Dr. Kieran Wood is a Lecturer in Aerospace Systems at the University of Bristol, specializing in aerial robotics and remote sensing for hazardous environments. His work focuses on advancing unmanned aerial systems (UAS) for applications such as volcanic plume monitoring and post-disaster radiation mapping. He holds a PhD in Control Strategy for Micro Aerial Vehicles and has extensive experience in UAS design, testing, and field experiments. Education: Doctor of Philosophy (2009–2013), University of Bristol Master of Engineering (2005–2009), University of Bristol Research Interests: UAS development for environmental sensing Volcanic gas and ash monitoring Nuclear disaster response using robotics Sensor design and post-processing methodologies His research integrates robotics, control systems, and environmental science to address challenges in hazardous environments, with notable contributions to volcanic plume 3D reconstruction and radiation mapping in Chernobyl. Projects & Grants: Leads the 'Aerospace Engineering' project (2010–2035), involving UAS development for low-Earth-orbit robotics and sensor integration. Advises multiple postgraduate researchers in robotics and environmental monitoring. Labs & Teams: Collaborates with interdisciplinary teams on volcano monitoring and nuclear robotics, including projects with institutions like the University of Bristol's Mechanical and Aerospace Engineering department.
Tahiya Chowdhury is an Assistant Professor of Computer Science at Colby College. Her research focuses on intersections of machine learning, computer vision, and human-centric AI applications. She teaches courses including CS166 (Computational Thinking: Computer Vision), CS232 (Computer Organization), and CS343 (Neural Networks). Her work emphasizes ethical AI, multimodal interaction analysis, and sustainable technology solutions. Research interests span feature reliability in open-source tools, autism interaction studies using non-verbal cues, and urban policy compliance monitoring via vision-language sensing. Her projects include semantic segmentation frameworks for environmental monitoring and frameworks for marine debris cleanup without human labels. She explores user-centric approaches in street view image quality ranking and child-centered AI ethics. Her publications from 2021-2025 reflect a trend toward applied AI in healthcare (e.g., Medbuds medication tracking), environmental sustainability (atoll imagery analysis), and pandemic-era communication studies. Despite prolific output, no scientific awards are listed in the provided materials. Advising and grant information are not documented here. Her work integrates hardware-software systems like the Maestro ambient sensing platform, emphasizing practical deployments in smart environments while addressing privacy challenges. Key themes include reducing labeling efforts in IoT systems and designing socially responsible AI interfaces for diverse user groups.
University of Illinois Urbana-ChampaignUnited States
Hamze Dokoohaki is an Adjunct Assistant Professor at the National Center for Supercomputing Applications (NCSA) and the Center for Digital Agriculture, University of Illinois. His research focuses on advancing agricultural sustainability through integrated modeling, remote sensing, and data assimilation techniques. Key areas include crop yield prediction, soil carbon dynamics, and the impact of cover crops and biochar in agricultural systems. Research Interests: Dokoohaki’s work spans crop modeling (APSIM, CERES-Maize), soil moisture and nitrogen dynamics, and climate-smart agriculture. He explores how remote sensing and Bayesian optimization can enhance agricultural forecasting, particularly in arid and Midwest regions. Recent projects assess cereal rye cover crops' long-term benefits in Illinois and biochar's role in improving soil health. Collaborations: His work leverages interdisciplinary teams to address challenges in carbon sequestration, greenhouse gas emissions, and sustainable farming practices. He contributes to tools like PEcAn and Comet-Farm for carbon budgeting and agricultural decision-support systems. Technical Contributions: He has developed data-assimilation frameworks integrating remote sensing with crop models, improving yield predictions and resource management. His research emphasizes scaling carbon dioxide removal strategies and verifying soil carbon sequestration at regional scales.
Prof. Dr. Birgit Gemeinholzer is a Professor at the University of Kassel, where she serves as Head of Working Group and Chair Holder in the Department of Botany within the Institute of Biology (FB 10). Her academic journey began with horticulture studies at the Berlin University of Applied Sciences, followed by a Master of Science at the University and Royal Botanic Gardens Edinburgh. She earned her PhD at the University of Heidelberg focusing on Solanaceae phylogeny, held postdoctoral positions at IPK-Gatersleben, and worked as Research Associate at the Botanical Garden and Botanical Museum Berlin-Dahlem. Prior to her current position at Kassel, she served as Academic Councillor at Justus Liebig University of Giessen, where she completed her habilitation in plant systematics and biodiversity research and was appointed extraordinary professor in 2017. Prof. Gemeinholzer's research focuses primarily on plant metabarcoding, population genetic analyses of endangered plants, and networking of biological data. Her work bridges molecular techniques with ecological and conservation applications, particularly in understanding plant-insect interactions, genetic diversity in threatened species, and the development of biodiversity monitoring systems. She has made significant contributions to DNA barcoding methodologies, metabarcoding applications for environmental monitoring, and the conservation genetics of endangered plant species. Her research integrates cutting-edge molecular approaches with traditional botanical knowledge to address pressing conservation challenges. Analysis of her recent publications reveals a strong emphasis on developing and applying molecular techniques for biodiversity assessment and conservation. Her work spans from fundamental methodological improvements in DNA metabarcoding to applied studies on plant-pollinator interactions, genetic consequences of habitat fragmentation, and the development of automated biodiversity monitoring systems. She frequently collaborates across disciplines, working with ecologists, data scientists, and conservation practitioners to translate molecular findings into practical conservation applications. Her research shows a clear trajectory toward integrating artificial intelligence with molecular data for more comprehensive ecosystem understanding. Prof. Gemeinholzer has been actively involved in major research initiatives including NFDI4Biodiversity (National Research Data Infrastructure for Biodiversity), the Global Genome Biodiversity Network (GGBN), and the DNA Bank Network. She has contributed significantly to developing data standards and infrastructure for biodiversity research, particularly in the areas of DNA barcoding and metabarcoding. Her work on the Disentis Roadmap for biodiversity data release demonstrates her commitment to open science and data sharing practices in biodiversity research. At the University of Kassel, Prof. Gemeinholzer leads research activities focused on plant biodiversity assessment using molecular methods. Her laboratory develops and applies metabarcoding techniques for environmental monitoring, with particular emphasis on plant-insect interactions and conservation genetics of endangered species. She collaborates extensively with other researchers across Germany and internationally, contributing to large-scale biodiversity monitoring initiatives and conservation projects. Her work bridges fundamental research in plant systematics with practical applications for nature conservation.
Tan Phat Huynh is an Associate Professor (tenure track, level 2) at Åbo Akademi University’s Faculty of Science and Engineering, Department of Chemistry and Chemical Engineering. His research focuses on materials science, metabolomics, and sustainable sensor technology. Education: Doctorate in Physical Chemistry, Polish Academy of Sciences Research Interests: Development of biocompatible nanocomposites and hydrogels Non-invasive cancer diagnosis via NMR metabolomics Machine learning for medical diagnostics Green technology and sustainable materials Wearable and plant-inspired sensors Publication Trends: Recent work emphasizes prostate cancer biomarker detection through metabolomics, sustainable hemicellulose-based sensors, and advanced algorithms like Protomix for NMR data analysis. Collaborative projects span molecularly imprinted polymers, self-healing materials, and international education initiatives. Grants and Collaborations: He leads projects funded by the Academy of Finland and Finnish Education and Culture Ministry, including a double-degree program with IIT Mandi. Collaborations involve Finnish-Indian research networks and Ukraine education rebuilding efforts. Lab and Team: His work integrates molecular engineering with clinical applications, supported by Åbo Akademi’s Printed Intelligence Infrastructure project and international academic visitors hosted in 2024.
Arlene John is an Assistant Professor at the Biomedical Signals and Systems (BSS) group within the Faculty of Electrical Engineering, Mathematics and Computer Science at the University of Twente. She holds a Ph.D. in Electrical and Electronic Engineering from University College Dublin (2022), with prior academic experience including a bachelor’s degree in Electrical and Electronics Engineering from the National Institute of Technology, Calicut (2017) and research internships at the Indian Institute of Science (2016) and Beijing University of Technology (2019). Bachelor: Electrical and Electronics Engineering, National Institute of Technology, Calicut (2017) Ph.D.: School of Electrical and Electronic Engineering, University College Dublin (2022) Her research focuses on biomedical signal processing, machine learning, explainable AI, and multisensor data fusion for wearable health monitoring devices. She has industry experience as a Project Manager at Bosch India Ltd. and a Machine Learning Mathematics Engineer at ASML Netherlands B.V., bridging technical sales, engineering strategy, and computational modeling. Recent publications emphasize language testing frameworks, CEFR alignment, and psychometric modeling for vocabulary and grammar assessment. These works span interdisciplinary themes in education, linguistics, and standardized evaluation systems.
Stanislav Shevchuk is a Researcher at the Institute of Engineering Geodesy within the Faculty 6: Aerospace Engineering and Geodesy at the University of Stuttgart. He contributes to the Cluster of Excellence IntCDC, focusing on integrative computational design and construction for architecture. His contact details include a phone number (+49 711 685 84050) and physical address (Geschwister-Scholl-Str. 24D, 70174 Stuttgart, Germany). Research Project: RP 16-2 – Cyber-Physical On-Site Construction Processes using a Spider Crane Robotic Platform His expertise spans GNSS technologies , multi-sensor data integration , and map matching . He teaches exercises in Monitoring (Geomatics Engineering, Master 1st semester) and Terrestrial Multisensor Systems (Geomatics Engineering, Master 3rd semester).
Professor Matthias Braun is a distinguished academic in the field of physical geography, specializing in remote sensing and GIS applications for glaciology and polar research. He holds a professorship at the Institute of Geography at Friedrich-Alexander University Erlangen-Nuremberg (FAU), where he leads the Chair of Geography (Remote Sensing and GIS) and serves as Chairman of the Examination Board for B.Sc./M.Sc. Physical Geography and BA/MA Cultural Geography since 2022. His research focuses on monitoring glacier dynamics, ice sheet changes, and climate impacts in polar and mountainous regions using advanced remote sensing techniques. Professor Braun has held several significant leadership positions including Chairman of the International Doctoral Program 'Measuring and Modelling Mountain Glaciers in a Changing Climate' in the Bavarian Elite Network funded by the Bavarian Ministry of Science & Art since 2022, and Coordinator of the DFG SPP Antarctic Research since 2017. His academic journey includes an Associate Professor position at the University of Alaska Fairbanks (2010-2011) and extensive field experience leading multiple Arctic and Antarctic expeditions since 1994/95, with research stays in Alaska, South America, West & East Africa, Himalaya & Karakorum. His research interests span glaciology, remote sensing, geographic information systems, climate change impacts, land use change, polar regions, and high mountain environments. Professor Braun's work integrates microwave and optical remote sensing data from satellite and airborne platforms to derive geobiophysical parameters and their spatiotemporal variations. He employs advanced digital image processing, pattern recognition, SAR interferometry, and polarimetry techniques in his research. His laboratory maintains active participation in major research initiatives including the TanDEM-X and TanDEM-L Science Teams since 2010. Professor Braun's extensive publication record demonstrates a clear progression from foundational work on glacier monitoring to sophisticated applications of machine learning and deep learning for glacier feature extraction. His recent work focuses on calving front detection using SAR imagery, glacier velocity mapping, and integration of multi-sensor data for comprehensive glaciological analysis. Key research themes include glacier mass balance, ice sheet dynamics, supraglacial hydrology, and climate change impacts on cryospheric systems across diverse regions including Antarctica, Patagonia, the Himalayas, and the European Alps. Among his notable recognitions is the 2009 Science Award for Physical Geography from the Prof. Dr. Frithjof Voss Foundation for Geography and his Habilitation at the Mathematical-Natural Science Faculty of the University of Bonn in 2009. He serves as an Associate Editor for Frontiers in Earth Sciences – Cryospheric Sciences and reviews for numerous peer-reviewed journals. Professor Braun has mentored numerous doctoral students to completion, with recent graduates including Dr. Christian Sommer (2022), Dr. David Farias Barahona (2021), Dr. Stefan Lippl-Seifert (2020), and Dr. Peter Friedl (2019). Several students are currently completing their dissertations under his supervision. His research is supported by various funding mechanisms including the Bavarian Elite Network, DFG research programs, and international collaborations. He maintains strong connections with national and international research institutions including membership in the International Glaciological Society (IGS), German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF), German Society for Polar Research (DGP), and German Society for Geography (DGfG).
Dr. Alex Mouapi is a Lecturer in the Department of Electrical Engineering at Polytechnique Montréal, specializing in energy harvesting systems for wireless communications and IoT. His research bridges theoretical frameworks with industrial applications, particularly in autonomous sensor networks. Education Ph.D. in Engineering, Université du Québec à Chicoutimi (UQAC)/Université du Québec en Abitibi-Témiscamingue (UQAT) M.Sc. in Engineering, Université du Québec en Abitibi-Témiscamingue (UQAT) Master in Physics (Electronics), University of Dschang, Cameroon B.Sc. in Physics, University of Dschang, Cameroon Diploma in Technical Education (Electrotechnics), University of Douala, Cameroon Research Focus Dr. Mouapi's research centers on sustainable energy solutions for wireless devices. Key areas include: Energy Harvesting Systems: RF and piezoelectric microgenerators for self-powered sensors Circuit Design: Optimization of voltage doublers, rectifiers, and power management circuits Industrial IoT: Applications in mining, manufacturing, and infrastructure monitoring Predictive Algorithms: Energy forecasting and allocation for network efficiency Publication Trends His 33 publications demonstrate consistent focus on energy autonomy for industrial sensors. Recent works (2020-2022) emphasize RF harvesting circuit optimization and IoT integration, with theoretical contributions in efficiency modeling and practical validations through industrial case studies. Laboratory & Collaborations While no dedicated lab is specified, his research involves simulations and prototyping for energy systems. Collaborations include industrial partners in mining and manufacturing sectors.
Dr. Grzegorz Jasiński serves as an Assistant Professor in the Department of Biomedical Engineering at the Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology. His academic work centers on sensor technology development for environmental and biomedical applications, with emphasis on electrochemical systems and air quality monitoring. His research portfolio demonstrates deep expertise in gas sensor technology , particularly addressing critical challenges in: Amperometric and metal-oxide sensor reliability under environmental interference (humidity, temperature) Advanced signal processing techniques for fault detection in sensor arrays Selectivity enhancement in multicomponent gas mixtures Electrochemical degradation mechanisms in energy systems like solid oxide fuel cells Analysis of his 2020-2025 publications reveals consistent methodological focus on experimental validation of sensor performance under real-world conditions, with increasing sophistication in diagnostic approaches like EIS-DRT analysis. His work bridges fundamental electrochemistry with practical environmental monitoring solutions. Dr. Jasiński leads externally funded research initiatives, notably the LIDER-program project "Wieloczujnikowy system pomiaru zanieczyszczeń powietrza" (Multisensor Air Pollution Measurement System) initiated in 2011, demonstrating sustained research leadership in sensor network development.
Prof. Dr. Thorsten Uphues is a Professor at Coburg University of Applied Sciences, Faculty of Applied Natural Sciences and Health. He specializes in applied sensor technology and acoustics research, with current leadership in the KonDispUS project (2024-2026) developing ultrasonic multisensor systems for concentration analysis in liquid dispersions. His research focuses on: Acoustic measurement principles Ultrasonic sensor design Multisensor data fusion Concentration quantification in complex liquids Non-invasive industrial analytics Contact: Thorsten.Uphues@hs-coburg.de | ORCID iD: 0000-0003-3423-4510
Anette Eltner is a Junior Professor for Geosensor Systems at the Technical University of Dresden since 2021. Her work focuses on developing innovative methods for environmental monitoring using AI and remote sensing technologies. She leads research projects that aim to improve flood forecasting systems and monitor geomorphological changes through advanced imaging techniques. Her educational background includes: PhD in Geography from TU Dresden (2016), awarded by the German working group for geomorphology Diploma in Geography from TU Dresden (2010) with minors in Photogrammetry/Remote Sensing, Soil Science and Hydrology Professor Eltner's research spans multiple cutting-edge areas in geospatial technology and environmental science. She specializes in UAV photogrammetry and remote sensing, developing methods for precise geomorphological and hydrological monitoring. Her work integrates artificial intelligence with environmental sciences to create more accurate predictive models for natural processes. She focuses particularly on erosion processes in fragile landscapes and has pioneered techniques for spatio-temporal high resolution topography using laserscanning, structure-from-motion, and time-lapse imaging. Her innovative approach to image processing enables automatic feature detection and tracking in geographic applications, while her development of low-cost geosensor systems makes advanced monitoring accessible in resource-limited settings. Her publication record demonstrates consistent advancement from foundational photogrammetric techniques to sophisticated AI integration for real-time environmental assessment. The research shows increasing emphasis on practical flood warning applications and dynamic landscape monitoring, particularly for small waterways where traditional monitoring systems are lacking. Her notable scientific achievements include: PhD thesis award from the German working group for geomorphology (AK Geomorphologie) TUD Young Investigator status (recognizing excellent, independent junior research group leaders) Editorial board membership for Geoscientific Instrumentation, Methods and Data Systems and Photogrammetric Record Professor Eltner leads the "Artificial Intelligence for Flood Warning (KIWA)" project, evaluating camera-based water level monitoring systems along small tributaries of the Elbe River in Saxony. Her research group develops AI-supported methods to analyze water surface imagery and predict flood events in small waterways. She has secured funding for projects in Germany and internationally, including current work in Oman combining cameras and seismic sensors to monitor rare flood events in wadis. Her team is developing systems that allow sensors to communicate with each other for dynamic environmental monitoring. Her laboratory work focuses on developing cost-effective monitoring solutions using accessible technology like Raspberry Pi computers and standard cameras. She has demonstrated that these low-cost systems can effectively monitor significant landscape changes, making advanced environmental monitoring more widely available. Current projects involve creating 3D point cloud analysis methods that incorporate temporal components to better predict dynamic processes like soil erosion, rockfalls, and landslides.
