AI/ML for Environmental and Aquatic Science

In our laboratory, we harness the power of Artificial Intelligence (AI) and Machine Learning (ML) to advance environmental and aquatic sciences. Our work focuses on developing AI-driven tools to accelerate environmental solutions, including biodiversity monitoring and ecosystem knowledge summation.

Selected Published Works

A bacterial sensor taxonomy across earth ecosystems for machine learning applications

Park H, Joachimiak MP, Jungbluth SP, ..., Dehal P. (2023). A bacterial sensor taxonomy across earth ecosystems for machine learning applications. mSphere, 9, e00026-23. https://doi.org/10.1128/msystems.00026-23