Find Academic Supervisors

Arti SinghView profile

Assistant Professor

• Plant breeding
• Soybean diseases
• Genomics
• +3 more

Arti Singh is an Assistant Professor in the Department of Agronomy at Iowa State University. Her research focuses on plant breeding, soybean diseases, genomics, and phenomics, with a strong emphasis on integrating artificial intelligence and high-throughput technologies into agricultural systems. She leads projects involving AI-driven disease identification, precision agriculture, and crop improvement strategies. Her expertise includes developing machine learning models for real-time weed and insect classification (e.g., WeedNet and InsectNet), deploying drones and ground robots for crop phenotyping, and leveraging genomic data to map traits like flowering time and disease resistance in legumes. Singh collaborates on initiatives like the AIIRA Institute for Resilient Agriculture and the BioTrove biodiversity dataset. Singh’s work spans plant stress phenotyping, digital twin technologies for plant sciences, and multi-sensor phenotyping for early disease detection. Her research bridges computational methods with traditional agronomy, aiming to enhance crop resilience and sustainability in the face of environmental challenges. Her recent projects include optimizing robotic navigation for precision agriculture, improving soybean yield estimation via video analysis, and dissecting genetic architectures of traits in mungbean and soybean using GWAS and genomic tools. She actively contributes to conferences and publishes in high-impact journals, advancing both foundational and applied aspects of agricultural science.

Jiaxuan YouView profile

Assistant Professor

• Graph Neural Networks
• Relational Data
• Machine Learning Systems
• +9 more

Jiaxuan You is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign, leading the U Lab focused on achieving Artificial General Intelligence (AGI) in digital environments. His research spans graph neural networks (GNNs), relational data, foundation models, and machine learning systems. PhD and MS in Computer Science from Stanford University (2021) Developed GraphGym and PyTorch Geometric (PyG) for graph learning Core member at Kumo AI (2021-2023) His research explores: Graph-enhanced LLMs: Integrating relational structures into foundation models AGI Development: Self-optimizing AI agents and tool utilization ML Systems: Scalable architectures and redundancy-free computation Interdisciplinary Applications: Financial networks, crop yield prediction, and metro systems Recent publications focus on temporal reasoning, multi-agent dynamics, and hybrid architectures for LLMs. He actively develops open-source tools like DBGYM and GraphRouter. Scientific recognition includes: JPMC PhD Fellowship Baidu Scholarship Best Student Paper at AAAI 2017 World Bank Big Data Innovation Challenge winner He mentors PhD and intern students, emphasizing machine learning systems expertise. His lab collaborates on AGI workshops (e.g., ICLR 2024) and industry projects.

Yin BaoView profile

Assistant Professor

• Robotics and Controls
• Agricultural Automation
• Precision Farming
• +4 more

Yin Bao is an Assistant Professor in Plant and Soil Sciences and Mechanical Engineering at the University of Delaware since 2023, previously holding the same position at Auburn University's Department of Biosystems Engineering (2019-2023). He holds a BE in Mechanical Engineering from China Agricultural University (2012) and a PhD in Agricultural and Biosystems Engineering from Iowa State University (2018), followed by postdoctoral research there until 2019. His research focuses on automation technology for agriculture and forestry, leveraging robotics, machine learning, and sensing systems to develop tools for precision farming and plant phenotyping. Key areas include unmanned systems (UGVs/UAVs), spectral imaging, and AI-driven predictive models for crop and livestock management. Recent work emphasizes automated inventory systems for forest nurseries, UAV-based vegetation assessment, and machine learning applications in crop yield prediction. His publications span robotic guidance systems, root segmentation in X-ray CT scans, and equine gait analysis using deep learning. Notable projects include the Robotic Assay for Drought (RoAD) system and the 'smart canopy' sorghum initiative. Collaborative efforts involve integrating multifrequency microwave sensing and electronic nose technologies for crop quality analysis.

Xiaowei JiaView profile

Assistant Professor

• Machine Learning
• Data Mining
• Physics-Guided Neural Networks
• +5 more

Xiaowei Jia is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh. He holds a Ph.D. from the University of Minnesota (supervised by Prof. Vipin Kumar) and B.S./M.S. degrees from the University of Science and Technology of China (USTC) and SUNY Buffalo. His research focuses on integrating scientific theory with machine learning to address societal and environmental challenges, such as climate modeling, hydrology, and fairness in AI. Education: Ph.D., University of Minnesota (2020) M.S., State University of New York at Buffalo B.S., University of Science and Technology of China (USTC) Research Interests: Knowledge-Guided Machine Learning Spatiotemporal Data Mining Fairness in AI for Social Good Applications in Environmental Science and Healthcare Publications showcase his work on physics-integrated neural networks, spatiotemporal modeling (e.g., water temperature prediction), and fairness-aware algorithms. His work has been recognized with Best Paper awards at SIAM SDM (2022, 2023). Awards include the Best Applied Data Science Paper Award at SIAM SDM in 2022 and 2023. He teaches advanced machine learning courses, emphasizing theory integration with real-world applications.

Jonathan VanceView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Precision Agriculture
• +3 more

Jonathan Vance is a Lecturer in the School of Computing at the University of Georgia. He holds a Ph.D. and B.S. in Computer Science from the University of Georgia (2023 and 2009, respectively). His research focuses on applying artificial intelligence techniques to precision agriculture, particularly machine learning for biomass yield prediction and audio processing. He explores machine learning applications in agriculture, climate science, and image/audio processing. His educational background includes a strong foundation in computer science from UGA. His work emphasizes interdisciplinary approaches combining machine learning with agricultural challenges. Recent publications highlight advancements in data synthesis, domain adaptation, and feature selection for alfalfa biomass prediction. These studies contribute to sustainable agriculture through AI-driven solutions. No scientific awards or grants are explicitly listed in the provided information. He advises no listed students and maintains a professional website at jonathanvance.online .

Senthold AssengView profile

Professor

• Digital Agriculture
• Climate Change Impacts
• Sustainable Crop Production
• +5 more

Prof. Senthold Asseng is a Professor of Digital Agriculture at the Technical University of Munich (TUM), leading the Hans Eisenmann Forum for Agricultural Sciences since 2021. His research focuses on climate-plant-soil systems modeling, addressing global food security, sustainable agriculture, and digital technologies like vertical farming and autonomous robotics. He holds a PhD from Humboldt University Berlin and habilitation from TUM, with prior roles at CSIRO Australia and the University of Florida (as Full Professor and Director of the Florida Climate Institute). His honors include AAAS Fellow, Web of Science Highly Cited Researcher, and multiple teaching/mentorship awards. Education: BSc/MSc in Agronomy & Horticulture, Humboldt University Berlin (1989-1990) PhD in Agronomy, Humboldt University Berlin (1994) Habilitation in Agronomy, TUM (2004) Research Interests: Climate change impacts on crops, digital agriculture technologies, systems analysis for autonomous farming, and vertical farming sustainability. His work integrates modeling, robotics, and environmental control to enhance agricultural resilience and productivity. Awards: AAAS Fellow (2019) Highly Cited Researcher (2019) UF Research Foundation Professor (2016-2019) Grants & Leadership: Led interdisciplinary projects like AgMIP-Wheat, SECC, and FAO advisory roles. Active in editorial roles for journals like Global Change Biology and Environmental Research Letters . Labs/Teams: Chair of Digital Agriculture at TUM, collaborating on projects like Smartfield and Proteins4Singapore , focusing on automation, controlled environment agriculture, and climate-smart solutions.

Angela D KentView profile

Professor

• Microbial Ecology
• Nitrogen Cycling
• Agroecosystems
• +3 more

Angela D. Kent is a Professor in the Department of Natural Resources and Environmental Sciences at the University of Illinois Urbana-Champaign, where she also serves as Director of the Program in Ecology, Evolution, and Conservation Biology within the School of Integrative Biology. She is affiliated with the Carl R. Woese Institute for Genomic Biology. Her research focuses on microbial communities in agroecosystems and natural environments, particularly their roles in nitrogen cycling, soil health, and sustainable bioenergy production. Key research interests include microbial interactions in plant-microbe systems, the impact of genetic variation in crops on microbial processes, and the ecological and biogeochemical implications of soil microbiomes. She has authored over 99 publications and supervised datasets on topics such as denitrification dynamics, rhizosphere microbiome assembly, and microbial contributions to nitrogen retention. Dr. Kent has received the NACTA Educator Award (2012) and has contributed to high-impact studies on topics like microbial community responses to environmental stressors and the application of stable isotopes in bioenergy research. Her work bridges microbial ecology, agronomy, and environmental science, emphasizing practical solutions for sustainable agriculture and ecosystem management.

Karsten BorgwardtView profile

Professor

• Machine Learning
• Systems Biology
• Bioinformatics
• +3 more

Karsten Borgwardt is a Professor and Director of the Department of Machine Learning and Systems Biology at the Max Planck Institute of Biochemistry. He holds a PhD in Computer Science (2007) from LMU Munich and has held academic positions at ETH Zürich, Universität Tübingen, and the Max Planck Institutes in Tübingen. His research focuses on machine learning applications in biology and medicine, including biomarker discovery, personalized medicine, and systems biology. Education: PhD in Computer Science (2007), LMU Munich M.Sc. in Biology (2003), University of Oxford Diplom (M.Sc. equivalent) in Computer Science (2004), LMU Munich Research Interests: Development of machine learning algorithms for large biomedical datasets Pattern recognition in genomic and clinical data Applications in sepsis biomarkers, antimicrobial resistance prediction, and personalized medicine Grants & Projects: Scientific Coordinator of Marie Curie Networks (2013-2022) Swiss National Science Foundation Starting Grant (2014) Personalized Swiss Sepsis Study (CHF 5.3M, 2018) Awards: Krupp Award (2013), Golden Owl Teaching Award (2017), multiple 'Top 40 under 40' recognitions (2014-2016). Labs: Leads the Machine Learning and Systems Biology Department at MPI, collaborating with 22+ labs in sepsis research and international networks.

• Climate Change Adaptation
• Infrastructure Resilience
• Risk Analysis
• +5 more

Jim Hall is a Professor of Climate and Environmental Risk at the University of Oxford's School of Geography and the Environment, and serves as Director of Research there. He is also a Visiting Fellow at Linacre College and holds leadership roles including Chair of the Science Advisory Committee at IIASA, and Expert Advisor to the UK's National Infrastructure Commission. His work focuses on systemic risk analysis, infrastructure resilience, and policy implications of climate change adaptation. Prof Hall has pioneered methodologies like the National Infrastructure Systems Model (NISMOD) and chairs the Data and Analytics Facility for National Infrastructure (DAFNI). His research spans flood risk management, energy systems decarbonization, and transboundary water resource conflicts in regions such as the Eastern Nile Basin and the Caribbean. Key research areas include robust decision making under uncertainty, info-gap theory applications, and integrated assessments of human-environmental systems. He has contributed to major international assessments, including the IPCC's Fourth Assessment Report, and developed frameworks for multi-hazard stress testing of infrastructure networks. Scientific Awards: George Stephenson Medal (2001), Prince Sultan Prize for Water (2018), Royal Academy of Engineering Fellowship (2010) His advising and grants work includes mentoring a DPhil student Erin Canning and leading projects like MARIUS and ENHANCE. He has also developed innovative modeling tools for coastal erosion prediction and probabilistic assessments of global shipping fuel transitions. Prof Hall’s research groups actively engage in interdisciplinary projects, including the Oxford Martin Programme on Resource Stewardship and the UK Infrastructure Transitions Research Consortium. His work emphasizes bridging scientific analysis with actionable policy solutions for climate adaptation.

Justin SheffieldView profile

Professor

• Hydrology
• Climate Change
• Hydrological Extremes
• +5 more

Justin Sheffield is a Professor of hydrology and remote sensing and Head of the School of Geography and Environmental Science at the University of Southampton, UK. He holds a BSc in Mathematics with Oceanography (1989), MSc in Engineering Mathematics (1992), and PhD in Hydroclimatology (2008). His research focuses on large-scale hydrology, climate variability, hydrological extremes, and applications to natural hazards mitigation, with emphasis on water and food security in developing regions. Key research interests include drought monitoring/prediction, climate change impacts, and remote sensing integration. He leads projects like APP3793 (heat-related health risks) and EO-Africa (agricultural water management). Awards include the Prince Sultan Prize (2014), Plinius Medal (2013), and Robert E. Horton Lecturer (2019). His work spans global collaborations, including projects with the FAO and ESA, and he advises on PhD students. Notable publications address drought indices, crop yield modeling, and climate adaptation strategies.

Li ChenView profile

Associate Professor

• Deep Learning Systems
• Federated Learning
• Cloud Computing
• +3 more

Dr. Li Chen is an Alfred and Helen Lamson/BORSF Endowed Associate Professor in the School of Computing and Informatics at the University of Louisiana at Lafayette. She leads the CELESTIAL research lab, focusing on distributed systems and networking for machine learning and AI. Her research interests include federated learning, cloud computing, and resource optimization. Dr. Chen holds a Ph.D. from the University of Toronto and has received awards such as the NSF EPSCoR RII Track-4 grant and the BoRSF Endowed Professorship. Education: Ph.D. (2018), M.A.Sc. (2015) in Electrical and Computer Engineering from University of Toronto; B.Eng. (2012) in Computer Science from Huazhong University of Science and Technology. She also visited Hong Kong Polytechnic University (2013-2014). Research spans federated learning frameworks (e.g., SEAFL, FedClust), cloud resource scheduling (e.g., Hadar, HarmonyBatch), and applications in weather forecasting (e.g., MMST-ViT). Her work is supported by NSF, Louisiana BoRSF, and industry partners like XRMedix. Awards include the Alfred and Helen Lamson/BORSF Endowed Professorship (2024-2027), NSF EPSCoR grant (2024-2026), and best paper recognitions at IEEE conferences. She advises a diverse group of graduate students and has supervised alumni now in academia and industry. Teaching includes courses on computer networks, operating systems, and distributed systems. She organizes workshops and tutorials (e.g., 2023 Summer Tutorial on ML & Meteorology) and serves on conference committees such as INFOCOM and IWQoS.

Nicolas Federico MartinView profile

Associate Professor

• Precision Agriculture
• Crop Science
• Machine Learning Applications in Agriculture
• +7 more

Nicolas Federico Martin is an Associate Professor in the Department of Crop Sciences at the University of Illinois at Urbana-Champaign, with additional appointments as Associate Professor in the Center for Latin American and Caribbean Studies, Center for Digital Agriculture, and the National Center for Supercomputing Applications (NCSA). His interdisciplinary work bridges traditional agricultural science with cutting-edge computational approaches. Dr. Martin's research focuses on the intersection of agriculture and data science, with particular emphasis on: Precision agriculture and on-farm experimentation methodologies Machine learning applications for crop management and yield prediction Nitrogen and nutrient management optimization Soybean and corn breeding and production systems Remote sensing and UAV applications in agriculture Sustainable agricultural practices including cover crop management His publication record demonstrates a clear trajectory toward increasingly sophisticated integration of artificial intelligence with agricultural science. Recent work shows heavy emphasis on using machine learning algorithms (particularly reinforcement learning, convolutional neural networks, and generalized additive models) to solve practical farming challenges related to crop management decisions, yield prediction, and resource optimization. This research has significant implications for both scientific understanding of crop-environment interactions and practical farm management. Dr. Martin actively collaborates across disciplines and institutions, as evidenced by his extensive co-authorship network spanning agronomy, computer science, environmental science, and economics. His work has garnered attention from numerous news outlets and social media platforms, indicating its relevance to current agricultural challenges. He is a key contributor to the Data-Intensive Farm Management project, which aims to transform agronomic research through on-farm precision experimentation. His affiliation with NCSA provides access to high-performance computing resources essential for processing large agricultural datasets. Additionally, his work in Latin American agriculture (particularly in Mexico and Argentina) reflects his commitment to addressing global food security challenges.

Elfatih EltahirView profile

Professor

• Climate Change
• Hydroclimatology
• Environmental Science
• +3 more

Elfatih Eltahir is the H. M. King Bhumibol Professor at MIT, affiliated with the Climate and Environment faculty and the Department of Civil and Environmental Engineering. He holds a B.Sc. in Civil Engineering (University of Khartoum, 1985), M.Sc. in Hydrology (National University of Ireland, 1988), and S.M. and Sc.D. in Meteorology/Hydroclimatology (MIT, 1993). His research focuses on climate change impacts on water availability, extreme weather, and vector-borne diseases, with a particular emphasis on developing numerical models like the MIT Regional Climate Model (MRCM) and the Hydrology-Entomology-Malaria Transmission Simulator (HYDREMATS). Key research areas include projecting climate change effects on regions such as the Mediterranean, South Asia, and West Africa, with a focus on adaptation strategies. He has pioneered the concept of 'outdoor days' to quantify climate impacts on human activity. Awards include membership in the National Academy of Engineering (2023), World Academy of Sciences (2023), and American Geophysical Union Fellowship (2008). Teaching: Global Change Science (1.071), Surface Hydrology (1.714), Environmental Data Analysis (1.073) Projects: UM6P-MIT Research Program, Jameel Observatory Climate Resilience Early Warning System Network (CREWSnet) His work has addressed societal challenges like Nile River water management, malaria transmission dynamics, and climate adaptation in vulnerable communities. Field campaigns and collaborations with institutions like the United Arab Emirates reinforce his applied research focus. Awards: 2023: National Academy of Engineering Member 2023: World Academy of Sciences Fellow 2008: AGU Fellow 2017: Hydrologic Science Award Research grants and funding support impactful projects such as climate modeling for the Middle East, Africa, and the U.S., with a focus on interdisciplinary solutions.

Gary BrewerView profile

Professor

• Field Crops Entomology
• Insect Ecology
• Insect Pest Management
• +3 more

Dr. Gary Brewer is a Professor in the Department of Entomology at the University of Nebraska-Lincoln, with a 60% research and 40% teaching appointment. He has served as department head at NDSU (1997-2006) and UNL (2006-2018). His research focuses on field crops entomology, IPM of pasture cattle flies, salt creek tiger beetle conservation, and pollinator health. He has pioneered a push-pull strategy using coconut oil-derived repellents and led curriculum design for Rwanda’s Conservation Agriculture program. Education: B.S. in Zoology, University of Nebraska-Lincoln (1974) M.S. in Entomology, University of Nebraska-Lincoln (1978) Ph.D. in Entomology, Kansas State University (1984) Research Interests: Brewer’s work spans insect ecology, pest management, and conservation. Key areas include stable fly and horn fly control, pollinator protection, and endangered species recovery (e.g., Salt Creek tiger beetle). His lab develops natural product-based pest repellents and evaluates biopesticides for sustainable agriculture. Grants & Contributions: $362,150 USDA grant for undergraduate research in beneficial insect protection (2018) $325,000 USDA grant for multi-tactic stable fly control (2017) NE Game and Parks funded salt creek tiger beetle reintroduction programs Labs & Teams: Brewer’s team collaborates with industry (e.g., Vestergaard Frandsen) and international partners to advance IPM strategies. His work bridges field research with applied solutions for farmers and conservationists.

Preet SinghView profile

Professor

• Corrosion Science
• Electrochemistry
• Metals and Alloys
• +5 more

Preet Singh is a Professor and Associate Chair for Graduate Studies in the School of Materials Science and Engineering at Georgia Tech, with affiliations to the College of Engineering. His research focuses on corrosion science, electrochemistry, and environmental degradation of materials, particularly metals and alloys. Prior to joining Georgia Tech in 2003, he was a faculty member at the Institute of Paper Science and Technology (IPST), where he investigated corrosion issues in the pulp and paper industry. Professor Singh's work explores fundamental mechanisms of material degradation in industrial environments, aiming to develop mitigation strategies against environment-induced failures. Key research areas include corrosion fatigue, hydrogen embrittlement, stress corrosion cracking, and oxidation behavior. His group employs experimental approaches to study material reliability under varying chemical and mechanical conditions. Recent publications demonstrate interdisciplinary collaboration across oncology, agriculture, and energy systems, reflecting broad applications of materials science principles. Research trends show increased focus on biomedical materials and sustainable technologies alongside core corrosion studies. Professor Singh advises graduate students including Abdullah Alzubail, Yousif Al Rabie, Sai Shreeya, Yara, and Sean Li. He directs the Corrosion and Materials Reliability Laboratory (CMCRL), which partners with industry to solve practical engineering challenges related to material performance.