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.

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 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 Sai Sathiesh Rajan – This dissertation serves to remind us of the importance of thoroughly testing machine learning models before deploying them as they can cause societal, economical and reputational damage.

ISTD PhD Oral Defence Seminar by Jan Melechovsky – In the modern era, new technology is opening opportunities to help various groups of people around the world. In this thesis, deep learning and audio processing is utilized to target the needs of and develop specific applications for patients with progressive neurological diseases, speakers of non-native English accents, and amateur and leisure musicians and music enjoyers.

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.

ISTD PhD Oral Defence Seminar by Zeng Guangtao – Language models are foundational to modern artificial intelligence, but their development is often constrained by challenges in efficiency, controllability, and reasoning. In this thesis, we aim to address these limitations by introducing advanced paradigms at both the pre-training and post-training stages.

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

Liu Guangxin, received the Best Flash Talk Award at Advanced Photonics: The Intelligent Photonics Forum, held in Foshan, China, from 7 to 9 November 2025. His presentation, Deep Learning-Driven Quantum Nanophotonic Systems, was recognized for its excellence and innovation.