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Accelerating the Response to Biothreats: Machine Learning as Screening for Antimicrobials

CEDER supports the College of Engineering Biothreat team's goal of using neural networks and machine learning to discover more effective antimicrobials by developing materials for and piloting a curriculum that introduces high school students to relevant science and technologies. 


The Biothreats team at the University of Michigan College of Engineering is attempting to curb dangerous drug-resistant bacteria and shorten the time it takes to get new effective medications on the market. The team is exploring the use of neural networks and machine learning to help identify promising nanoparticles that can serve as the next generation of antimicrobials.

The Design Coordinator at the Center for Education Design, Evaluation, and Research (CEDER) supports this project by thinking about the education of future users and developers of these technologies and interdisciplinary solutions to complex problems.  In particular, the goal of ongoing design work is to develop an instructional module for high school science students that introduces them to biofilms, drug-resistant bacteria, nanotechnology, and the use of computational experiments in the development of new treatments for illnesses caused by these bacteria. Design and development of the materials is ongoing and have already been piloted with students.