CEMS Faculty Receive Rapid Response Seed Grant From UMN DSI
Congratulations to Assistant Professor Natalie Boehnke and Associate Professor Qi Zhang for receiving a Rapid Response Seed Grant from the University of Minnesota’s Data Science Initiative (DSI). This funding mechanism, which provides up to $15,000 per project, supports innovative research in data science and artificial intelligence. The DSI aims to foster collaboration and chart new directions in research, addressing grand challenges through interdisciplinary efforts.
The funded project, "A Machine Learning Approach for the Prediction of Materials and Biological Features Mediating Delivery Outcomes of Nanoparticles," leverages machine learning (ML) to optimize drug delivery outcomes. In addition to Drs. Boehnke and Zhang, Professor Lynn Walker is also a collaborator on this initiative. Their interdisciplinary approach combines preclinical drug delivery, data science, and colloidal characterization to tackle challenges that could not be addressed individually.
Abstract:
The development of nanoparticles for drug delivery applications is empirical, low-throughput, and iterative, creating a need to reformulate for each disease target. Biological heterogeneity further limits the translational success of nanoparticle candidates evaluated in limited preclinical contexts. Data-driven approaches to nanoparticle testing have demonstrated potential to yield new insights. With funding from the DSI Rapid Response Grant, this project aims to expand these approaches, establishing generalizable workflows for the data-driven and accelerated design of nanoparticles. The researchers will create a machine learning-enhanced design of experiment framework for nanomedicine, with applications extending to additional colloidal systems.
To learn more about this award, visit the DSI’s website.
To learn more about Dr. Natalie Boehnke, Dr. Qi Zhang, and Dr. Lynn Walker’s research, please visit their respective research group’s website linked below.