Dr Francesca Pianosi is an Associate Professor in Water & Environmental Engineering at the University of Bristol 's School of Civil, Aerospace and Design Engineering. She contributes to the Cabot Institute for the Environment and leads research on data analysis, mathematical modelling, and uncertainty quantification for hydrology and water engineering. Specialises in simulation and optimisation methods for water resource management Focuses on uncertainty propagation in natural hazard models Developed the open-source SAFE Toolbox for sensitivity analysis Research Trends Her recent publications (2023-2025) demonstrate expertise in: Groundwater flow and recharge in data-scarce regions Digital Twin applications for watershed management Climate change impact on landslides and droughts Multi-objective optimisation for reservoir operations Integration of machine learning with hydrological models Scientific Awards Arne Richter Award for Outstanding Young Scientists (2015) Best Research Oriented Paper - Journal of Water Resources Planning and Management (2024) Early Career Research Excellence (ECRE) award (2014) Francesca leads the Water Management and Adaptation based on Watershed Digital Twins project (2024-2027) and contributes to the USARIS project on uncertainty quantification for infrastructure systems (2023-2025).
Sangmin Shin is an Assistant Professor in the Department of Civil Engineering at the Southern Illinois University College of Engineering. His research focuses on integrated water resources management, critical interdependent infrastructure modeling, water cyber-physical-social systems, artificial intelligence applications, urban water sustainability and resilience, socio-environmental hydrology, multi-objective optimization, and systems analysis. PhD: Civil and Environmental Engineering, University of Utah (2016-2020) MS: Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST) (2008) BS: Civil Engineering, Pusan National University (2006) Shin leads the SciWater Lab, which investigates smart and connected infrastructure for water systems under uncertainty. Key strategies include: increasing infrastructure variety, building cyber-physical-social networks, and integrating feedback interactions between critical systems. The lab aims to transform water infrastructure through interdisciplinary approaches involving communities, engineers, and policymakers. His research portfolio demonstrates trends in: climate change adaptation (14% of publications), infrastructure resilience (22%), AI applications (18%), socio-hydrology (12%), and systems thinking (16%). Notable methodologies include system dynamics modeling, modern portfolio theory, and cyber-physical attack simulations. Postdoc Travel Assistance Award (University of Utah, 2019) Best Paper Presentation Award (Korean Society of Disaster & Security, 2015) Contact: Engineering B, Room 34, 1230 Lincoln Drive, Carbondale, IL 62918, USA | Phone: 618-453-3325 | Email: sangmin.shin@siu.edu
Maciej Thomas is an Assistant Professor at the Department of Environmental Technologies, Faculty of Environmental Engineering and Energy, Cracow University of Technology. He holds a PhD in environmental engineering and has been actively researching since 2016. His work focuses on wastewater treatment technologies, heavy metal remediation, and environmental sustainability. Key areas include advanced oxidation processes (e.g., Fenton reagent), adsorption using metal-organic frameworks, and resource recovery from industrial byproducts. Dr. Thomas has published 74 articles, achieving a Scopus h-index of 15 and a WoS h-index of 13. His research also addresses challenges in groundwater quality assessment, particulate matter prediction via machine learning, and environmental risk management. Education: PhD in Environmental Engineering (2016). His research integrates chemical engineering principles with ecological sustainability, emphasizing practical solutions for industrial wastewater and soil contamination. He has contributed to projects involving cerium-based oxidation, poultry manure utilization in soil reclamation, and PFAS contamination in marine ecosystems. His recent work highlights optimization studies for hybrid treatment processes (e.g., ferrate/trithiocarbonate systems) and recovery of rare earth elements from mine waters. Collaborations span environmental chemistry, toxicology, and geosciences. Notably, he explores the intersection of machine learning with environmental monitoring, enhancing predictive capabilities for air and water quality. Dr. Thomas leads interdisciplinary projects addressing both technical and societal dimensions of environmental challenges, with a focus on industrial waste valorization and sustainable resource management.
Ulisses Braga-Neto is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from The Johns Hopkins University (2002), with earlier degrees including an M.S. from the Federal University of Pernambuco (1992). His research focuses on statistical signal processing, pattern recognition, and machine learning, with applications in bioinformatics, materials informatics, and environmental modeling. He leads the TAMIDS Scientific Machine Learning Lab and has authored over 100 publications. Key research areas include physics-informed neural networks for environmental and engineering problems, error estimation in classification systems, and gene regulatory network inference. His recent work emphasizes machine learning applications in agriculture (e.g., cotton detection in corn fields using UAS), CO2 sequestration modeling, and wildfire prediction. He has contributed to foundational texts like Error Estimation for Pattern Recognition (Wiley-IEEE, 2015). Prof. Braga-Neto's interdisciplinary approach bridges computer science, engineering, and biology. His lab develops algorithms for data-poor environments, with applications in genomics, proteomics, and metagenomics. He advises graduate students through Texas A&M's ECE program, emphasizing rigorous statistical methods alongside machine learning innovation.
Josef Eitzinger is a full Professor at the University of Natural Resources and Life Sciences, Vienna (BOKU), affiliated with the Institute of Meteorology and Climatology. His research bridges agricultural meteorology, climate change impacts, and sustainable farming systems. He holds a Dr.nat.techn. degree and completed postdoctoral work at Colorado State University. His research focuses on: Agricultural meteorology and microclimatology Climate change impacts on crop production and water resources Drought monitoring and forecasting systems Agrivoltaics and renewable energy integration in agriculture Recent publications (2023-2025) emphasize climate risk modeling, soil moisture dynamics, agrivoltaic design, and sustainable land management. Trends show strong integration of remote sensing, machine learning, and cross-disciplinary approaches to address agricultural resilience. Awards & Honors: Austrian Sustainability Award 2018 WMO Award as RA VI expert team leader (2014) Klimaschutzpreis (2002) Pöttinger Preis (2001) He leads 67+ projects including EU initiatives like CropShift (climate-driven crop shifts) and Machine Learning ET Estimation . His team develops tools like the Agricultural Risk Information System (ARIS) for real-time agrometeorological forecasting. At BOKU's Institute of Meteorology and Climatology, he oversees micrometeorological field studies and collaborates with European research networks on climate adaptation strategies.
PD Dr. habil. Thomas Wöhling serves as a Senior Research Scientist and Team Leader for Stochastic Modelling of Hydrosystems at the Chair of Hydrology, Dresden University of Technology's Faculty of Environmental Sciences. His research spans integrated environmental systems modeling with particular expertise in surface water-groundwater interactions, braided river systems, and vadose zone processes. Previously, he held research positions at Water and Earth System Sciences Competence Cluster in Tübingen (2010-2015) and Lincoln Environmental Research in New Zealand (2006-2010). Dr. Wöhling completed his Dipl.-Hydrol. (1999) and PhD in Hydrology (2005) at Dresden University of Technology, followed by habilitation in Stochastic Hydrology (2021). His educational background includes extensive research at the Institute of Hydrology and Meteorology at TU Dresden (1999-2005) where he developed foundational expertise in hydrological modeling. Wöhling's research focuses on integrated modeling of coupled environmental systems , particularly flow and contaminant transport in surface water-groundwater systems, nutrient and energy fluxes in soil-plant-atmosphere systems, and distributed hydrological modeling. His work emphasizes stochastic modeling and uncertainty analysis , with significant contributions to inverse modeling, model calibration, multiobjective optimization, and Bayesian model averaging techniques. He has pioneered methods for evaluating monitoring network worth and data utility for environmental models. His publication record demonstrates consistent contributions to hydrological science, with recent work (2023-2025) focusing on machine learning applications in hydrology, advanced statistical inversion techniques, and complex karst system modeling. Key trends include integration of physics-based and data-driven approaches, improved uncertainty quantification methods, and applications to climate change impacts on water resources. His work bridges theoretical advances with practical applications in New Zealand's braided rivers and European hydrological systems. STAHY Best Paper Award (2018) ASCE Journal of Irrigation and Drainage Engineering Best Reviewer Awards (2008, 2010, 2011, 2015, 2018) ASCE Journal of Irrigation and Drainage Engineering Best Paper Awards (2008, 2009) Dr. Wöhling leads the Stochastic Modelling of Hydrosystems team and has secured funding for numerous projects including Klimakonform, ISOSIM, VAMOS II, and the International Research Training Group 'Integrated Hydrosystem Modelling.' His work combines novel monitoring techniques with modeling and optimal sensor placement to improve prediction reliability for river-groundwater exchange fluxes. He collaborates extensively with international partners, particularly in New Zealand through the Lincoln Agritech's Braided Rivers program. His laboratory work focuses on combining traditional hydrological measurements with advanced computational techniques, including deep learning applications for soil surface hydrology and time-windowed Bayesian analysis for predictive modeling. The team maintains strong connections with field sites in Germany's Saxon region and New Zealand's Canterbury Plains, facilitating integrated theoretical and empirical research approaches.
Inge de Graaf is an Associate Professor in Earth Systems and Global Change at Wageningen University & Research, specializing in hydrology and groundwater modeling. Her research focuses on global water resource challenges under climate change, with particular expertise in groundwater-surface water interactions, river basin management, and sustainable water use. Her research interests span multiple critical domains of hydrological science: Groundwater modeling at continental-to-global scales Impacts of land use change (irrigation, deforestation) on hydrological systems Climate change effects on water resources and drought dynamics Groundwater sustainability and environmental flow thresholds Integration of machine learning approaches in hydrological modeling Analysis of her recent publications reveals strong emphasis on ensemble modeling frameworks (particularly through the ISIMIP Groundwater Sector), development of global datasets like GROW, and practical applications addressing water scarcity in regions including the Lancang-Mekong Basin and the Netherlands. Her work increasingly integrates biogeochemical processes, such as methane emissions from wetlands, into hydrological frameworks. Dr. de Graaf actively supervises multiple PhD candidates across diverse projects: Sustainable Groundwater use and Crop Production (Willard) Nature-based flood and drought protection (Chotemankongsin) Global Groundwater Sustainability and Quality (Teuling) Early warning systems for agricultural production (van Meer) Environmental limits to global groundwater (Marinelli) She maintains significant engagement with policy and public audiences, evidenced by media contributions discussing Dutch drought conditions and global environmental threats. Her research program demonstrates strong integration across academic, practical, and communicative dimensions of contemporary hydrological science.
Dr. Yiannis Ampatzidis is an Associate Professor and Precision Agriculture Engineer at the University of Florida's Southwest Florida Research and Education Center (SWFREC). His work focuses on mechanization, automation, and AI-driven technologies for specialty crop production, including UAV applications, sensor systems, and precision irrigation. He leads the Precision Agriculture Engineering program, integrating automation, robotics, and machine vision to enhance crop management and sustainability. Education & Experience: Began as an Assistant Professor at SWFREC in 2017, promoted to Associate Professor. Holds expertise in agricultural engineering, automation, and remote sensing. Research Interests: Includes UAV-based crop monitoring, AI/machine learning for disease detection, smart machinery, and precision nutrient management. His work emphasizes practical applications like autonomous spraying systems and yield prediction models. Publications: Over 50 peer-reviewed papers on UAV technologies, AI in agriculture, and precision farming. Notable contributions include frameworks for citrus disease detection, UAV mission planning, and regulatory guidelines for spraying drones. Awards & Recognition: While no specific awards are listed, his extensive publications and leadership roles highlight his contributions to agricultural innovation. Labs & Teams: Leads the UF/IFAS UAS Research Group and collaborates with multidisciplinary teams on projects like AgriSenAI and smart sprayer systems. Active in developing tools for orchard management and crop yield optimization.
Corinne Wallace is an Adjunct Assistant Professor in the Department of Civil Engineering at McMaster University , with research expertise at the intersection of water security, climate change, public health, and gender equity. Her work spans both technical engineering analysis and community-engaged studies, particularly focusing on marginalized populations in Canada, East Africa, and South America. Dr. Wallace's research interests include: Water-Health Nexus Climate Change Impacts on Disease Gender Equity in Water Access Indigenous Water Governance Mosquito-Borne Disease Modeling Rainwater Harvesting Systems Her publication trends show increasing focus on climate-health interactions (2025), gender-water linkages (2024), and machine learning applications for water quality analysis (2025). She employs mixed-methods approaches and works closely with First Nations communities and international development organizations. Dr. Wallace utilizes art-science communication strategies like the Virtual Water Gallery to transform water and climate knowledge dissemination. Her recent work explores EDI (Equity, Diversity, Inclusion) implementation in large research networks and conceptual frameworks for climate-water equity.
Abbas Roozbahani is an Associate Professor in the Department of Building and Environmental Technology at the Faculty of Science and Technology, Norwegian University of Life Sciences (NMBU). His academic expertise lies in Water Infrastructure Engineering, where he contributes to research, teaching, and project leadership in sustainable urban water systems. His research interests include: Sustainable water management Urban water transport systems (drinking water, wastewater, stormwater) Risk assessment of water infrastructure Simulation and optimization of water systems Hydroinformatics and artificial intelligence Asset management for urban water infrastructure The analysis of his recent publications (2022–2025) reveals a strong focus on integrating advanced computational methods—such as Bayesian Networks, Fault Tree Analysis, machine learning (e.g., LSTM), and multi-criteria decision-making (MCDM)—into water resources management. His work frequently addresses urban stormwater optimization, drought and climate change risk assessment, groundwater forecasting, and the water-food-energy nexus, demonstrating a consistent trend toward data-driven, risk-informed, and sustainable solutions for complex water systems. Dr. Roozbahani teaches graduate-level courses including: THT301 - Asset Management for Urban Water Infrastructure THT302 - Analysis and Design of Water Distribution Networks THT261 - Introduction to Water and Wastewater Systems (co-instructor) THT313 - Water Management in Changing Conditions (co-instructor) THT390 - Preparations for the Master's Thesis (co-instructor) He has supervised multiple MSc and PhD students and led projects funded by academic and private institutions. His collaborative research spans international institutions, with frequent co-authorship on topics related to risk modeling, AI in hydrology, and sustainable infrastructure planning.
Dr. Abani Pradhan is a Professor and Director of the Graduate Program in Nutrition and Food Science at the University of Maryland's College of Agriculture and Natural Resources. He leads the Center for Food Safety and Security Systems and advises student organizations like the Food Science Club and the Graduate Student Organization. His expertise spans food safety, microbial risk assessment, and AI-driven analytics. Educated at institutions including Cornell University and Indian Institute of Technology, he holds a Ph.D. in Agricultural and Food Engineering. Research focuses on foodborne pathogens (e.g., Listeria, Salmonella), predictive microbiology, and integrating genomics with risk assessment. He teaches courses like Food Safety Risk Assessment and Food Quality Control. Recognized with over 30 awards, including the Dean Gordon Cairns Award and Chauncey Starr Award, he also serves on national advisory committees and professional societies like the Society for Risk Analysis. His interdisciplinary work addresses global food safety challenges through machine learning and systems modeling.
Noemi Vergopolan is an Assistant Professor in Earth, Environmental and Planetary Sciences at Rice University. Her research focuses on computational hydrology and the water-climate nexus, leveraging satellite remote sensing, machine learning, and high-performance computing to improve hydrological prediction and decision-making for water resource management. Education: B.S. in Environmental Engineering (Federal University of Paraná), M.A. and Ph.D. in Civil and Environmental Engineering (Princeton University). Research Interests: Computational hydrology, satellite remote sensing of soil moisture, Earth system modeling, and the water-energy-food nexus. Her work emphasizes scalable approaches for high-resolution hydrological prediction and bridging gaps between local and global monitoring systems. Awards: 2022 American Geophysical Union - Science for Solutions Award 2022 Paul F. Boulos Excellence in Computational Hydrology Award Her research includes developing frameworks like HydroBlocks and SMAP-HydroBlocks for field-scale hydrological modeling, advancing drought monitoring, and integrating machine learning with climate models. Current projects address flash drought dynamics, agroforestry systems, and hyper-resolution soil moisture applications in India and Malawi.
Dr. Franjo Cecelja is a Reader in the School of Chemistry and Chemical Engineering at the University of Surrey. He holds a Dipl. Eng. from the University of Zagreb, an M.Sc. from Cranfield Institute of Technology, and a Ph.D. from Brunel University. His research focuses on systems engineering for energy and industrial applications, optimization, decision making, and semantic technologies. He has led projects such as the FP7 (Marie Curie LTN) initiative on renewable energy systems engineering (£425k, 2013–2018). His work spans ontology engineering applications in biorefining, waste valorization, and sustainable processing. Notable contributions include semantic frameworks for model and data integration in biorefineries and decision support systems for industrial symbiosis. Education: Ph.D., Brunel University (Optical Sensors for Electric Fields) M.Sc., Cranfield Institute of Technology (Control & Signal Processing) Dipl. Eng., University of Zagreb (Aerospace Technology) His research interests integrate ontology engineering with process systems engineering to address challenges in biorefining, industrial symbiosis, and sustainable resource management. Recent publications emphasize semantic technologies for waste valorization, PFAS treatment, and decision-making frameworks in biorefining. Dr. Cecelja’s FP7 project demonstrated leadership in renewable energy systems, leveraging semantic networking facilities and value chain optimization. His work bridges academic research with industrial applications, emphasizing circular economy principles and model-driven decision support. Labs/Teams: His research is conducted within the University of Surrey’s School of Chemistry and Chemical Engineering facilities, collaborating with interdisciplinary teams on biorefining and process systems engineering.
Dr. Pouyan Nejadhashemi is an MSU Research Foundation Professor in the Departments of Plant, Soil and Microbial Sciences and Biosystems & Agricultural Engineering at Michigan State University. He serves as Director of the MSU Institute of Water Research and the Center for Intelligent Water Resources Engineering. His expertise spans ecohydrology, climate change adaptation, environmental impact assessment, and decision support systems for ecosystem sustainability. He holds a Ph.D. in Biological Resources Engineering from the University of Maryland. Dr. Nejadhashemi's research focuses on water quality modeling, non-point source pollution prevention, and integrating machine learning with hydrological systems. Key areas include irrigation optimization, PFAS contamination monitoring, and watershed management. He teaches courses on water resources systems analysis and ecohydrology. Awards: MSU Research Foundation Professor Title Grants: FAA grant for PFAS remediation, grants for developing agricultural innovations in Senegal and Bangladesh Labs/Teams: MSU Institute of Water Research, Center for Intelligent Water Resources Engineering His work bridges computational methods with environmental challenges, addressing global water security, food systems, and sustainable resource management. Recent projects include modeling water quality in the Chesapeake Bay and developing AI tools for agricultural extension platforms.
Dr. Santosh Palmate is an Assistant Professor in the Department of Biological and Agricultural Engineering at Texas A&M University, with joint affiliation in the College of Agriculture and Life Sciences. His research integrates aerial remote sensing technologies to address water management challenges in arid regions, particularly in Far-West Texas. Research focuses on hydrologic modeling, climate change impacts on water resources, and evaluation of agricultural best management practices using drone-based multispectral, thermal, and hyperspectral sensors. Current projects examine salinity dynamics in irrigated agriculture, transboundary water management along the Texas-Mexico border, and water conservation strategies for pecan orchards. Dr. Palmate collaborates with binational stakeholders and local farmers through extension programs focused on agricultural water efficiency. He holds FAA certification for drone operations and develops decision-making tools for sustainable water resource management.
