Events

ISTD PhD Oral Defence Seminar by Loo Yi – How can AI agents and robots adapt to new human partners when collaborative data is limited? This thesis explores methods that help them learn preferences and work effectively with people across a range of tasks.

ISTD PhD Oral Defence Seminar by Yao Xiao – Adapting large language models to downstream tasks and to human preferences has become the dominant cost in the model development pipeline. This thesis argues that the binding constraint in adaptation is not the volume of parameters, data, or computation available, but the fraction of that budget which carries usable learning signal – and that this holds in both parameterisation and post-training data.

ISTD PhD Oral Defence Seminar by Zhengbo Zhang – Video content discovery and editing are two core tasks in computer vision. Discovery localises an object and its keypoints in each frame and tracks the object across frames, whereas editing modifies the discovered content according to the user’s intent. Deep learning has advanced both tasks and enabled their application in visual surveillance, content creation, and film production.

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.

ISTD PhD Oral Defence Seminar by Wang Tianduo – This thesis studies how large language models (LLMs) generalise under distribution shift.

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 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.

The SUTD’s Master of Architecture (MArch) programme offers a future-forward professional degree programme, highlighting design and research for sustainability and the digital transformation of the architectural profession. The programme launches the careers of leaders in architecture by emphasizing independent critical thinking in a thesis project, supported by close collaborations with faculty on cutting-edge research.

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.

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.