جستجوی استاد راهنمای دانشگاهی

Dr. Sierra Young is an Assistant Professor in the Department of Civil and Environmental Engineering and the Utah Water Research Laboratory at Utah State University, where she leads the DAISy (Digital Agro-environment and Intelligent Systems) Lab. Her research integrates robotics, computer vision, and environmental sensing to advance monitoring in agriculture and hydrology. PhD, Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 2018 MS, Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 2015 BS, Civil and Environmental Engineering, Cornell University, 2014 Dr. Young's research focuses on field robotics, automation, and optical sensing systems for environmental and agricultural applications. She specializes in unmanned aerial systems (UAS), developing robotic payloads for tasks such as aerial pollination, soil moisture measurement, and water quality sampling. Her work also emphasizes hyperspectral imaging and machine learning for non-destructive evaluation of crops like industrial hemp and loblolly pine, enabling high-throughput phenotyping and disease detection. Her recent publications demonstrate a strong trend in intelligent robotics for agriculture, computer vision for environmental monitoring, and sensor fusion for hydrological applications. Key themes include autonomous decision-making in UAS, hyperspectral analysis for plant health, and real-time data processing for operational field deployment. NSF CAREER Award, 2024 Outstanding Reviewer, Journal of Sustainable Water in the Built Environment, 2024 Educational Aids Blue Ribbon Award, ASABE, 2024 ASABE Outstanding Reviewer, 2023 Dr. Young mentors graduate students in civil, environmental, and electrical engineering, guiding research in robotics, sensing, and data science. She has secured funding from agencies including the U.S. Geological Survey and the National Robotics Initiative to support projects on camera-based hydrologic monitoring and autonomous water sampling. Her teaching includes courses in computer programming and computer vision for engineers. The DAISy Lab fosters interdisciplinary collaboration, particularly with NC State University, focusing on scalable robotic solutions for agricultural and environmental challenges. The DAISy Lab is actively developing mobile sensor systems for applications in precision agriculture, hydrology, and aquaculture. Current projects include autonomous water quality monitoring using aerial and surface vehicles, hyperspectral imaging for crop breeding, and low-cost camera networks for operational hydrology.

• Field Robotics
• Unmanned Aerial Systems (UAS)
• Environmental Sensing
• +۷ مورد دیگر

Prof. Adriano Mancini is an Associate Professor at the Department of Information Engineering, University of Marche Polytechnic (UNIVPM), Italy. His research focuses on AI-driven solutions in computer vision, remote sensing, and IoT applications. He leads projects in precision agriculture, environmental monitoring, and smart retail systems, leveraging deep learning and edge computing technologies. Current work includes neural rendering for fashion design, vegetation recovery assessment via drones, and AI-based predictive maintenance for infrastructure. His research integrates interdisciplinary approaches across computer science, engineering, and environmental sciences. Notable projects involve UAV-based vegetation mapping, wastewater sensor forecasting, and ethical frameworks for AI in remote sensing. He collaborates on EU-funded initiatives like 4IPLAY for infrastructure inspection and WATERBALANCE for water resource management. Prof. Mancini's lab develops real-world applications such as automated mussel farming monitoring, retail robot navigation, and blockchain-integrated quality assurance systems. He advises on AI ethics and standards for maritime sustainability and low-carbon operations. His work bridges academic innovation with industrial needs through collaborations with industries like plastics manufacturing and smart retail environments.

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Dr. Mark Lee is a Senior Lecturer in Global Health at the Department of Health Studies, Royal Holloway, University of London. He serves as Co-Director of the Sustainable Agricultural Systems (CSAS) initiative and Course Leader for the MSc Global Health: Food Security, Sustainability and Biodiversity program. He also chairs the Department Examination Board. PhD: Imperial College London (Air pollution and climate change effects on grassland ecosystems) MSc: Imperial College London (Ecology, Evolution and Conservation) BSc: University of Bristol (Biology) His research focuses on nature-based solutions (NBS) integrating emerging technologies like robotics, AI, and drones to address climate change, food security, and biodiversity loss. Key projects include: Nature Returns: A UK government-funded initiative to optimize land use for carbon sequestration and biodiversity while ensuring food production. N2Vision+: Developing robotic systems for automated soil nitrogen monitoring to reduce fertilizer overuse and emissions. He has led 7 active/funded projects and contributed to 24 research outputs. His work aligns with UN Sustainable Development Goals (SDGs) targeting environmental sustainability and food security. Recent activities include keynote talks on greenhouse gas flux measurement, workshops on policy interventions, and farm-based research demonstrations.

• Agriculture
• Agritech
• Biodiversity
• +۷ مورد دیگر

Dr. Jake Bicknell is a Senior Lecturer in Biodiversity Conservation at the University of Kent, affiliated with the Durrell Institute of Conservation and Ecology (DICE). His research focuses on tropical forest conservation, including responses to logging, mining, climate change, and habitat fragmentation. He emphasizes degraded forest restoration, biodiversity-carbon linkages, and policy-driven conservation strategies. He teaches courses on conservation science, field methods, and data analysis for ecologists. Dr. Bicknell advises multiple PhD students researching topics like gold mining impacts, UK rewilding, and primate conservation in Kenya. His work integrates machine learning for environmental monitoring and advocates for community-led tropical forest protection. Notable contributions include demonstrating the ecological benefits of reduced-impact logging and riparian buffers in oil palm landscapes. Publications span 20+ years, with recent emphasis on robotics in biodiversity monitoring, conservation policy efficacy, and tropical land-use trade-offs. He actively engages with policymakers and the public through talks and media commentary on sustainable resource management.

• Biodiversity Conservation
• Tropical Forest Ecology
• Environmental Policy
• +۵ مورد دیگر

Max Cichocki is a Researcher at the Institute of Logistics Engineering, Vienna University of Technology, specializing in Mobility & Production. His work integrates engineering principles with sustainable industrial solutions, particularly in biomass and forestry logistics. Education includes: Dipl.-Ing. (BSc equivalent) Dr.techn. (PhD in technical sciences) Research focuses on: Model-Based Systems Engineering for industrial environments Industry 4.0 applications in composting and forestry Autonomous machinery development for sustainable waste processing Virtual engineering and hyperconnected logistics platforms Recent publications reveal a cohesive trend toward smart industrial systems, emphasizing physical internet principles and digital twins to reduce complexity in heavy machinery operations. His work bridges mechanical engineering with environmental sustainability across composting, bioenergy, and autonomous forestry sectors. Dr. Cichocki actively disseminates findings through conference presentations, including talks on Kompostwender 4.0 and advanced gripper systems at international forums like the Internationale Kranfachtagung.

• Model-Based Systems Engineering
• Industry 4.0
• Sustainable Logistics
• +۳ مورد دیگر

Dr. Christoph Kehl is a Researcher at the Karlsruhe Institute of Technology (KIT), working with the Institute for Technology Assessment and Systems Analysis (ITAS) and the Office for Technology Assessment at the German Bundestag (TAB). He holds a Diplom in Environmental Sciences (ETH Zurich) and a Magister Artium in Philosophy (Freie Universität Berlin), with a doctorate in Biomedical Memory Research (Humboldt University Berlin). His work spans technology assessment in bio- and neurotechnologies, robotics/AI, and environmental sustainability. Education: Environmental Sciences (ETH Zurich), Philosophy (Freie Universität Berlin), Doctorate in Biomedicine (Humboldt University Berlin) Research focuses on human-machine interaction, ethical technology design, climate-friendly policies, and digitalization impacts across agriculture, construction, and healthcare. Recent articles address forest conversion under climate change, recycling strategies, and generative AI in policy advice. His work emphasizes interdisciplinary collaboration and societal preconditions for sustainable innovation. Professional roles include contributions to parliamentary advisory projects, participation in European technology assessment conferences, and editorial work. No explicit awards or student advising details are noted in the provided texts. Current projects involve foresight for critical infrastructure systems and climate-safe shipping concepts.

• Technology Assessment
• Robotics
• Artificial Intelligence
• +۴ مورد دیگر

Davut KARAYEL is a Professor at Akdeniz University, Faculty of Agriculture, Department of Agricultural Machinery and Technologies Engineering. He also holds a remote Chief Researcher position at Vytautas Magnus University, Lithuania. Education: BSc in Agricultural Engineering (Cukurova University, 1995), MSc and PhD in Agricultural Engineering (Akdeniz University, 2005) Editorial Roles: Associate Editor of Journal of Computers and Electronics in Agriculture , Editorial Board Member of Turkish Journal of Agriculture and Forestry Metrics: 169 WoS publications, 408 h-index (WoS), 1012 h-index (Scopus), 1961 h-index (Scholar) His research focuses on Agricultural Engineering , particularly technological processes of sowing and crop care machines and environmental impact assessment of agricultural operations . Key areas include soil-machine interaction , precision seeding systems , biomass processing , and sustainable agricultural technologies . The 15 most recent articles highlight advancements in pelletization (bioenergy from crop residues), AI-optimized planting systems , soil crust mitigation , and sensor-based precision agriculture . These works bridge mechanical engineering and agroecology , addressing challenges in resource efficiency and environmental sustainability . He has supervised 3 theses and authored 2 international book chapters. With over 100 WoS-indexed publications and 27 Scopus publications, his work emphasizes machine design , combustion properties , and agricultural mechanization .

• Agricultural Engineering
• Precision Agriculture
• Soil Mechanics
• +۳ مورد دیگر

Litao Yu is a Part-Time Lecturer and Visiting Scholar at the Faculty of Engineering and Information Technology, University of Technology Sydney. Concurrently, he serves as a Senior Data Analyst at Australia's Department of Agriculture, Fishery and Forestry. His academic appointments include Research Fellow at UTS (2019-2024) and post-doctoral positions at Griffith University and Queensland University of Technology. Education: PhD from The University of Queensland (2013-2016) MPhil from Dalian University of Technology (2009-2012) BSc from Dalian Maritime University (2003-2007) Dr. Yu's research focuses on computer vision and machine learning with applications in agriculture, multimedia systems, and multimodal learning. His work spans few-shot learning, semantic segmentation, fine-grained recognition, and multimodal fusion techniques. Key application domains include animal welfare monitoring, aquaculture quality control, and travel information enhancement. His publications demonstrate strong thematic convergence around efficient visual recognition systems , with recurring focus on few-shot learning paradigms and attention mechanisms. Recent work shows increasing emphasis on agricultural applications of computer vision, including poultry monitoring, fish processing automation, and sheep tracking. Blockchain integration for supply chain transparency represents another emerging theme. No scientific awards are mentioned in available records. Teaching responsibilities at UTS include courses on Real-time Operating Systems and Shell Programming . No information is available regarding research grants, supervised students, or laboratory affiliations.

• Computer Vision
• Multimedia Computation
• Machine Learning
• +۲ مورد دیگر

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.

• UAV photogrammetry
• Remote sensing
• Geomorphological monitoring
• +۷ مورد دیگر