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Somrita Banerjee

somrita _at_ stanford _dot_ edu

Somrita Banerjee

Alumni · now at Apple

Somrita Banerjee is a Ph.D. candidate in Aeronautics and Astronautics. She received her B.S. in Mechanical Engineering with minors in Aerospace Engineering and Computer Science from Cornell University in 2017. At Cornell University, she worked in the Space Systems Design Studio with Professor Mason Peck.

Somrita's current research interests lie at the intersection of trajectory optimization, machine learning, and optimal control of the next generation of space robots, specifically to further goals of greater autonomy and risk-sensitive learning.

In her free time, Somrita enjoys dancing, playing board games with friends, and going hiking in sunny California.

Awards:

  • Stanford Graduate Fellowship

Publications

8 publications · full lab bibliography

2024
  1. S. Banerjee, “Learning-enabled Adaptation to Evolving Conditions for Space Robotics,” Ph.D. dissertation, Stanford University, Dept. of Aeronautics and Astronautics, 2024.
  2. S. Banerjee, B. Balaban, M. Shirley, K. Bradner, and M. Pavone, “Contingency Planning Using Bi-level Markov Decision Processes for Space Missions,” IEEE Aerospace Conference, 2024.
  3. A. Hindy, R. Luo, S. Banerjee, J. Kuck, E. Schmerling, and M. Pavone, “Diagnostic Runtime Monitoring with Martingales,” 2024. (Submitted)
2023
  1. M. Foutter, R. Sinha, S. Banerjee, and M. Pavone, “Self-Supervised Model Generalization using Out-of-Distribution Detection,” Conf. on Robot Learning - Workshop on Out-of-Distribution Generalization in Robotics, 2023.
  2. S. Banerjee, A. Sharma, E. Schmerling, M. Spolaor, M. Nemerouf, and M. Pavone, “Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications,” IEEE Aerospace Conference, 2023.
2022
  1. 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.
2020
  1. S. Banerjee, J. Harrison, P. M. Furlong, and M. Pavone, “Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics,” Int. Symp. on Artificial Intelligence, Robotics and Automation in Space, 2020.
  2. S. Banerjee, T. Lew, R. Bonalli, A. Alfaadhel, I. A. Alomar, H. M. Shageer, and M. Pavone, “Learning-based Warm-Starting for Fast Sequential Convex Programming and Trajectory Optimization,” IEEE Aerospace Conference, 2020.