Mansur M. Arief

I am a research engineer at the Stanford Intelligent Systems Lab (SISL) advised by Mykel Kochenderfer with a joint appointment at Mineral-X working with Jef Caers. I finished my Ph.D. in Mechanical Engineering from Carnegie Mellon University in 2023 advised by Ding Zhao in the Safe AI lab. Prior to that, I completed my Bachelor's degree in Industrial and Systems Engineering at Institut Teknologi Sepuluh Nopember (ITS) in Indonesia and my Master's degree in Industrial and Operations Engineering at the University of Michigan, Ann Arbor.

My research primarily integrates machine learning, optimization, and sample-efficient simulation methods to assist scalable decision making tasks that considers risks of catastrophic failure events. This approach is particularly useful in the safety validation of autonomous robots, subsurface explorations, and healthcare applications. I am grateful to have been surrounded with great mentors, collaborators, students, and colleagues on various occasions and am constantly learning new things from them every single day. For a concise overview of our work, please visit my research page. I am always open to exploring potential collaborations, especially in domains that combine AI, safety, and sustainability. Here is the link to my CV for further information.

• Arief, Mansur, Mike Timmerman, Jiachen Li, David Isele, Mykel Kochenderfer. "Optimized Guided Meta Training for Intelligent Agents under Highly Interactive Driving Scenarios." IEEE RA-L. [Link]
• Ding, Wenhao, Chejian Xu, Mansur Arief, Haohong Lin, Bo Li, Ding Zhao. "A Survey on Safety-Critical Driving Scenario Generation—A Methodological Perspective." T-ITS, 2023. [Link]
• Arief, Mansur, Zhepeng Cen, Zhenyuan Liu, Zhiyuan Huang, Bo Li, Henry Lam, and Ding Zhao. "Certifiable Evaluation for Autonomous Vehicle Perception Systems Using Deep Importance Sampling (Deep IS)." ITSC, 2022. [Link]
• Arief, Mansur, Zhiyuan Huang, Guru Koushik Senthil Kumar, Yuanlu Bai, Shengyi He, Wenhao Ding, Henry Lam, and Ding Zhao. "Deep Probabilistic Accelerated Evaluation: A Certifiable Rare-Event Simulation Methodology for Black-Box Autonomy. "AISTATS, 2021. [Link]