Search results

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 Dongrui Li – The dissertation investigates four AI accelerator design directions, each validated through tapeout prototypes. Spiking Neural Network (SNN) accelerators are explored with in network computing.

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

ISTD PhD Oral Defence Seminar by Jiaxi Li – Foundation models have demonstrated strong reasoning capabilities, yet their behaviours can vary substantially depending on how learning signals are constructed and exposed during training.

ISTD PhD Oral Defence Seminar by Qi Zhang – Analog-to-digital converters (ADCs) are crucial in signal processing but face challenges when handling high-dynamic-range signals. In radar systems, the coexistence of strong and weak targets can lead to significant information loss due to ADC limitations.

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 Best Oral Presentation 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 Tomomasa Yamasaki – This dissertation proposes an integrated research framework that spans algorithm-level network evaluation, hardware-aware optimisation, and cycle-accurate performance simulation.