I am a postdoctoral scholar at Stanford Intelligent Systems Lab (SISL). I received my Ph.D. degree in Mechanical Engineering from Carnegie Mellon in 2023 and a master's degree in Industrial and Operations Engineering at the University of Michigan, Ann Arbor. Much of my work combines machine learning and rare-event theories to efficiently simulate rare catastrophic events. The applications of this line of work include the accelerated testing of intelligent systems. Currently, I am working on AI for safety and sustainability projects, which merge efficient simulation frameworks with optimization and decision-making algorithms.

Professional Education

  • Master of Science in Engr, University of Michigan Ann Arbor (2018)
  • Doctor of Philosophy, Carnegie Mellon University (2023)
  • Bachelor of Engineering, Institut Teknologi Surabaya (2014)
  • PhD, Carnegie Mellon University, Mechanical Engineering (2023)
  • MSE, University of Michigan, Ann Arbor, Industrial and Operations Engineering (2018)
  • BE, Institute Technology of Sepuluh Nopember, Indonesia, Industrial and Systems Engineering (2014)

Stanford Advisors

Lab Affiliations

All Publications

  • A Survey on Safety-Critical Driving Scenario Generation-A Methodological Perspective IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS Ding, W., Xu, C., Arief, M., Lin, H., Li, B., Zhao, D. 2023; 24 (7): 6971-6988
  • An Accelerated Approach to Safely and Efficiently Test Pre-Production Autonomous Vehicles on Public Streets Arief, M., Glynn, P., Zhao, D., IEEE IEEE. 2018: 2006–11