Events
A multi-modal LLM-augmented deep reinforcement learning framework for adaptive traffic signal control
ISTD Seminar by Maonan Wang – Urban traffic congestion continues to pose severe challenges for sustainable mobility, and adaptive traffic signal control is widely recognized as a key solution.
The OT Security Platform
ISTD COIL Seminar by Jonathan Chin – Operational Technology (OT) and Information Technology (IT) security safeguard devices, networks, and critical systems.
ISTD Industry Engagement Day 2025: Bridging Classrooms and Industry
Information Systems Technology and Design (ISTD) Pillar at SUTD hosted its annual Industry Engagement Day, bringing together ten leading industry and government partners and our talented Computer Science and Design (CSD) students for an inspiring afternoon of discussions, networking, and career insights.
Towards trustworthy and explainable AI for multimodal hate content moderation
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.
Software-hardware co-design for energy-efficient neural network accelerators
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.
Explaining graph-based misinformation detection models
ISTD PhD Oral Defence Seminar by Chin Wai Kit Daniel – Social media and social networking platforms have greatly connected people worldwide and democratised information creation and propagation by facilitating seamless and almost instantaneous information sharing between people and communities.
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.
Detecting concurrent bugs in Go via capture and replay
ISTD Seminar by Martin Sulzmann – Prof Martin will survey the state of the art in the area of controlled concurrency (CCT) testing for Go to explore schedules that reveal panic and blocking bugs, and discuss several new novel aspects such as the ability to perform controlled replay of programs to guarantee that new schedules are actually explored in this seminar.
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.
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.