Bio

Robert Moss is a computer science Ph.D. student at Stanford University studying algorithms to validate safety-critical autonomous systems. He holds an M.S. in computer science from Stanford where his research received the best computer science master’s thesis award and he also received the Centennial TA award for his teaching efforts. He earned his B.S. in computer science with a minor in physics from the Wentworth Institute of Technology in Boston, MA. Robert was an associate research staff member at MIT Lincoln Laboratory where he was on the team that designed, developed, and validated the next-generation aircraft collision avoidance system for commercial aircraft, unmanned vehicles, and rotorcraft. Robert was also a research engineer at the NASA Ames Research Center, developing decision support tools for the VIPER autonomous Lunar rover mission searching for water deposits on the Moon. Robert is a member of the Stanford Intelligent Systems Laboratory, the Stanford Center for Earth Resources Forecasting, and part of the Stanford Center for AI Safety conducting research on methods for high-dimensional planning under uncertainty using low-dimensional surrogate models, autonomous vehicle risk assessment, and efficient algorithms for safety validation.

Education & Certifications

  • M.S., Stanford University, Computer Science (2021)
  • B.S., Wentworth Institute of Technology, Computer Science (2014)

Contact

  • Academic
    mossr@stanford.edu
    University - Student Department: Computer Science Position: Graduate

Additional Info

  • Mail Code: 4035

Links

Lab Affiliations

All Publications

  • Bayesian Safety Validation for Failure Probability Estimation of Black-Box Systems JOURNAL OF AEROSPACE INFORMATION SYSTEMS Moss, R. J., Kochenderfer, M. J., Gariel, M., Dubois, A. 2024

    View details for DOI 10.2514/1.I011395

    View details for Web of Science ID 001232925800001

  • ConstrainedZero: Chance-Constrained POMDP Planning Using Learned Probabilistic Failure Surrogates and Adaptive Safety Constraints Moss, R. J., Jamgochiani, A., Fischer, J., Corso, A., Kochenderfer, M. J., Larson, K. IJCAI-INT JOINT CONF ARTIF INTELL. 2024: 6752-6760

    View details for Web of Science ID 001347142806098

  • Uncovering heterogeneous effects in computational models for sustainable decision-making ENVIRONMENTAL MODELLING & SOFTWARE Kozlova, M., Moss, R. J., Yeomans, J., Caers, J. 2024; 171

    View details for DOI 10.1016/j.envsoft.2023.105898

    View details for Web of Science ID 001126162900001

  • Formal and Practical Elements for the Certification of Machine Learning Systems Durand, J., Dubois, A., Moss, R. J., IEEE IEEE. 2023

    View details for DOI 10.1109/DASC58513.2023.10311201

    View details for Web of Science ID 001103267600097

  • A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH Corso, A., Moss, R. J., Koren, M., Lee, R., Kochenderfer, M. J. 2021; 72: 377-428

    View details for Web of Science ID 000735206800011

  • Certification Considerations for Adaptive Stress Testing of Airborne Software Durling, M., Herencia-Zapana, H., Meng, B., Meiners, M., Hochwarth, J., Visser, N., Lee, R., Moss, R., Valapil, V., IEEE IEEE. 2021

    View details for DOI 10.1109/DASC52595.2021.9594395

    View details for Web of Science ID 000739652600097

  • Adaptive Stress Testing of Trajectory Predictions in Flight Management Systems Moss, R. J., Lee, R., Visser, N., Hochwarth, J., Lopez, J. G., Kochenderfer, M. J., IEEE IEEE. 2020

    View details for Web of Science ID 000646035600136