Welcome to the Song Lab!

We are passionate about developing innovative modeling and computational tools to address key challenges in biological, life, and environmental sciences. Our approach features synergistic integration of a suite of complementary modeling and theoretical methods (process-based modeling, data-driven modeling, and ecological theory) for enhanced prediction and data incorporation.

Updates 
  • November 2022: Dr. Manokaran Veeramani joins our lab from IIT Madras as a postdoctoral researcher; Ayeon Park joins our lab as an intern through the WEST program - welcome Manokaran and Ayeon!
  • November 2022: Naeun Lee, Dr. Firnaaz Ahamed, and Dr. Song give oral presentations at the 2022 AIChE Meeting in Phoenix, AZ: (1) Naeun Lee: "A Combined Analysis of Metabolic Networks and Transcriptomic Data to Predict the Impacts of Copper Deficiency on the Liver Metabolism", (2) Dr. Ahamed: Knowledge-Informed Data-Driven Modeling for Robust Prediction of Microbial Inactivation in Food, and (3) Dr. Song: Data-Driven QSAR Modeling for the Iterative Identification of Chemical Motifs from Limited Data.
  • November 2022: Dr. Song gives an invited talk at the Plant Science Retreat on "Data-driven and metabolic network modeling to predict context-dependent microbial interactions and community dynamics in soil systems." 
  • October 2022: Naeun Lee and Dr. Firnaaz Ahamed give NFHC sponsored Works in Progress presentations on "Metabolic network modeling of the coupled impacts of copper deficiency and diets on the liver metabolism" and "Data-driven identification of microbial sensors differentiating the impacts of starch variants on gut microbiomes," respectively.
  • October 2022: Steve Zhang and Dr. Firnaaz Ahamed publish an article in Frontiers in Food Science and Technology (title: Knowledge-informed data-driven modeling for sparse identification of governing equations for microbial inactivation processes in food).

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