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Rachel Luo
Alumni · now at NVIDIA Research
Rachel Luo is a Ph.D. candidate in the Electrical Engineering department. She received a B.S. in Electrical Engineering and Computer Science from MIT in 2014, and an M.S. in Electrical Engineering from Stanford in 2017. Rachel's research focuses on uncertainty quantification for problems at the intersection of computer vision and robotics.
In her free time, Rachel enjoys photography, rock climbing, hiking, and commuting by electric longboard.
Awards:
- Stanford Graduate Fellowship
- National Science Foundation (NSF) Fellowship
Publications
7 publications · full lab bibliography
2024
- R. Luo, “Sample-Efficient Uncertainty Calibration for Reliable Autonomous Systems,” Ph.D. dissertation, Stanford University, Dept. of Aeronautics and Astronautics, 2024.
- A. Hindy, R. Luo, S. Banerjee, J. Kuck, E. Schmerling, and M. Pavone, “Diagnostic Runtime Monitoring with Martingales,” 2024. (Submitted)
- R. Luo, R. Sinha, Y. Sun, A. Hindy, S. Zhao, S. Savarese, E. Schmerling, and M. Pavone, “Online Distribution Shift Detection via Recency Prediction,” Proc. IEEE Conf. on Robotics and Automation, 2024.
2022
- R. Luo, S. Zhao, J. Kuck, B. Ivanovic, S. Savarese, E. Schmerling, and M. Pavone, “Sample-Efficient Safety Assurances using Conformal Prediction,” Workshop on Algorithmic Foundations of Robotics, 2022.
- R. Sinha, S. Sharma, S. Banerjee, T. Lew, R. Luo, S. M. Richards, Y. Sun, E. Schmerling, and M. Pavone, “A System-Level View on Out-of-Distribution Data in Robotics,” 2022.
- R. Luo, A. Bhatnagar, H. Wang, C. Xiong, S. Savarese, Y. Bai, S. Zhao, S. Ermon, E. Schmerling, and M. Pavone, “Local Calibration: Metrics and Recalibration,” Proc. Conf. on Uncertainty in Artificial Intelligence, 2022.