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ISTD PhD Oral Defence Seminar by Ng Xun Long – Behaviour analysis in complex environments
ISTD PhD Oral Defence Seminar by Ng Xun Long – The thesis presents Chaotic World, a large-scale multi-modal dataset with fine-grained annotations of human actions, interactions, and sounds in chaotic situations.
ISTD PhD Oral Defence Seminar by Pamela Wang – Guided cooperation for multi-agent teams
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 Jiaxi Li – Shaping inductive bias in foundation models: A signal-centric perspective on reasoning and learning dynamics
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 student Zhao Yunqing received Chinese Government Award for Outstanding Self-financed Students Abroad
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.
Liu Guangxin, PhD student under the supervision of Associate Professor Wu Lin, has received the Best Flash Talk Award.
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.
ISTD PhD Oral Defence Seminar by Hu Zhiqiang – Learning text styles: a study on transfer, attribution, and verification
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 Sai Sathiesh Rajan – Leveraging out of distribution testing to build robust machine learning systems
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 – Analysis and synthesis of audio with AI: from neurological disease to accented speech and music
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 Zeng Guangtao – Beyond scale: efficient pre-training and controllable post-training for language models
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.
ISTD PhD Oral Defence Seminar by Tan Yu Xiang – Context-aware perception in adverse conditions
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.