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ISTD PhD Oral Defence Seminar by Loo Yi – How can AI agents and robots adapt to new human partners when collaborative data is limited? This thesis explores methods that help them learn preferences and work effectively with people across a range of tasks.

ISTD PhD Oral Defence Seminar by Zhengbo Zhang – Video content discovery and editing are two core tasks in computer vision. Discovery localises an object and its keypoints in each frame and tracks the object across frames, whereas editing modifies the discovered content according to the user’s intent. Deep learning has advanced both tasks and enabled their application in visual surveillance, content creation, and film production.

ISTD PhD student Nan Li has won the Bronze Award at the Student 3MT (Three-Minute Thesis) Pitch Competition which was held as part of the IEEE 6G Summit Singapore 2026.

ISTD PhD Oral Defence Seminar by Mark He Huang – Modern AI systems often struggle in physical environments where observations are incomplete, noisy and constantly changing. This seminar presents a feedback-guided representation learning framework that helps AI systems revise what they know, maintain useful representations over time and generate physically plausible outcomes. The work spans machine unlearning, object-centric 3D visual memory and simulator-guided video generation, with the broader aim of enabling more reliable reasoning in the dynamic physical world.

ISTD PhD Oral Defence Seminar by Tan Yu Xiang – Adverse conditions such as rain or murky water environment, significantly impacts various perception tasks. In rain conditions, the images captured are easily corrupted by both raindrops on the lenses and lens flare. Meanwhile in turbid underwater conditions, the murkiness reduces the contrast and saturation of the image. To tackle these problems, we utilise contextual information to improve robustness of perception algorithms.