Mansur Arief
Research Engineer
Aeronautics and Astronautics
Bio
I am a research engineer at Stanford Intelligent Systems Lab (SISL) and Mineral-X. 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. My work mostly focuses on the development of trustworthy AI for safety and sustainability domains. Applications include safety validation of cyber-physical systems, decision making under uncertainty for safe subsurface resource planning and operations, and supply chain designs for energy transition commodities.
Academic Appointments
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Research Engineer, Aeronautics and Astronautics
Professional Education
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PhD, Carnegie Mellon University, Mechanical Engineering (2023)
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MSE, University of Michigan, Ann Arbor, Industrial and Operations Engineering (2018)
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BE, Institute Technology of Sepuluh Nopember, Indonesia, Industrial and Systems Engineering (2014)
All Publications
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Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments
IEEE ROBOTICS AND AUTOMATION LETTERS
2025; 10 (2): 1098-1105
View details for DOI 10.1109/LRA.2024.3518087
View details for Web of Science ID 001383062500018
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Optimal Control of Mechanical Ventilators with Learned Respiratory Dynamics
IEEE COMPUTER SOC. 2024: 192-198
View details for DOI 10.1109/CBMS61543.2024.00040
View details for Web of Science ID 001284700700056
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A Survey on Safety-Critical Driving Scenario Generation-A Methodological Perspective
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
2023; 24 (7): 6971-6988
View details for DOI 10.1109/TITS.2023.3259322
View details for Web of Science ID 000966962200001
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An Optimized and Safety-aware Maintenance Framework: A Case Study on Aircraft Engine
IEEE. 2022: 2057-2062
View details for DOI 10.1109/ITSC55140.2022.9922187
View details for Web of Science ID 000934720602011
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Certifiable Evaluation for Autonomous Vehicle Perception Systems using Deep Importance Sampling (Deep IS)
IEEE. 2022: 1736-1742
View details for DOI 10.1109/ITSC55140.2022.9922202
View details for Web of Science ID 000934720601114
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Designing an Optimized Electric Vehicle Charging Station Infrastructure for Urban Area: A Case study from Indonesia
IEEE. 2022: 2812-2817
View details for DOI 10.1109/ITSC55140.2022.9922278
View details for Web of Science ID 000934720602121
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How to Evaluate Proving Grounds for Self-Driving? A Quantitative Approach
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
2021; 22 (9): 5737-5748
View details for DOI 10.1109/TITS.2020.2991757
View details for Web of Science ID 000692209100027
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Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems
MICROTOME PUBLISHING. 2021: 595-+
View details for Web of Science ID 000659893800067
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SAnE: Smart Annotation and Evaluation Tools for Point Cloud Data
IEEE ACCESS
2020; 8: 131848-131858
View details for DOI 10.1109/ACCESS.2020.3009914
View details for Web of Science ID 000552995800001
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Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field
IEEE. 2019: 3974-3980
View details for Web of Science ID 000521238104008
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Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach
IEEE. 2019: 2793-2799
View details for DOI 10.1109/icra.2019.8793619
View details for Web of Science ID 000494942302015
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Evaluation Uncertainty in Data-Driven Self-Driving Testing
IEEE. 2019: 1902-1907
View details for Web of Science ID 000521238101148
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An Accelerated Approach to Safely and Efficiently Test Pre-Production Autonomous Vehicles on Public Streets
IEEE. 2018: 2006–11
View details for Web of Science ID 000457881302002
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Synthesis of Different Autonomous Vehicles Test Approaches
IEEE. 2018: 2000-2005
View details for Web of Science ID 000457881302001
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A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods
IEEE. 2018: 4796-4802
View details for Web of Science ID 000591256604142
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An integrated shipment planning and storage capacity decision under uncertainty A simulation study
INTERNATIONAL JOURNAL OF PHYSICAL DISTRIBUTION & LOGISTICS MANAGEMENT
2015; 45 (9-10): 913-937
View details for DOI 10.1108/IJPDLM-08-2014-0198
View details for Web of Science ID 000369544700005