University of Utah Department of Electrical and Computer Engineering assistant professor Cunxi Yu has received a five-year, National Science Foundation (NSF) Faculty Early Career Development (CAREER) Award for his project, “CAREER: OneSense: One-Rule-for-All Combinatorial Boolean Synthesis via Reinforcement Learning.”

“The NSF CAREER award is a highly competitive award for young faculties. It is a great honor to receive this award and more importantly is a great encouragement for my future research and teaching,” Yu said.

The CAREER Program is a Foundation-wide activity that offers the National Science Foundation’s most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in both research and education. Awarded faculty lead advances in the mission of their department or organization.

Over the next five years, Yu will use the $478,526 awarded to work towards employing reinforcement learning and neural networks to enable self-learning high-performance algorithms and heuristics over graphs.

With this new research, networks can outperform existing hand-crafted approaches without human supervision and domain knowledge. This will can be generalized to autonomously learn and discover novel graph-based combinatorial optimization heuristics at a wide range of application domains without any guidance.

This project will produce open-source software and conference tutorials to facilitate technology transfers and fruitful industry-academia interactions in a multidisciplinary community.

“The proposed research focuses on generic optimization techniques that could generate broader social impacts to an average person’s life such as scheduling and computing. Specifically, I will focus on leveraging the proposed techniques into minimal carbon footprint computing,” Yu said.

With this project, Yu will train the next generation of scientists. Given the underrepresentation of women in engineering and theoretical computer science, Yu is making a distinct effort to bring on female undergraduate students and gradient researchers. He is also bringing early knowledge of Computer Engineering to low-income k-12 schools.