MSc Information Studies · Nanyang Technological University
BSc Psychology & Language Sciences · University College London
AI can already discover new mathematics on its own, yet stalls on questions of policy and society. I study that gap — and how fast, hard-to-fake validators for slow-verification domains can close it, keeping AI progress both genuine and trustworthy.
My work spans computational social science, AI self-improvement, and governance design. One thread runs through all of it: in domains where outcomes are slow and hard to verify, how do we build the fast validators — and the institutions — that keep AI progress both genuine and trustworthy? The three directions below move from describing human–AI interaction, to the methods that let AI make real progress in such domains, to designing institutions around it.
Empirical and computational study of how people interact with AI systems, and how those interactions aggregate into social-scale dynamics. Spans mixed-methods behavioural experiments (N = 86) on autonomy, trust, and delegation; NLP analysis of cross-cultural AI sentiment; and dynamical-systems modelling of how AI controversies propagate through opinion networks.
When can AI improve itself through genuine discovery rather than mere self-elicitation? Auto-research and recursive self-improvement deliver real gains only where fast, hard-to-fake validators exist (mathematics, code). I study how to sharpen slow-validation domains by building fast surrogate validators — agent-based modelling, mathematical and dynamical-systems modelling, structural invariants — which makes bold hypothesis generation safe again and yields the negative examples needed to train auto-research taste.
Turning sharpened validators into governance: designing accountable institutions for AI-enabled public resource allocation, grounded in mechanism design, Bayesian Truth Serum, and agent-based stress-testing. Targets two pathologies of autonomous delegation — moral responsibility diffusion and delegation blindspots.
Reverse chronological order. Full CV →
Comparison of hand tracking-based and controller-based interaction in a consumer virtual reality game
A controlled VR study comparing two interaction modalities — hand tracking versus traditional controllers — in a consumer game setting. Contributes empirical evidence on how input modality shapes user behaviour, presence, and performance.
Stability of AI Governance Systems: A Coupled Dynamics Model of Public Trust and Social Disruptions
Derives closed-form spectral stability conditions (ρ(J2n) < 1) for a bidirectionally coupled Hawkes–Friedkin–Johnsen trust dynamics system. Demonstrates irreversible governance collapse under echo-chamber topologies and identifies tipping-point thresholds for institutional intervention.
Can freedom lead to trust & satisfy? How user autonomy influences trust perception in LLM-powered conversational agents
A mixed-methods study (N = 86) challenging standard assumptions about user autonomy and trust in human–AI interaction. Employs formal SEM/CFA latent-variable modelling to link AI system design choices to delegation and adoption outcomes.
The Disinformation Campaigns about the Israel-Palestine Conflict in the Digital Era
Analyses the systemic impact of generative AI on state-sponsored computational propaganda through the lens of Strategic Narrative Warfare theory. Awarded full marks as MSc Critical Inquiry Project; full paper available as a writing sample below.
Research, policy, and industry roles across Singapore, the UK, and China. Full CV →
under Dr. William Tjhi
PhD-level Mathematical Economics
under Prof. Yvonne Rogers (UCLIC Director)
Open-source research code and tools.
A daily human–AI brainstorm loop. Each morning it proposes four research ideas (high-fit, adjacent-stretch, contrarian, and random mutation), learns your taste from how you re-rank and comment on them, and consolidates durable lessons into a versioned “taste skill.” Model-agnostic, with built-in novelty and anti-repetition guards and an optional self-evolution step gated by held-out backtests. The asset is the append-only feedback log that compounds over months.
I hold an MSc in Information Studies from Nanyang Technological University and a BSc in Psychology & Language Sciences from University College London. My research sits at the intersection of computational social science, mechanism design, and AI governance.
My work combines formal modelling with behavioural experiments: I build coupled dynamical systems to characterise how AI controversies erode public trust, and I run empirical studies to understand how people delegate to — and hold accountable — AI-powered agents. I am also developing governance frameworks that address moral responsibility diffusion in AI-enabled public resource allocation.
I am currently a Research Assistant at AI Singapore (under Dr. William Tjhi), building human-governed extension layers and auditable evaluation workflows for policy simulation. I have previously conducted research at the UCL Interaction Centre (under Prof. Yvonne Rogers), NTU School of Economics, and the China Development Research Foundation, with industry experience in machine learning deployment and quantitative modelling.
Formal & Quantitative Methods
AI Evaluation & Governance
Empirical & Mixed Methods
Machine Learning & Programming
Aug. 2024 – Jul. 2025
MSc Information Studies
Nanyang Technological University
Singapore
Sep. 2021 – Jun. 2024
BSc Psychology & Language Sciences
University College London
London, United Kingdom
Happy to discuss research ideas, potential collaborations, or questions about my work.