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About UsIn our research group, we develop new data-driven learning and inference algorithms for autonomous perception and decision-making in complex cyber-physical systems with emphasis on health monitoring, decision frameworks, information fusion, resilient control, multi-agent systems, cyber-physical security and human-machine interaction. Our application areas of interest include energy (e.g., smart buildings, building-power grid interaction, power plants and wind turbines), manufacturing (e.g., design for manufacturability), agriculture (e.g., high-throughput phenotyping, multi-scale data assimilation), transportation (e.g., complex traffic networks, bridge health monitoring), aerospace (e.g., gas turbine engines) and industrial (e.g., gearbox, electric motors) systems. The nature of our research is inherently multi-disciplinary involving machine learning, dynamical systems, information theory, theory of computation, topology and statistical mechanics.
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What's New
Our new USDA/NSF project on large-scale machine learning for early detection and mitigation of row crop diseases:
https://tinyurl.com/yc797g57
Our new USDA/NSF project on large-scale machine learning for early detection and mitigation of row crop diseases:
https://tinyurl.com/yc797g57
Research Areas
OpportunitiesCheck back soon.
WorkshopsUpcoming Workshop:
2nd ACM SIGKDD Workshop on Machine Learning for Prognostics and Health Management (ML for PHM 2017): https://sites.google.com/site/mlforphm2017/ |