Congratulations to John-Paul Ore, assistant professor; Thomas Price, associate professor; and Wujie Wen, associate professor, on receiving a Faculty Early Career Development award, also known as the CAREER Award, from the National Science Foundation (NSF). The award is one of the highest honors given by the NSF to young faculty members in science and engineering.

John-Paul Ore has received $594,739 for his project, “Robust and Lightweight Formal Methods for Mobile Robot System Development.” His award is effective through July 31, 2029. 

Ore’s Research Abstract: Open-source robot software aims to enable rapid system development, but comes with little or no tooling for automated testing and analysis. This work utilizes model checking of behavior trees and abstract type inference of physical units to automatically suggest system tests and help ensure the absence of certain classes of software defects. This CAREER proposal examines whole system representation and tooling across interdisciplinary boundaries. We aim to greatly reduce the cost and improve the scalability of lightweight formal methods for robotic software systems, thus laying the foundation for the next generation of automated testing and analysis of robotic systems.

Thomas Price has received $644,883 for his project, “Improving Machine Learning Education Through Data-driven Support for Pipeline Design and Implementation.” His award is effective through August 14, 2028. 

Price’s Research Abstract: Machine learning (ML) is a powerful computing tool for building models from data, which is becoming a vital skill across STEM disciplines. Yet ML is a challenging subject, requiring students to construct complex ML “pipelines,” often with little one-on-one support from instructors. The goal of this CAREER proposal is to aid students in learning to design and implement ML pipelines through a data-driven tutoring system. The project will develop novel techniques for evidence-centered, real-time assessment of students’ ML knowledge and new forms of automated support for ML, including design feedback and adaptive code examples.

Wujie Wen has received $600,000 for his project, “Dependable and Secure Machine Learning Acceleration from Untrusted Hardware.” His award is effective through September 30, 2028. 

Wen’s Research Abstract: Fueled by ML model and hardware advancements, intelligence is transforming every walk of life. For critical applications like autonomous vehicles, ensuring inference dependability is essential. Unfortunately, current hardware cannot provide such a promise. This CAREER project aims to create a new paradigm of safeguarding ML execution against both passive hardware faults and active fault attacks. The novelties lie in the new capability development inside ML processing and the cross-layer exploration of algorithm, architecture and hardware security. The broader impacts include yielding practical solutions for ensuring the root of trust of accelerated intelligence services and abundant educational opportunities.