Two new interdisciplinary research projects led by ECE faculty have won seed funding from Princeton University’s Schmidt DataX Fund, marking the third round of grants undertaken by the fund since 2019. The fund, supported through a major gift from the Schmidt Futures Foundation, provides grants to explore using artificial intelligence and machine learning to accelerate discovery.
One team will address a crucial performance gap in organic semiconductors, key to organic solar cells, while the other team plans to improve safety systems for autonomous vehicles.
“These projects are exciting because they explore how important and challenging problems can be tackled using modern data analysis and machine learning approaches while speeding scientific discovery. The core idea is to replace processes which are slow and laborious with machine-assisted methods,” said Peter Ramadge, director of the Center for Statistics and Machine Learning (CSML) and Gordon Y.S. Wu Professor of Engineering. “These projects are not confined to traditional ‘technical’ fields, but also span large-scale problems in the humanities and social sciences.”
In total, DataX funded eight projects this year involving 13 faculty across seven departments and programs, from engineering and computer science to Near Eastern studies and psychology.
CSML is overseeing a range of efforts made possible by the Schmidt DataX Fund to extend the reach of data science and machine learning across campus. These efforts include hiring data scientists and overseeing the awarding of DataX grants.
The two funded projects from electrical and computer engineering are:
Enabling Crystalline Organic Semiconductor Devices
Barry Rand, associate professor of electrical and computer engineering and the Andlinger Center for Energy and the Environment
Adji Bousso Dieng, assistant professor of computer science
Organic semiconductor devices are multilayered, sometimes involving seven to eight distinct layers, but these layers are disordered and restrict the performance of organic semiconductor devices. This project addresses these shortcomings by using data science to design crystalline layers that will enable the further advancement of these devices.
Provably Robust Perception and Control for Safe Autonomous Driving
Jaime Fernández Fisac, assistant professor of electrical and computer engineering
Prateek Mittal, associate professor of electrical and computer engineering
Autonomous vehicles are poised to revolutionize transportation but still face hurdles in navigating unexpected or even hostile situations. This project seeks to overcome these challenges by unifying robust visual perception and safe trajectory planning under a common framework.
This article was adapted from the original, which ran on the Princeton University homepage.