Engine analytics and sUAS data collection • Member of Gamma Theta Upsilon, AAG, ASPRS, and NACIS
Dr. Ioanna Lykourentzou is an Associate Professor in the Software Technology for Learning and Teaching department at Utrecht University's Faculty of Science. She leads the Collaborative Technologies Lab and coordinates the Computing Science Master's and Information Sciences Honors Bachelor's programs. Additionally, she serves as a Fair Data and Software fellow within the Open Science Team of the Faculty of Science and as a member of the Ethics Review Board for the Faculties of Science and Geosciences. Her research focuses on collaborative and crowd systems, developing methods that help people work together, coordinate efforts, and innovate at scale, both online and in physical spaces. Her interdisciplinary approach combines computational science (machine learning, agent-based modeling, mathematical optimization) with social sciences (personality testing, team building). Her expertise spans Human-Computer Interaction, Algorithms, Agent-Based Modelling, Telecollaboration, Creativity, and Innovation. Her recent publications (2021-2025) demonstrate a strong focus on human-AI interaction, generative models, and applications in cultural heritage and education. She examines how technology can facilitate collaboration, with particular attention to team formation, personality factors, and digital nudging techniques. Her work bridges theoretical research with practical applications in digital humanities, cultural heritage, and computing education. Dr. Lykourentzou has received significant recognition for her research, with multiple publications garnering substantial citations and reader attention across platforms like Mendeley and social media. Her work on personality-based team formation (2016) has been particularly influential with over 90 citations. Prior to joining Utrecht University, she worked as a Senior Researcher at the Luxembourg Institute of Science and Technology (LIST), where she coordinated the European H2020 project CROSSCULT. She has also collaborated with the Human-Computer Interaction Institute of Carnegie Mellon University as a visiting researcher and with INRIA Nancy-Grand Est and the Public Research Center Henri Tudor as a postdoctoral fellow.
Dr. Mark Dekker is a Researcher at Utrecht University's Faculty of Geosciences, specifically within the Copernicus Institute of Sustainable Development and the Environmental Sciences department. He also holds a position at the Netherlands Environmental Assessment Agency (PBL). His work bridges data science, mathematical modeling, and environmental policy, with a particular focus on climate change mitigation strategies. Dr. Dekker completed his PhD in 2022 with a thesis titled 'Macroscopic Dynamics in Complex Systems,' which examined interactions between microscopic and macroscopic phenomena across various complex systems including climate tipping points, railway disruptions, ecological patterns, neuroscience data, and pandemic interventions. His educational background has equipped him with strong analytical skills in mathematical and computational modeling. His research interests span Dynamics of Complex Systems , Climate Dynamics , Critical Transitions , and Integrated Assessment Modelling . Dr. Dekker applies data science and mathematical techniques to climate change questions, particularly focusing on climate justice and mitigation effort-sharing. He distinguishes normative considerations from climatological uncertainties to understand what determines fair national emission reduction targets. His work includes developing the Carbon Budget Explorer webtool for visualizing fair climate targets. Dr. Dekker's publication record shows a clear evolution from fundamental complex systems research during his PhD (2017-2021) toward applied climate policy analysis. His recent work (2022-2025) focuses on energy system modeling, climate scenario analysis, and effort-sharing frameworks. He has made significant contributions to understanding variance in climate policy scenarios and identifying 'model fingerprints' in mitigation pathways. His interdisciplinary approach connects climate science with social considerations of fairness and justice. As an educator, Dr. Dekker contributes to several bachelor-level courses including Introduction to Adaptive Systems, Graphics, Image processing, Introduction to Complex Systems, and Introduction Project. His teaching reflects his expertise in complex systems and computational approaches. Dr. Dekker is actively involved in the IMAGE team within the Horizon-2020 ECEMF project, working on scenarios describing future energy economy evolution and identifying potential routes to climate neutrality. His research combines data analysis, network analysis, computational modeling, and mathematical modeling using Python and Matlab. His work has significant policy relevance, particularly for national and international climate target setting.
Merethe Frøyland is a Professor at the Science Center (Naturfagsenteret) within the Faculty of Mathematics and Natural Sciences (MN-fakultetet) at the University of Oslo (UiO). With a background in geology and a PhD in science education, she specializes in science didactics , geodidactics , and informal learning environments . Her work focuses on integrating other learning arenas (e.g., fieldwork, museums) into science teaching and improving teacher education through continuing professional development programs.
Mark Last is a Professor at Ben-Gurion University in Beersheba, Israel, with a distinguished career spanning over three decades in computer science research. His work primarily focuses on data mining, machine learning, and natural language processing applications. His research interests encompass stream data mining, text summarization, fuzzy logic systems, and classification algorithms. Last has made significant contributions to developing techniques for analyzing dynamic data streams, multilingual text processing, and applying machine learning to real-world problems in healthcare, social media analysis, and security informatics. His work often bridges theoretical advancements with practical applications, particularly in handling non-stationary data and developing interpretable models. Recent research trends show a continued focus on stream data analysis, with applications expanding into social media monitoring, healthcare prediction systems, and multilingual content analysis. His work demonstrates consistent innovation in adapting machine learning techniques to evolving data environments and practical challenges. Mark Last has maintained a prolific publication record with over 175 publications documented in DBLP, collaborating extensively with researchers including Abraham Kandel, Marina Litvak, and Oded Maimon. His work has been published in top venues including IEEE Access, Machine Learning journal, and Expert Systems with Applications.
