Ten interdisciplinary research projects have won funding from Princeton University’s Schmidt DataX Fund, with the goal of spreading and deepening the use of artificial intelligence and machine learning across campus to accelerate discovery.
The 10 faculty projects, supported through a major gift from Schmidt Futures, involve 19 researchers and several departments and programs, from computer science to politics. Three of these projects are led by faculty in electrical and computer engineering.
The projects explore a variety of subjects, including an improved magnetic technology for powering electronics, a data map of COVID-19 patient responses, and a new framework for modeling semiconductor devices.
“We are excited by the wide range of projects that are being funded, which shows the importance and impact of data science across disciplines,” said Peter Ramadge, Princeton’s Gordon Y.S. Wu Professor of Engineering and the director of the Center for Statistics and Machine Learning (CSML). “These projects are using artificial intelligence and machine learning in multifaceted ways: to unearth hidden connections or patterns, model complex systems that are difficult to predict, and develop new modes of analysis and processing.”
CSML is overseeing a range of efforts made possible by the Schmidt DataX Fund to extend the reach of data science across campus. These efforts include the hiring of data scientists and overseeing the awarding of DataX grants. This is the second round of DataX seed funding, with the first in 2019.
The three winning projects and research faculty from ECEMagNet: Transforming power magnetics design with machine learning tools and SPICE simulations
Minjie Chen, assistant professor of electrical and computer engineering and the Andlinger Center for Energy and the Environment; Niraj Jha, professor of electrical and computer engineering; Yuxin Chen, assistant professor of electrical and computer engineering
Magnetic components are typically the largest and least efficient components in power electronics. To address these issues, this project proposes the development of an open-source, machine learning-based magnetics design platform to transform the modeling and design of power magnetics.
Generalized clustering algorithms to map the types of COVID-19 response
Jason Fleischer, professor of electrical and computer engineering
Clustering algorithms are made to group objects but fall short when the objects have multiple labels, the groups require detailed statistics, or the data sets grow or change. This project addresses these shortcomings by developing networks that make clustering algorithms more agile and sophisticated. Improved performance on medical data, especially patient response to COVID-19, will be demonstrated.
New framework for data in semiconductor device modeling, characterization and optimization suitable for machine learning tools
Claire Gmachl, the Eugene Higgins Professor of Electrical Engineering
This project is focused on developing a new, machine learning-driven framework to model, characterize and optimize semiconductor devices.