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ISTD PhD Oral Defense Seminar by Li Xu – In the ever-evolving field of artificial intelligence (AI), deep learning has emerged as a pivotal technique driving remarkable advancements across various domains. Among its many branches, multi-modal learning stands out as a particularly significant approach, which involves integrating and processing information from multiple modalities of data, such as visual content and language information, to enhance the capabilities of AI systems.

ISTD PhD Oral Defence Seminar by Pamela Wang – The thesis will study various degrees of centralisation in cooperation mechanisms, spanning from fully centralised planning-based approaches to fully decentralised communicating agents.

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.

ISTD PhD Oral Defence Seminar by Gionnieve Lim – The thesis investigates the use of various labelling interventions that incorporate automated fact-checking elements for humans, examining people’s perceptions of the labels and their attitudes to the labelled content.

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.

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.

ISTD PhD Oral Defense Seminar by Menglin Li – Social media has become an integral part of daily life, leading to an explosion of social data. Geographical information within social media is essential for applications such as location-based analysis, recommendations, and targeted advertising. However, such information is sparse, prompting the exploration of mining it from social media data.

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.

Congratulations to PhD Student Garbelini Matheus Eduardo for winning the Intel Bug Bounty Award – Information Systems Technology and Design (ISTD)