School of Engineering
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Grace Gao
Associate Professor of Aeronautics and Astronautics
BioGrace Gao is an associate professor in the Department of Aeronautics and Astronautics at Stanford University. She leads the Navigation and Autonomous Vehicles Laboratory (NAV Lab). Before joining Stanford University, she was faculty at University of Illinois at Urbana-Champaign. She obtained her Ph.D. degree at Stanford University. Her research is on robust and secure perception, localization and navigation with applications to manned and unmanned aerial vehicles, autonomous driving cars, as well as space robotics.
Prof. Gao has won a number of awards, including the NSF CAREER Award, the Institute of Navigation Early Achievement Award and the RTCA William E. Jackson Award. She received the Inspiring Early Academic Career Award from Stanford University, Distinguished Promotion Award and Dean's Award for Excellence in Research from University of Illinois at Urbana-Champaign. She has won Best Paper/Presentation of the Session Awards 29 times at Institute of Navigation conferences over the span of 17 years. For her teaching and advising, Prof. Gao has been on the List of Teachers Ranked as Excellent by Their Students multiple times. She won the College of Engineering Everitt Award for Teaching Excellence, the Engineering Council Award for Excellence in Advising, and AIAA Illinois Chapter’s Teacher of the Year. Prof. Gao also received AIAA Stanford Chapter Excellence in Advising Award and Excellence in Teaching Award in 2022 and 2023, respectively. -
Xiaojing Gao
Assistant Professor of Chemical Engineering
Current Research and Scholarly InterestsHow do we design biological systems as “smart medicine” that sense patients’ states, process the information, and respond accordingly? To realize this vision, we will tackle fundamental challenges across different levels of complexity, such as (1) protein components that minimize their crosstalk with human cells and immunogenicity, (2) biomolecular circuits that function robustly in different cells and are easy to deliver, (3) multicellular consortia that communicate through scalable channels, and (4) therapeutic modules that interface with physiological inputs/outputs. Our engineering targets include biomolecules, molecular circuits, viruses, and cells, and our approach combines quantitative experimental analysis with computational simulation. The molecular tools we build will be applied to diverse fields such as neurobiology and cancer therapy.
