ISTD PhD Oral Defence Seminar by Hong Pengfei – This thesis addresses critical questions surrounding the evaluation and enhancement of reasoning robustness, generalisability, and comprehensiveness in modern language models, particularly under realistic conditions involving noise, ambiguity, domain shifts, and multimodal inputs.

ISTD PhD Oral Defence Seminar by Ong Kian Eng – Understanding and analysing animal behaviours is crucial for gaining profound insights into the health, needs, and overall well-being of the animal. This involves measuring and monitoring factors such as size, growth, poses, and actions. The analysis of animal behaviour holds significant importance in a wide range of domains and industries, such as livestock farming, veterinary sciences, scientific research, ecological and conservation studies.

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.

Congratulations to Associate Professor Liu Xiaogang’s PhD Student in winning the Royal Society of Chemistry (RSC) Excellent Student Award

ISTD PhD Oral Defence Seminar by Wang Jiazhao – The increase of the Internet of Things (IoT) has created a complex and heterogeneous wireless ecosystem, demanding IoT gateways that are both flexible and efficient. While Software Defined Radio (SDR) provides the necessary hardware adaptability, its potential is frequently undermined by significant software implementation challenges, including a lack of portability across platforms, prohibitive design complexity for advanced algorithms, and poor computational efficiency. This thesis posits that these persistent bottlenecks can be overcome by a paradigm shift in physical layer (PHY) design: reframing core communication functionalities as learnable, interpretable neural network (NN) models.

ISTD PhD Oral Defence Seminar by Yeo Shun Yi – In this dissertation, PhD candidate Yeo Shun Yi will examine how reflection can be systematically supported through interface interventions to enhance the deliberative quality of user contributions.

ISTD PhD Oral Defence Seminar by Chia Yew Ken – This thesis investigates the crucial role of structured information in natural language processing and artificial intelligence, with a focus on its extraction, utilisation, and extension to multimodal reasoning.

ISTD PhD Oral Defence Seminar by Bhardwaj Rishabh – This thesis investigates critical non-idealities in AI systems, focusing on safety behaviour post-training and alignment.

ISTD PhD Oral Defence Seminar by Perry Lam – Neural networks are known to be over-parametrised and sparse models have been shown to perform as well as dense models over a range of image and language processing tasks. However, while compact representations and model compression methods have been applied to speech tasks, sparsification techniques have rarely been used on text-to-speech (TTS) models. We seek to characterise the impact of selected sparse techniques on the performance and model complexity.

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