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Congratulations to Srilalitha Gopalakrishnan, ASD PhD candidate, for her appointment as President of the Singapore Institute of Landscape Architects (SILA), 2021-2023

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 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 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.

ISTD PhD Oral Defence Seminar by Hee Ming Shan – The proliferation of hateful multimodal content, particularly in the form of hateful memes, poses significant threats to online safety and social cohesion. Although deep learning systems, especially vision-language models, are essential to automated multimodal content moderation, they operate as black boxes, offering limited explainability into their decision-making processes.

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 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 – 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 Mark He Huang – Modern AI systems often struggle in physical environments where observations are incomplete, noisy and constantly changing. This seminar presents a feedback-guided representation learning framework that helps AI systems revise what they know, maintain useful representations over time and generate physically plausible outcomes. The work spans machine unlearning, object-centric 3D visual memory and simulator-guided video generation, with the broader aim of enabling more reliable reasoning in the dynamic physical world.