ISTD PhD Oral Defence Seminar by Wei Han – This thesis, aims to provide practical solutions towards basic issues such as high computational costs of multimodal learning, and giving practical solutions to each of them.

Congratulations to PhD student Hee Ming Shan for obtaining SDSC Dissertion Research Fellowship 2023

Congratulations to Srilalitha Gopalakrishnan, ASD PhD candidate, for her appointment as President of the Singapore Institute of Landscape Architects (SILA), 2021-2023

ISTD PhD Oral Defence Seminar by Teo Tzu Hsuan Christopher – In this dissertation, we make important contributions in improving fairness in generative models by identifying and addressing constraints which may limit their broader adoption.

Congratulations to Associate Professor Liu Xiaogang’s PhD Student in Winning the Best Oral Presentation Award

ISTD PhD Oral Defence Seminar by Zhu Lanyun – Semantic segmentation is a fundamental and important task in computer vision, which aims to classify each pixel in an image. The rapid development of deep learning has significantly advanced semantic segmentation and improved the accuracy, promoting its application in fields with high accuracy requirements for pixel-level prediction, such as autonomous driving and medical diagnosis. Current works for semantic segmentation are typically based on a standard setup that all data is accessible beforehand and can be learned simultaneously.

The award is the highest government award granted by the Chinese government to Chinese doctoral students who study overseas as well as postdoctoral researchers who conduct research and have received doctorates overseas.

ISTD PhD Oral Defence Seminar by Hu Zhiqiang – This thesis advances the computational understanding and manipulation of text styles
through three interconnected pillars: (1) Text Style Transfer (TST); (2) Authorship Attribution (AA); and (3) Authorship Verification (AV), determining whether two texts share the same authorship.

ISTD PhD Oral Defence Seminar by Dai Siyang – Urbanisation has given rise to increasingly complex systems that sustain and manage the lives of urban residents. As populations grow, the challenges of managing traffic, overcrowding, and environmental challenges escalate. These complexities place stress on urban ecosystems, demanding innovative solutions to ensure smooth and sustainable operations.

ISTD PhD Oral Defence Seminar by Duo Peng – In this paper, we investigate Diffusion-Based Domain Adaptation, leveraging emerging
diffusion models to address domain adaptation tasks. The motivation behind our research stems from the powerful distribution transformation capabilities of diffusion models, which we aim to harness to help AI models adapt to new data distributions.