Jen‐Ming Wu

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About

Jen‐Ming Wu is a leading researcher in real-time systems for autonomous driving, with a focus on the critical challenge of multi-sensor data synchronization. Her work addresses a fundamental bottleneck in autonomous perception: ensuring that data from distributed sensors—such as cameras, LiDAR, and radar—arrives at fusion algorithms with minimal and predictable time disparity. Wu’s most-cited papers, including "Worst-Case Latency Analysis of Message Synchronization in ROS" (2023, 8 citations) and "Modeling and Property Analysis of the Message Synchronization Policy in ROS" (2023, 5 citations), provide the first rigorous formal models for analyzing the worst-case latency of ROS-based synchronization policies. By proving that naive approaches can introduce unbounded delays in V2X and distributed systems, she has established foundational design principles for time-aware sensor fusion. Her work directly impacts the reliability of autonomous systems, where even millisecond-level misalignment can lead to catastrophic perception errors. Wu’s contributions are essential reading for engineers building robust AD stacks, as they bridge the gap between theoretical real-time analysis and practical ROS middleware design.