جست‌وجوی استاد و پژوهشگر

Yiannis Ampatzidisمشاهده پروفایل

دانشیار

• Precision Agriculture
• Unmanned Aerial Vehicles (UAVs)
• Artificial Intelligence (AI) in Agriculture
• +۷ مورد دیگر

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.

Vasit Saganمشاهده پروفایل

استاد

• Geospatial Science
• Remote Sensing
• Photogrammetry
• +۴ مورد دیگر

Vasit Sagan is a Professor of Geospatial Science and Computer Science at Saint Louis University's School of Science and Engineering . He directs the Remote Sensing Lab , serves as Deputy Director of the Taylor Geospatial Institute , and holds the role of Associate Vice President for Geospatial Science in the Office of the Vice President for Research and Partnership. Education: Ph.D. from Peking University (2006) Leadership: Director of Remote Sensing Lab; Deputy Director, Taylor Geospatial Institute Research Interests center on geospatial computer vision , integrating remote sensing, photogrammetry, machine learning/AI, and imagery analysis. His work addresses critical challenges in food and water security , ecosystem monitoring , and social instability at scales ranging from local to global. He has secured over $50M in grants as PI/Co-PI and authored 150+ peer-reviewed publications. Recent Publications highlight his interdisciplinary approach, with studies on crop yield prediction via satellite/UAV imagery, disease detection in wheat, urban tree species classification, and deep learning applications for water quality monitoring. These works span agricultural technology , environmental science , and security informatics , emphasizing data fusion and AI-driven geospatial analysis. Scientific Awards include: 2021 Best Paper Award (Remote Sensing) Best Paper Award (International Archives of Photogrammetry) Advising and Grants : He has mentored numerous doctoral, master's, and postdoc researchers, and led major funded projects focused on geospatial AI and environmental sustainability. Labs and Facilities : Remote Sensing Lab at Saint Louis University and collaborative work with the Taylor Geospatial Institute.

Baihua Liمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۹ مورد دیگر

Professor Baihua Li is a Professor of Computer Vision and Machine Learning in the Department of Computer Science at Loughborough University. She is a key member of both the Centre for Sensing and Imaging Science and the Vision, AI, Autonomous and Human Centred Systems Research Group. With over 15 years of research experience, her work spans multiple disciplines including healthcare, sports science, environmental protection, and food manufacturing. Her educational background includes a BSc and MSc in Electronic Engineering from Tianjin University, China (one of China's top 15 universities), followed by a PhD in Computer Science from Aberystwyth University, UK. Prior to joining Loughborough University in 2015, she served as a Lecturer and later Senior Lecturer at Manchester Metropolitan University. Professor Li's research focuses on developing novel AI and machine learning technologies with applications across diverse sectors. Her primary expertise lies in computer vision, pattern recognition, and signal/image processing. She has made significant contributions to human motion tracking and activity recognition, which have been applied in sports performance analysis, healthcare diagnostics, and robotics. Her work emphasizes practical applications that solve real-world problems, from improving football analytics to developing sandwich-making robots. Her research team has developed AI systems for air pollution prediction, upper-limb prosthesis control, autism spectrum condition diagnosis in adults, and retinal disease identification. She has also led projects involving intelligent drone logistics for medical supplies and agricultural robots for precision crop spraying. MSDUK Innovation Challenge Award under category Industry 4.0 in 2019 Made in the Midlands 2023 Food and Drink Award for the Millitec KTP project Professor Li has secured over £1.4 million in external funding in the last three years, with ongoing grants exceeding £1.2 million. She leads a research team comprising 6 Postdoctoral Research Associates and 7+ PhD students. Her projects include AI for sleep breathing disorder diagnosis (£540k), food manufacturing automation (£204k), football action event detection (£359k), air quality monitoring (£470k), and agricultural robotics (£500k). She serves as the Impact Coordinator for her department, emphasizing the transfer of research to industry for societal and economic benefits. She leads an active research team within the Centre for Sensing and Imaging Science and the Vision, AI, Autonomous and Human Centred Systems Research Group. Her laboratory work spans multiple application domains including sports analytics with Statmetrix, food manufacturing automation with Millitec, environmental monitoring through the Triple-Network Air Quality project, and agricultural robotics for precision farming. Her team has developed the i-Gene delta robot that produces 750,000 sandwiches daily for a UK factory, addressing industry skills shortages.

• Agronomy
• Sustainable Agriculture
• Crop Production
• +۵ مورد دیگر

Reinhard Neugschwandtner is an Associate Professor at the Institute of Agronomy , University of Natural Resources and Life Sciences, Vienna . His research focuses on sustainable crop production, soil science, and precision agriculture in Pannonian climates. Key projects: Agri-photovoltaic systems, digitalization in agriculture, drought-tolerant legumes, autonomous robotics in crop technology Leadership roles: Project leader in 8+ research initiatives since 2014 Research Interests : Dr. Neugschwandtner specializes in: Soil health and earthworm ecology under different tillage systems Nitrogen dynamics in legume-cereal intercropping systems Digital tools for canopy parameter estimation and precision farming Life cycle assessment of agricultural practices Climate-smart crop management strategies Scientific Awards : Awardee of five prestigious honors including: Kardinal-Innitzer-Förderungspreis (2017) Klaus Fischer Innovationspreis für Technik und Umwelt (2016) Walter-Kubiena-Preis (2008) BISi Award (2003) Publications & Presentations : Over 171 publications and 102 presentations focusing on: Long-term tillage experiments Winter crop adaptation Intercropping efficiency Digital agriculture tools Soil nutrient dynamics

Javier Rodriguez Sanchezمشاهده پروفایل

پژوهشگر ارشد

• Plant Breeding
• Genetics
• Genomics
• +۵ مورد دیگر

Javier Rodriguez Sanchez is a Postdoctoral Associate at the Institute of Plant Breeding, Genetics and Genomics (IPBGG) within the Department of Crop & Soil Sciences at the University of Georgia's College of Agricultural and Environmental Sciences (CAES), based at the Tifton Campus under Dr. Nino Brown's mentorship. His research integrates advanced technologies with plant science to revolutionize agricultural practices, specializing in: Autonomous field phenotyping systems using robotics and terrestrial laser scanning Deep learning applications for 3D/4D crop trait extraction and yield estimation Genetic improvement of cotton and peanut through seedling vigor and morphological trait analysis Spatiotemporal data fusion for real-time crop monitoring and precision agriculture Publications from 2017-2025 reveal a clear trajectory from horticultural robotization toward sophisticated AI-driven phenotyping platforms, with cotton and peanut as primary models. His work consistently bridges computer vision, LiDAR, and machine learning to solve breeding challenges, demonstrating increasing methodological complexity in data acquisition and analysis. No scientific awards were documented in the source materials. As a postdoctoral researcher, Dr. Sanchez operates within Dr. Nino Brown's mentorship structure with no reported advisees or independent grant leadership. His collaborative framework focuses on technology deployment rather than traditional academic advising. He contributes to the IPBGG's mission through the Tifton Campus research facility, which features specialized laboratories and field sites for crop genomics and phenomics. This environment supports his development of mobile robotic platforms for in-field data collection, positioning him at the intersection of agricultural engineering and plant breeding innovation.

Gabriel Dallagoمشاهده پروفایل

استادیار

• Animal Welfare
• Machine Learning Applications
• Livestock Production Systems
• +۳ مورد دیگر

Gabriel Dallago serves as Assistant Professor in the Department of Animal Science within the Faculty of Agricultural and Food Sciences at the University of Manitoba. His research integrates advanced data analytics with livestock production systems to enhance animal welfare and farm decision-making processes. His educational background includes: PhD from McGill University, Canada MSc from Federal University of the Vales of Jequitinhonha and Mucuri, Brazil BSc from Federal University of the Vales of Jequitinhonha and Mucuri, Brazil Dallago's research program centers on machine learning and deep learning applications for livestock management. Key initiatives include developing predictive models for animal bio-responses, integrating multimodal data streams to analyze complex farm systems, and optimizing dairy cow longevity through data-driven interventions. His work bridges computational science with practical agricultural challenges, focusing on tangible improvements in production efficiency and animal well-being. Precision monitoring of dairy cow behavior and welfare Early-life management impacts on herd productivity Computer vision applications for livestock assessment Economic modeling of livestock production systems Analysis of his 15 most recent publications (2022-2025) reveals strong thematic concentration in dairy science (47%), swine production (20%), and animal nutrition (20%), with consistent application of computational methods. Notable trends include increasing use of machine learning for welfare assessment (evident in 60% of recent works), growing emphasis on economic sustainability metrics, and innovative sensor integration for real-time livestock monitoring. Dallago teaches ANSC 7500 (Methodology in Agricultural and Food Sciences) and AGRI 4100 (Current Issues in Agricultural Systems), though specific advising relationships and grant details remain unreported in available materials. His research appears to operate through interdisciplinary collaborations leveraging computational tools and on-farm data collection systems, though dedicated laboratory descriptions are absent from current documentation.

Jennifer Lachowiecمشاهده پروفایل

دانشیار

• genetic interactions
• gene regulatory networks
• plant robustness
• +۷ مورد دیگر

Jennifer Lachowiec serves as Associate Professor in the Department of Plant Sciences and Plant Pathology at Montana State University's College of Agriculture. Her research program focuses on understanding genetic robustness in plants through interdisciplinary approaches spanning quantitative genetics, genomics, and molecular biology. She teaches foundational courses in biometry and research design while leading a technology-driven laboratory that utilizes unoccupied aerial systems for large-scale phenotyping. Educational background: Ph.D. in Molecular and Cellular Biology, University of Washington (2014) B.S. in Genetics and Anthropology, University of Wisconsin - Madison (2008) Her research centers on predicting phenotypes from genetic sequences with emphasis on plant robustness—the capacity to maintain phenotypes despite genetic mutations and environmental fluctuations. The Lachowiec Lab integrates quantitative genetics, molecular biology, and evolutionary approaches to study gene regulatory networks and genetic interactions. A key innovation involves deploying UAVs and automated imaging systems to quantify robustness across plant populations, enabling high-throughput analysis of crop resiliency traits. This work bridges fundamental genetic mechanisms with practical agricultural applications for developing climate-resilient crops. Recent publications (2022-2025) reveal consistent focus on UAV-based phenotyping, polyploid genomics, and crop stress adaptation. Key trends include development of robust phenomic databases, genetic dissection of barley malting traits, herbicide resistance mechanisms in aquatic weeds, and metabolic responses to environmental stress. The work demonstrates strong translational impact through USDA-funded projects addressing nitrogen-efficient camelina production and heat stress tolerance in cereal crops. Scientific recognition includes: Nominated for USDA Teaching Award (2023) Phi Kappa Phi Anna K. Fridley Award (2023) College of Agriculture Teaching Award of Merit (2023) Transformative Teaching Award (2022) Open Education Resources Adoption Award (2021) Research is supported by multiple USDA NIFA and DOE grants including 'Enhancing Camelina Oilseed Production with Minimum Nitrogen Fertilization,' 'Uncovering developmental mechanisms to sustain grain number during heat stress,' and 'Genetic controls for crop microbiome recruitment.' Dr. Lachowiec serves on the North American Plant Phenotyping Network DEI Committee and has organized outreach events including MSU Family Science Day. Her laboratory, located in the Plant Bioscience Building on traditional Amskapii Pikani, Apsaalooke, Tsétsêhéstâhese, and Seliš lands, maintains specialized facilities for UAV-based phenotyping and growth room imaging systems to advance plant robustness research.

• Plant Phenotyping
• Precision Agriculture
• Machine Vision
• +۶ مورد دیگر

Dr. Huajian Liu is a Grant-Funded Researcher (B) at the University of Adelaide's School of Agriculture, Food and Wine, within the Faculty of Sciences, Engineering and Technology. He is affiliated with The Plant Accelerator, the Adelaide node of the Australian Plant Phenomics Facility. His research focuses on machine vision and machine learning for plant phenotyping and precision agriculture, particularly using hyperspectral imaging for plant nutrient estimation, disease detection, stress tolerance analysis, and pest monitoring. Dr. Liu holds a PhD in machine vision for invertebrate detection from the University of South Australia (2018) and has been a post-doctoral researcher at The Plant Accelerator since then. His expertise spans hyperspectral imaging, bio-inspired machine vision systems, 3D model reconstruction, and optical sensing system design. He is eligible to supervise Masters and PhD students in plant phenotyping and precision agriculture. Key research areas include crop stress detection (drought, salt, nutrient deficiencies), invertebrate pest identification, and automation in agricultural monitoring. Affiliations: The Plant Accelerator, School of Agriculture, Food and Wine Research Themes: Plant phenotyping, precision agriculture, machine learning applications Grants: Ongoing grant-funded research in plant phenomics and hyperspectral technologies Publications highlight innovations in hyperspectral imaging for crop yield prediction, disease diagnostics, and nitrogen content analysis. His work bridges computational methods with agricultural challenges, emphasizing sustainable farming solutions.

George Chenمشاهده پروفایل

استادیار

• Machine Learning
• Healthcare
• Information Systems
• +۳ مورد دیگر

George Chen is an Assistant Professor of Information Systems at Carnegie Mellon University's Heinz College and an affiliated faculty member of the Machine Learning Department. His research focuses on machine learning applications in healthcare and developing countries, particularly in time series analysis and forecasting. He holds a PhD in Electrical Engineering and Computer Science from MIT, where he received the George Sprowls Award for his thesis on nonparametric methods. He also advises the AgriTech startup CoolCrop, providing farmers in India with market forecasts and cold storage solutions. Education: PhD, Electrical Engineering and Computer Science, MIT (2015) SM, Electrical Engineering and Computer Science, MIT (2012) BS, Electrical Engineering and Computer Sciences & Engineering Mathematics and Statistics, UC Berkeley (2010) Research: Chen develops nonparametric machine learning methods for healthcare (e.g., predicting patient outcomes) and agricultural forecasting (e.g., crop pricing for farmers). His work emphasizes interpretable models and statistical guarantees. He recently authored a monograph on deep learning for survival analysis and co-organized the 2023 AAAI Survival Analysis Symposium. Awards: NSF Fellowship, NDSEG Fellowship, Siebel Scholarship (PhD) MIT Goodwin Medal (2015, top teaching award) George Sprowls Award for Best Computer Science Thesis (MIT) Teaching: Teaches courses on unstructured data analytics at CMU, including graduate-level machine learning and time series analysis. Previously taught at MIT and UC Berkeley, winning teaching awards at both institutions. Professional Service: Area chair for ICML, NeurIPS, and MLHC (2025). Active in organizing conferences and workshops related to survival analysis and healthcare ML. Labs/Teams: Leads research collaborations in healthcare analytics and developing-world technology through the Heinz College and CMU's Machine Learning Department.

Markus Richterمشاهده پروفایل

استاد

• Ornamental plant cultivation
• Technology in horticulture
• Irrigation systems
• +۴ مورد دیگر

Markus Richter serves as Professor of Horticultural Plant Production and Production Technology at the Berlin University of Applied Sciences (BHT), within Department V - Life Sciences and Technology. His academic career combines extensive industry experience with scholarly research, focusing on the practical application of botanical knowledge to solve horticultural challenges. Since joining BHT in 2008, he has established himself as a specialist in ornamental plant cultivation technology and precision irrigation systems. Prof. Richter earned his foundational education as a Horticultural Engineer from the Technical University of Applied Sciences Berlin (1987-1990), followed by an MSc in Technology of Crop Protection from the University of Reading (1990-1991). He completed his doctorate (Dr. rer. hort) at Humboldt University of Berlin between 1996 and 2001. Prior to his academic appointment, he gained valuable industry experience as a test engineer at the Chamber of Agriculture Westphalia-Lippe (1991-2003), and later as Head of the Horticultural Research Center in Münster-Wolbeck (2003-2006) and Head of ornamental plant trials at GBZ Straelen/Cologne-Auweiler (2006-2007). His research interests center on ornamental plant cultivation , technology in horticulture , and irrigation systems , with a particular emphasis on developing sensor-based solutions for resource-efficient plant production. Prof. Richter has pioneered work in photogrammetric monitoring systems for irrigation management, with applications spanning both greenhouse and field production environments. His expertise bridges plant physiology, engineering, and practical horticulture, addressing critical challenges in water conservation and plant health monitoring. Analysis of his recent publications reveals a dominant focus on the PLANTSENS research initiative, which has evolved through multiple phases (2017-2023). This work demonstrates a consistent trajectory toward increasingly sophisticated multi-sensor systems for detecting plant water stress and automating irrigation. His research spans agricultural engineering, plant physiology, and computer vision, with applications in both ornamental and vegetable production systems. A secondary research thread examines nutrient management issues in specialty crops like Helleborus and Hydrangea. At BHT, Prof. Richter actively supervises student theses on topics including sensor systems for irrigation management, growth control of ornamental plants, and sustainable production methods. His research has been supported through multiple projects, including the PLANTSENS project (2017-2020), PlantSens II (2020-2023), and ongoing doctoral research on Helleborus cultivation. He serves on the Training Commission and Audit Committee, and acts as an academic advisor and officer for recognition of academic achievements.

Vitor S. Martinsمشاهده پروفایل

استادیار

• Remote Sensing
• Water Quality
• Deep Learning
• +۵ مورد دیگر

Dr. Vitor S. Martins is an Assistant Professor in the Department of Agricultural and Biological Engineering at Mississippi State University (MSU). He leads the Geospatial Computing for Environmental Research (GCER) Lab , focusing on satellite remote sensing, deep learning, and environmental monitoring. His research integrates Earth observation data with AI to advance digital agriculture, water resource management, and climate resilience. Education: Ph.D., Agricultural and Biosystems Engineering, Iowa State University M.Sc., Remote Sensing, Brazilian Institute for Space Research (INPE) B.Sc., Agriculture and Environmental Engineering, Federal University of Viçosa Research Interests: Remote sensing of water quality and pollution Land cover/land use change mapping Satellite and aerial image processing Soil & water conservation engineering Deep learning for geospatial data analysis Climate change impacts on environmental systems Recent Research Trends (Articles): His work emphasizes algal bloom monitoring , crop type mapping , and global water transparency using advanced satellite platforms like Sentinel-2/3 and Landsat. He also develops frameworks for burned area mapping and soil spectral analysis , leveraging cloud computing and AI-driven models. Awards & Honors: 2019 Reverent P.T. Taiganides Award (Outstanding Ph.D. Student) 2019 Harold & Katherine Guy Fellowship (Soil & Water Conservation Research) 2012 CAPES-FIPSE International Exchange Award Lab & Collaborations: The GCER Lab collaborates with multiple institutions to promote AI solutions for agriculture and water management. Projects include developing AlgaeMap for Latin American water quality monitoring and creating scalable frameworks like FieldSeg for agricultural field extraction.

Johnny Liمشاهده پروفایل

استادیار

• Precision Agriculture
• IoT Sensors
• Autonomous Robotics
• +۵ مورد دیگر

Johnny Li is an Assistant Professor in the Department of Soil and Water Systems at the University of Idaho, affiliated with the College of Agricultural & Life Sciences. He leads the Precision Agriculture and Intelligent Robotics Laboratory (PAIR) and is an affiliate professor at the Center for Intelligent Industry Robot (CI2R). Education: PhD in Mechatronics (Central South University, 2012), M.S. in Material Science (Central South University, 2005), B.S. in Agricultural Engineering (Hunan Agricultural University, 2002). Research focuses on IoT/robotics sensing, control systems, and AI integration for precision agriculture, environmental management, and infrastructure monitoring. Key areas include autonomous robotics, remote sensing, crop modeling, and explainable AI for decision support systems. Publications emphasize machine learning applications in disease detection (citrus, tomatoes, grapes), wildfire monitoring, and infrastructure health assessment. Over 40 peer-reviewed papers and $1.5M in grants secured since 2019. Awards include NSF CGCA Panel Fellow (2022) and editorial role at Agricultural Engineering International: CIGR Journal (2013). Labs/Teams: PAIR Lab (founded 2022), CI2R collaboration, interdisciplinary partnerships with soil scientists, engineers, and computer scientists.

Gea Rahmanمشاهده پروفایل

مدرس

• Data Science
• Machine Learning
• Ensemble Learning
• +۷ مورد دیگر

Dr. Gea Rahman is a Lecturer in Computing at Charles Sturt University (CSU), specializing in data science and machine learning. He holds a PhD in Computer Science from CSU, and MSc/BSc degrees from Rajshahi University, Bangladesh, where he was awarded a Gold Medal for academic excellence. With over 20 years of teaching experience, he previously served as Professor and Programme Director at Bangladesh Agricultural University. Education: PhD in Computer Science (Data Science/ML), Charles Sturt University (2011-2015) MSc in Computer Science & Engineering, Rajshahi University (2002-2003) BSc (Hons) in Computer Science & Technology, Rajshahi University (1998-2002) Research Interests: Data science applications in agriculture, healthcare, and environmental monitoring Machine learning techniques including ensemble/deep learning, transfer learning, and incremental learning Data preprocessing methods (missing value imputation, outlier analysis) AI ethics and healthcare consent strategies Awards: Global Research Impact Recognition Award 2020 Best Researcher Award Gold Medal for Academic Excellence (2003) Advising & Grants: Principal supervisor for multiple postgraduate students Recipient of grants including: Ai-Enabled Segmentation of Brain MRI (2024) Adaptive Federated Learning Framework (2024) Unusual Behaviour Detection in Aged Care (2023) He is part of the Data Mining Research Group (DaMRG) and actively contributes to conferences/journals as a reviewer and editorial board member.

Fatemehsadat Tabeiمشاهده پروفایل

استادیار

• Machine Learning
• Smart Sensors
• IoT
• +۳ مورد دیگر

Dr. Fatemehsadat (Azi) Tabei is an Assistant Professor of Electrical Engineering/Computer Science at the College of Engineering, West Texas A&M University. She holds a B.S. and M.S. in Electrical Engineering from Shiraz University (2009, 2014) and a second M.S. and Ph.D. from Texas Tech University (2020, 2021). Her research focuses on IoT-enabled healthcare solutions, biomedical signal/image processing, and smartphone-based diagnostics for conditions like keratoconus, arrhythmia, and periodontal disease. She is a co-investigator on two U.S. patents for eye disease detection tools and has authored/co-authored numerous publications in mHealth and smart sensor technologies. Dr. Tabei's academic journey includes teaching courses like Introduction to Computer Science and Python programming. She is an active member of IEEE and IEEE Women in Engineering. Her research emphasizes applying IoT and machine learning to healthcare, including remote disease monitoring via smartphones and agricultural IoT for precision farming. Recent work includes personalized arrhythmia detection and AI-driven tomato farming optimization. Dr. Tabei collaborates on lab initiatives within the College of Engineering, advancing human-machine teaming and immersive technologies. Her future work aims to expand smartphone-based diagnostics for broader healthcare accessibility.

Justine E. Vanden Heuvelمشاهده پروفایل

استاد

• Sustainable viticulture production
• Ecophysiological factors affecting fruit and wine composition
• Computational tools for vineyard management
• +۵ مورد دیگر

Justine E. Vanden Heuvel is a Professor in the Horticulture Section at Cornell University's School of Integrative Plant Science . Her work bridges viticulture research and undergraduate education . Education: Doctorate (2002) from the University of Guelph M.Sc. in Plant Agriculture (1999) from the University of Guelph B.Sc. (1996) from the University of Guelph Research Interests: Focus on digital viticulture for precision management, flavor/aroma optimization in wine grapes, and ecophysiological factors affecting fruit-wine composition. Implements computational tools like NDVI imaging and computer vision for sustainable practices in cool climates. Publication Trends: Research spans soil-plant interactions , climate adaptation , and vineyard technology . Key areas include under-vine vegetation for environmental sustainability, microbial composition studies , and canopy management to improve grape quality. Outreach Activities: Actively engages through @thegrapeprof YouTube content and media contributions to outlets like the New York Times Well blog and HuffPost Science . Teaches courses such as The Science of Grapevines (VIEN 3300) , Advanced Viticulture (VIEN 4460) , and Wine Culture (VIEN 2810) . Contact: G14 Plant Science Building, Ithaca, NY 14853