Jun Hyung Lee is a Visiting Assistant Professor in the Department of Environmental Biology at SUNY College of Environmental Science and Forestry (ESF). His research focuses on advancing forest tree improvement and conservation through molecular and synthetic biology approaches, with a particular emphasis on enhancing plant resilience to environmental stresses via beneficial microbial interactions. He teaches courses in plant biotechnology and tissue culture methods. Education includes a Ph.D. in Forest Genetics from Purdue University (USA), and M.S. and B.S. degrees in Plant Science from Seoul National University (South Korea). His work integrates cutting-edge genetic engineering techniques with ecological studies to address challenges in plant stress tolerance, symbiosis, and epigenetic regulation. Recent projects include identifying novel symbiosis pathways for thermotolerance and analyzing flooding tolerance in hybrid poplars. Publications highlight contributions to plant-microbe interaction research, synthetic biology applications, and genome editing epigenetic impacts. Collaborations span institutions like Oak Ridge National Laboratory and the University of Georgia, reflecting his transdisciplinary approach to plant science. Lee’s teaching emphasizes practical skills in biotechnology, bridging laboratory innovation with field applications.
Yin Bao is an Assistant Professor in Plant and Soil Sciences and Mechanical Engineering at the University of Delaware since 2023, previously holding the same position at Auburn University's Department of Biosystems Engineering (2019-2023). He holds a BE in Mechanical Engineering from China Agricultural University (2012) and a PhD in Agricultural and Biosystems Engineering from Iowa State University (2018), followed by postdoctoral research there until 2019. His research focuses on automation technology for agriculture and forestry, leveraging robotics, machine learning, and sensing systems to develop tools for precision farming and plant phenotyping. Key areas include unmanned systems (UGVs/UAVs), spectral imaging, and AI-driven predictive models for crop and livestock management. Recent work emphasizes automated inventory systems for forest nurseries, UAV-based vegetation assessment, and machine learning applications in crop yield prediction. His publications span robotic guidance systems, root segmentation in X-ray CT scans, and equine gait analysis using deep learning. Notable projects include the Robotic Assay for Drought (RoAD) system and the 'smart canopy' sorghum initiative. Collaborative efforts involve integrating multifrequency microwave sensing and electronic nose technologies for crop quality analysis.
Prof. Richard L. Peters is a Professor and Head of the Chair of Tree Growth and Wood Physiology at the Technische Universität München (TUM) since 2024. His research focuses on tree physiology, wood formation, and climate-forest interactions. He holds a doctorate summa cum laude from the University of Basel (2018) and has conducted postdoctoral research at the Swiss Federal Institute for Forest Science (WSL) and Ghent University. His work integrates forest ecology, dendrochronology, and ecophysiology to address climate change impacts on forests. Education: B.Sc./M.Sc. in Biology at Utrecht University, Ph.D. from University of Basel (2018). Key career steps include a Swiss National Science Foundation (SNSF) fellowship at Ghent University and coordination of the Swiss Canopy Crane II project (2021). Research Interests: Tree water-use strategies, drought tolerance mechanisms, carbon allocation dynamics, and the physiological basis of tree growth. He leads interdisciplinary projects to monitor forest responses to environmental stress using tools like the TreeNet network. Awards: Early Postdoc Mobility Fellowship (SNSF, 2019), Doctorate summa cum laude (2018). Contributions: Authored >180 publications, co-developed the datacleanr R package for ecological data processing, and pioneered methods linking tree-ring data with climate models. His work emphasizes the need for better observational data to improve vegetation models.
Elahe Soltanaghai is an Assistant Professor in the Department of Computer Science and a Faculty Affiliate in Electrical and Computer Engineering at the University of Illinois Urbana-Champaign. She is also a 2022 NCSA Fellow and received her PhD in Computer Science from the University of Virginia (2019), MS in Computer Engineering from Sharif University of Technology (2014), and dual BS degrees in Computer and Information Technology Engineering from Amirkabir University of Technology (2011, 2013). PhD: University of Virginia, Computer Science, 2019 MS: Sharif University of Technology, Computer Engineering, 2014 BS (Computer Engineering): Amirkabir University of Technology, 2011 BS (Information Technology Engineering): Amirkabir University of Technology, 2013 Her research spans wireless sensing and communication, focusing on Millimeter-wave Radar Sensing (for automotive, mixed reality, structural monitoring), Machine Learning for Wireless Systems (adaptive sensing/communication), Forest IoT (through-canopy biomass and soil sensing), Metaverse Technologies (gaze-based VR/AR), and Low-Power Backscatter Communication (WiFi/power-line tags). She directs the Wireless, Sensing & Embedded Networked Systems (iSENS) Lab and co-directs the Illinois Center for IoT. Her work bridges wireless networking with cyber-physical sensing , emphasizing environmental monitoring (e.g., wildfire fuel detection via radar tags) and human-computer interaction (e.g., gaze-tracking in VR). Recent articles include innovations in passive radar profiling , through-canopy biomass characterization , and integrated communication-sensing protocols . Scientific Awards: Google Research Scholar Award (2022) N2Women Rising Star (2021) ACM SIGMOBILE Dissertation Award (2020) EECS Rising Stars (2019) NCSA Faculty Fellowship (2023) Best Demo Runner-up, IPSN (2023) Teaching Excellence Award (2023) Grants: NASA FireTech Program Grant (2025) NSF Grant for Radar-based Perception (2024) Insper-Illinois Grant for VR Research (2024) Keysight Research Gifts (2022, 2023) T-Mobile Research Gift (2022)
Reed Maxwell is the William and Edna Macaleer Professor of Engineering and Applied Science in the Department of Civil and Environmental Engineering and the High Meadows Environmental Institute at Princeton University. He serves as Director of the Integrated Groundwater Modeling Center (IGWMC) and leads a research group comprising graduate students, postdoctoral researchers, and staff. His academic appointments include concurrent roles in both the School of Engineering and Applied Science and the High Meadows Environmental Institute. Maxwell's research focuses on understanding connections within the hydrologic cycle and how they relate to water quantity and quality under anthropogenic stresses. His work centers on hard problems in hydrology including groundwater, evapotranspiration and snow. His research group uses integrated hydrologic modeling, field observations, and remote sensing products to study terrestrial freshwater systems. Key research areas include surface water and the terrestrial hydrologic cycle; interactions of the land-surface, surface water and groundwater; and human health risk assessment. Maxwell has authored more than 185 peer-reviewed journal articles with an H-Index of 66 and over 19,000 citations. His recent work emphasizes machine learning applications in hydrology, continental-scale modeling, and physically rigorous scenario generation through projects like HydroFrame and HydroGEN. He teaches courses including CEE 306/ENV 318 Hydrology: Water and Climate and CEE 586/ENV 586 Physical Hydrology. 2020 Distinguished Henry Darcy Lecturer American Geophysical Union Fellow (2019) 2018 Boussinesq Lecturer Belle van Zuylen Chair (visiting), University of Utrecht 2017 School of Mines Research Award recipient Maxwell has mentored 17 PhD students and 20 MS thesis students throughout his career. His current research group includes multiple postdocs, research software engineers, and graduate students working on projects spanning continental-scale hydrologic modeling, groundwater-stream interactions, and machine learning applications in hydrology. The IGWMC maintains an active education and outreach program including STEM fairs, school visits, and digital educational tools like the HydroFrame Education Team's virtual sandtank aquifer model.
Dr. Anthony Filippi is an Associate Professor and Director of Graduate Programs at Texas A&M University. His research focuses on remote sensing, geographic information systems (GIS), and machine learning applied to aquatic and terrestrial environments. He leads the Fluvial-GEOS Lab, studying riverine/floodplain systems using remote sensing and GIS technologies. Educational Background: Ph.D. in Geography, University of South Carolina (2003) M.S. in Geography, University of South Carolina B.A. in Geography, Kansas State University Research Interests: Imaging spectroscopy, hyperspectral remote sensing of rivers and coastal oceans, GIS-based modeling, data fusion, aquatic optics, and machine learning. His work addresses coastal ocean bathymetry estimation, floodplain dynamics, and applications in environmental monitoring, including hazardous waste site tracking and agricultural studies. Recent Research Trends: Recent publications highlight advancements in UAS-based image analysis, LSTM networks for floodplain classification, and environmental policy impacts on forest resources. His work integrates machine learning with remote sensing to improve ecological and geomorphological understanding. Labs/Teams: Director of the Fluvial-GEOS Lab, focusing on remote sensing and GIS applications in riverine environments.
Hugo de Boer is a Professor at the Copernicus Institute of Sustainable Development , Faculty of Geosciences, Utrecht University. He serves as scientific lead for the Delta Climate Center in Vlissingen and coordinates MSc programs in Water Science and Management and Water Management for Climate Adaptation . His research explores climate-ecosystem interactions, focusing on plant ecophysiology, ecosystem dynamics, and nature-inclusive climate adaptation in deltas. Research Themes: Future Deltas, Pathways to Sustainability, Integrative Bioinformatics Projects: LEMONTREE, CloudRoots, 'From losers to winners' (ancient plant lineages under elevated CO2) Teaching Expertise: System thinking for sustainability, quantitative statistics, plant ecophysiology Research Trends emphasize interdisciplinary approaches to climate change impacts on ecosystems, with publications spanning plant-cloud processes, CO2 acclimation, and eco-evolutionary optimality models. His work bridges biogeochemistry, land-atmosphere interactions, and sustainable development frameworks. Projects & Collaborations include experimental studies on ancient plant lineages (Equisetum) and integrative field campaigns in Amazon and temperate forests. He contributes to modeling climate-vegetation feedbacks, pesticide emission scenarios, and social-ecological system transitions.
Dr. Jianguo Wang is a Professor in the Department of Earth and Space Science Engineering at York University's Lassonde School of Engineering. He has been a faculty member since 2006 and is a founding member of the Lassonde School. With over 35 years of academic and industrial experience, he specializes in multisensor integration, GNSS technology, and precision engineering surveying. He holds a Dr.-Ing. in Geomatics Engineering from Universität der Bundeswehr München, Germany, alongside Bachelor’s and Master’s degrees from Wuhan Technical University of Surveying and Mapping (WTUSM). His research focuses on advanced data processing methodologies, including Kalman filtering, error analysis, and LiDAR systems. He has authored/co-authored over 60 publications, including textbooks like Error Theory and Foundation of Surveying Adjustment and Foundation of Geodesy . He is a Fellow of Engineers Canada and licensed as a Professional Engineer in Ontario. Education: Dr.-Ing., Geomatics Engineering, Universität der Bundeswehr München (Germany) M.Sc., Surveying Engineering, Wuhan Technical University of Surveying and Mapping B.Sc., Surveying Engineering, Wuhan Technical University of Surveying and Mapping Dr. Wang teaches courses such as Advanced Optimization and Applications , GNSS , and Global Geophysics and Geodesy . He leads the Earth Observation Laboratory (PSE 432), focusing on multisensor integration for navigation and positioning. His work explores innovative solutions for sensor calibration, data fusion, and geospatial applications. Grants & Labs: Active in lab-based research with collaborators like Baoxin Hu, his laboratory integrates GNSS, IMUs, LiDAR, and cameras for precision navigation. His recent work addresses challenges in sensor error calibration, LiDAR point cloud accuracy, and Kalman filter enhancements.
Timo Vesala is Professor of Meteorology and Academy Professor at the University of Helsinki’s Faculty of Agriculture and Forestry, Institute for Atmospheric and Earth System Research (INAR). He is also affiliated with the Viikki Plant Science Centre (ViPS) and serves as a supervisor in the Doctoral Programme in Atmospheric Sciences. Research Interests: Micrometeorology and biogeochemical cycles Ecosystem–atmosphere exchanges of greenhouse gases Eddy-covariance methodology and flux networks Boreal lakes, wetlands, and forests as components of the climate system Development of long-term observational infrastructures such as ICOS-Finland Recent research output (2023–2025) is dominated by high-impact articles in Advances in Atmospheric Sciences , Biogeosciences , Agricultural and Forest Meteorology , and Geophysical Research Letters , reflecting a balanced portfolio of process understanding, methodological advances, and large-scale synthesis studies. Scientific Awards: Academy Professor (Akatemiaprofessori) – awarded by the Academy of Finland Doctoral Advising & Grants: Supervised or co-supervised doctoral theses of Sheila Wachiye, E. Lopez-Blanco, and X. Li Principal Investigator on Academy of Finland project “The Hidden Role of Gases in Trees” (2021–2025) Project leader for “Kasvihuonekaasujen maa-ilmakehävaihto järvi- ja suoekosysteemeille” (2024–2026) Co-leader of the art-science initiative “Periferia – Metsätaiteeellinen asema” (2021–2031) Participant in EU flagship EMME-CARE (2017–2026) Labs & Teams: Timo Vesala heads the Micrometeorology Group at INAR and leads the Finnish ICOS (Integrated Carbon Observation System) network node. His team operates multiple eddy-covariance towers across boreal lakes, wetlands, and forests, integrating field observations with modeling and remote-sensing data.
Bruño Fraga is an Assistant Professor in the Department of Civil Engineering at the University of Birmingham, part of the School of Engineering. He specializes in Computational Fluid Dynamics (CFD) with a focus on turbulent and multiphase flows, particularly in applications like indoor air quality, water treatment, and airborne pathogen transport. His research group develops models such as Multiflow3D, addressing challenges in multiphase flow dynamics and environmental engineering. Education: MEng in Environmental Engineering (University of Santiago de Compostela, 1st class honors), MSc in Applied Math and Numerical Simulation (University of A Coruña), PhD in Civil Engineering (Universities of A Coruña and Chalmers). Research Interests: CFD modeling, bubble-induced turbulence, indoor air quality, water treatment technologies, and multiphase flow dynamics. Dr. Fraga leads major projects such as Fusion Forest (£1m, UKRI) and the IAQ-EMS initiative (£1m, Met Office), focusing on indoor air quality and pathogen transmission modeling. His work includes collaborations with organizations like Deltares Institute and Severn Trent, addressing wastewater treatment and environmental challenges. He is co-leader of the Fluids Research Group and the Water Technology stream at the University of Birmingham’s Water Centre. Scientific Awards: National Outstanding Graduate Prize (2011). Advising & Grants: Supervises graduate students in CFD and multiphase flow research. Oversees grants totaling over £2.1M, including fusion forest and buildair projects. Focuses on translating CFD expertise into real-world solutions for public health and environmental sustainability. Labs & Teams: Leads the Multiflow3D development team and collaborates with the Fluids Research Group and Water Technology stream.
Dr. Matthew Brookhouse is a Senior Lecturer at the Fenner School of Environment & Society, part of the Australian National University's Institute for Climate, Energy & Disaster Solutions. With a PhD in Dendroclimatology from ANU, he specializes in using forest structural complexity and tree-ring analysis to understand climate interactions and ecological responses in Australian subalpine environments. Research Focus: Sub-alpine ecology, Dendrochronology, CO2 responsiveness in eucalypt species Teaching: First-year research methods with emphasis on statistical application, advanced modeling and field botany Projects: Leading collaborative snow-gum dieback research and dendrochronological monitoring initiatives His publications span 2006-2025 with recent emphasis on machine learning applications for forest monitoring, tropical tree-ring chronologies for climate change, and climate sensitivity in Australian alpine ecosystems. Key collaborations include institutions like Australian Nuclear Science and Technology Organisation and University of Canberra researchers. Current projects focus on snow-gum woodland dieback mechanisms, high-resolution dendrometric monitoring, and integrating dendrochronology with environmental policy frameworks. He maintains active supervision of research students and contributes to both undergraduate and postgraduate curriculum development.
Dr. Jonathan Lenoir is a CNRS Researcher at the Ecology and Dynamics of Anthropized Systems (EDYSAN) laboratory, University of Picardie Jules Verne , France. His work bridges Ecology and Biostatistics , focusing on ecological dynamics under spatial and temporal global changes, particularly biotic responses to climate change. His research spans broad-scale biodiversity patterns, species distribution modeling, and microclimate ecology, with special attention to forest systems. Dr. Lenoir leads and contributes to multiple research projects including MaCCMic (Impact of forest Management and Climate Change on understory Microclimate) and IMPRINT (Impacts of Microclimatic Processes on forest Biodiversity redistribution under macroclimaTe warming). These projects utilize advanced technologies like LiDAR and microclimate sensors to model understory temperature dynamics and predict biodiversity responses to climate change. His recent publications analyze microclimate buffering in forests ( 2024 ), species thermophilization ( 2024 ), and the application of deep learning to habitat identification ( 2024 ). His work also explores interdisciplinary connections like eco-oncology , comparing invasion dynamics in ecology and medicine. Dr. Lenoir actively mentors researchers and supervises fieldwork campaigns, emphasizing rigorous data collection ( 180 monitoring plots across French forests ) and advanced statistical analyses in R . He collaborates with European institutions and participates in large-scale initiatives like ReSurveyEurope , a database of resurveyed vegetation plots.
Prof. Dr. Fabian Gieseke is a Professor and Chair of Machine Learning and Data Engineering at the University of Münster. He holds a PhD in Computer Science from Carl von Ossietzky University of Oldenburg and a dual degree in Mathematics and Computer Science from the University of Münster. His research focuses on Machine Learning, High-Performance Computing, and their applications in Geosciences, Smart Cities, and Astrophysics. Education: PhD in Computer Science (2012), Carl von Ossietzky University of Oldenburg University studies in Mathematics and Computer Science (2006–2011), University of Münster Research Interests: Data Mining and Machine Learning High-Performance Computing & Distributed Systems Deep Learning Applications in Environmental Science and Astrophysics Geospatial Data Analysis using Satellite Imagery Publications Trends: His recent work emphasizes large-scale environmental monitoring via deep learning, including canopy height estimation, forest biomass prediction, and national-scale tree counting. He also explores interactive systems for geospatial data retrieval and optimization of machine learning models for resource-constrained environments. Advising & Grants: Supervised over 30 theses on topics like satellite image analysis, deep learning on microcontrollers, and data marketplaces for smart grids. Active in securing grants for interdisciplinary projects combining AI with Earth observation. Labs/Teams: Leads the Machine Learning and Data Engineering group at the University of Münster, focusing on scalable AI solutions for real-world challenges in science and industry.
Dr. Philipp Porada is a Junior Professor of Ecological Modeling at the University of Hamburg, affiliated with the Department of Biology within the Faculty of Mathematics, Computer Science and Natural Sciences. He works at the Institute of Plant Sciences and Microbiology, specifically in the Applied Plant Ecology group, based at the Otto Warburg House. His research integrates process-based modeling with ecological field studies to investigate non-vascular vegetation, biogeochemical cycles, and climate-vegetation interactions across multiple temporal and spatial scales. Porada's research focuses primarily on non-vascular vegetation (bryophytes, lichens, and biocrusts), examining their role in global biogeochemical cycles, biodiversity-ecosystem functioning relationships, and paleoclimate dynamics. His work spans from contemporary ecosystem processes to geological time scales, with particular emphasis on the impacts of climate change on non-vascular communities. He has developed several process-based models including LiBry for lichen and bryophyte communities, LiDELS for soil-vegetation interactions, and LYCOm for early vascular plants. His research demonstrates how non-vascular vegetation influences carbon sequestration, water cycling, and soil processes across diverse ecosystems from urban forests to polar regions. Analysis of Porada's publication record reveals a strong interdisciplinary approach combining ecological theory, biogeochemistry, and computational modeling. His work spans multiple ecosystems including peatlands, drylands, urban forests, and coastal blue carbon systems. A consistent theme across his research is understanding how non-vascular vegetation mediates the relationship between environmental conditions and ecosystem functions. His most recent work increasingly focuses on climate change impacts and potential mitigation strategies through vegetation management. Porada leads two major research projects funded by the German Research Foundation (DFG): 'Effects of nutrient limitation on non-vascular vegetation under climate change' and 'The role of early plants for palaeoclimate dynamics'. These projects reflect his dual interest in contemporary environmental challenges and deep-time ecological processes. His collaborative work, evident in his extensive publication record with international researchers, demonstrates strong interdisciplinary connections across ecology, biogeochemistry, and climate science. Dr. Porada maintains an active research laboratory focused on ecological modeling, with particular expertise in non-vascular vegetation dynamics. His team develops and applies process-based models to address questions ranging from micro-scale lichen water relations to global biogeochemical cycles. The research group collaborates extensively with field ecologists, climate scientists, and biogeochemists to ground-truth model predictions and explore new ecological phenomena.
Min Chen is an Assistant Professor in the Department of Forest and Wildlife Ecology at the University of Wisconsin–Madison, affiliated with the Russell Labs. His research focuses on terrestrial ecosystem modeling, remote sensing applications, and human-Earth system interactions. He holds a PhD in Earth & Atmospheric Sciences from Purdue University, an MS in Remote Sensing and GIS from Beijing Normal University, and a BS in Computer Science from Beijing Normal University. His postdoctoral work included roles at the Carnegie Institution for Science and Harvard University. Research interests include forest carbon dynamics, methane emissions from wetlands, wildfire risk analysis, and the integration of remote sensing with Earth system models. His work emphasizes advancing methods for global-scale environmental monitoring using satellite and drone technologies. Notable contributions include studies on forest edge dynamics, vegetation-climate feedbacks, and the application of machine learning in ecological modeling. Recent publications highlight advancements in leaf trait prediction using transfer learning, global wetland methane flux modeling, and the impacts of climate change on land-use patterns. His lab develops innovative approaches to track terrestrial carbon cycles and assess human-driven environmental changes. Ongoing projects explore urban land expansion effects on carbon balances and phenological shifts under global change scenarios.
