I was honoured to be among the students featured by the NTU Graduate College.

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Predictive coding and related world-model-based approaches offer a powerful framework for understanding how embodied systems can infer, predict, and adapt. My current work explores these ideas through models of sense of agency, internal representations, and uncertainty in embodied and AI systems.

Autonomous vehicles depend on tightly coupled sensing, learning, and control components. My work in this area examines how vulnerabilities in perception systems, especially LiDAR-based 3D object detection, can be exploited under adverse conditions such as rain, and how these weaknesses affect the safety and robustness of autonomous systems.

My earlier work explored short-range radar sensing for hand gesture recognition, using micro-Doppler signatures to classify subtle motion patterns. This experience contributed to my broader interest in sensing, signal interpretation, and machine perception.

I also worked on Synthetic Aperture Radar (SAR), including image analysis and degradation-related problems in remote sensing. This helped shape my interest in how sensing systems represent and interpret complex real-world signals under imperfect conditions.

This thesis studies how adverse weather and adversarial attacks interact to undermine LiDAR-based perception in autonomous vehicles, with a focus on minimal-point ghost-object insertion, object hiding, and defense failure under rain.