I am a PhD student at Stanford University advised by Marco Pavone in the Autonomous Systems Lab and Silvio Savarese in the Stanford Vision and Learning Lab. I completed my B.S. in Electrical Engineering and Computer Science at MIT.

I develop methods for rigorously quantifying uncertainty to improve the safety and reliability of autonomous systems. I am particularly interested in working on autonomous systems that can reason about uncertainty in unstructured real‑world environments, where the systems are complex and may be composed of classical algorithms, specialized learning‑based components, and general‑purpose foundation models. Topics that I’m currently interested in include machine learning, computer vision, robotics, uncertainty quantification and calibration, distribution shift detection and mitigation, and foundation models.

Contact: rsluo at stanford dot edu
CV (September 2023)

Preprints

• Sample-Efficient Safety Assurances using Conformal Prediction (extended version)
  Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone.
  Invited to a special issue of the International Journal of Robotics Research (IJRR) featuring the best papers of WAFR 2022.
  [paper] [talk]

• Online Distribution Shift Detection via Recency Prediction
  Rachel Luo, Rohan Sinha, Yixiao Sun, Ali Hindy, Shengjia Zhao, Silvio Savarese, Edward Schmerling, Marco Pavone.
  arXiv preprint: https://arxiv.org/abs/2211.09916.
  [paper]

• A System-Level View on Out-of-Distribution Data in Robotics
  Rohan Sinha, Apoorva Sharma, Somrita Banerjee, Thomas Lew, Rachel Luo, Spencer Richards, Yixiao Sun, Edward Schmerling, Marco Pavone.
  arXiv preprint: https://arxiv.org/abs/2212.14020.
  [paper]

• Privacy Preserving Recalibration under Domain Shift
  Rachel Luo, Shengjia Zhao, Jiaming Song, Jonathan Kuck, Stefano Ermon, Silvio Savarese.
  arXiv preprint: https://arxiv.org/abs/2008.09643.
  [paper]

Publications

• Local Calibration: Metrics and Recalibration
  Rachel Luo*, Aadyot Bhatnagar*, Yu Bai, Shengjia Zhao, Huan Wang, Caiming Xiong, Silvio Savarese, Stefano Ermon, Edward Schmerling, Marco Pavone.
  Conference on Uncertainty in Artifical Intelligence (UAI), 2022.
  [paper] [poster]

• Sample-Efficient Safety Assurances Using Conformal Prediction
  Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone.
  Workshop on the Algorithmic Foundations of Robotics (WAFR), 2022.
  [paper] [talk]

• Belief Propagation Neural Networks
  Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon.
  Conference on Neural Information Processing Systems (NeurIPS), 2020.
  [paper] [code] [poster]

• Scene Semantic Reconstruction from Egocentric RGB-D-Thermal Videos
  Rachel Luo, Ozan Sener, Silvio Savarese.
  International Conference on 3D Vision (3DV), 2017.
  [paper] [project]

• Analytical Model of Graphene-Enabled Ultra-Low Power Phase Change Memory
  Aaron Alpert*, Rachel Luo*, Medhi Asheghi, Eric Pop, Kenneth Goodson.
  IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), 2016.
  [paper]

• Using Iterated Local Search for Solving the Flow-Shop Problem
  Angel Juan, Helena Lourenco, Manuel Mateo, Rachel Luo, Quim Castella.
  International Transactions in Operational Research, 2013.
  [paper]