Three CSL assistant professors win NSF CAREER awards
CSL assistant professors Katie Driggs-Campbell, Jian Huang, and Negar Mehr have just received NSF CAREER awards, which are more than just ordinary research grants: they’re statements of confidence in the early-career recipients’ ability to achieve great things in the future.
According to NSF’s website, its Faculty Early Career Development (CAREER) Program “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 research and education and to lead advances in the mission of their department or organization.” The grants are intended to “build a firm foundation for a lifetime of leadership in integrating education and research.”
Katie Driggs-Campbell’s primary appointment is in Electrical & Computer Engineering. Her CAREER project is called “Uncovering Structure in Human-Robot Systems for Trajectory Prediction and Crowd Navigation” and received $500k in support. Its goal is to develop algorithms to enable more fluid navigation for robots deployed in real-world situations alongside humans.
“Our aim is to balance efficiency and safety, guaranteeing reliable performance even in the presence of erratic human behavior and sensor uncertainty,” says Driggs-Campbell. “It’s very important to us that we deploy and evaluate our research on real-world robots, so we can have impact across many challenging problem domains. We plan to run experiments in many different applications, including agricultural robots, which can help alleviate labor issues [in farming]; collaborative manufacturing or co-robots, which are seeing a rise in popularity in industry; and transportation, where behavior prediction and interaction remains one of the key challenges for autonomous vehicles.”
Jian Huang’s primary appointment is in Electrical & Computer Engineering. His CAREER project is called “Towards Learning-Based Storage Systems with Hardware-Software Co-Design” and received $593k in support. Under the award, his group will develop learning-based storage devices, storage software optimizations, and storage architecture innovations.
According to Huang, recent advances in machine learning techniques show that the learning-based approach has promise as a way to solve system optimization problems, although the best ways to apply learning techniques to modern storage systems remain unclear. “Our project will intensively explore learning-based techniques to enable automated development, management, and optimizations of storage systems,” he says.
Negar Mehr’s primary appointment is in Aerospace Engineering. Her project is called “Socially-Aware Control of Autonomy: Reshaping Urban Mobility in Traffic Networks with Mixed Vehicle Autonomy” and received $500k in support. It will look at how traffic networks are affected when roads are shared by human-driven and autonomous cars.
“To unlock the potentials of mixed-autonomy traffic, one of the key challenges is to account for how humans adapt and respond to autonomous cars’ decisions, such as routing decisions, lane choices, and speed, as such interactions will in turn affect mobility, sustainability, equity, etc.,” Mehr says. Specifically, the project will focus on travelers’ routing decisions as a key factor that affects system-level mobility.
“My long-term research goal is to create control algorithms that ensure socially aware control of autonomy. For example, control algorithms that take into account the social implications of the co-existence of humans and autonomous systems,” Mehr adds.
All three CAREER grants will run for five years.