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Song Li

Associate Professor

My research, teaching, and outreach are focused on the interface of agriculture and data science. My team develops computational tools and machine learning methods to integrate and interpret large scale data generated by advanced genomics and sensor technologies in agricultural research. More specifically, my research program includes two broad themes. First, we use machine learning to identify gene regulatory networks in plant abiotic stress responses. Second, we develop sensing technology and computational tools for early detection of plant diseases and to collect plant phenotype data at field scale to improve crop breeding programs.

CAIA Platform(s):

  • SmartFarm Innovation Network™:
    • Controlled Environment Agriculture Innovation Center
    • SmartTechnologies for Crop and the Green Industries
  • Cyberbiosecurity and Biosecurity in Agriculture and Life Sciences
  • Data Analytics, Decisions, and Machine Learning for Food, Agriculture, Communities, and Health Systems