I am an AI researcher with Monash University Malaysia and a proprietary research team. My background is applied mathematics, and most of what I do is mathematical modelling in one form or another. Lately that means learning and decision-making, finance, and some environmental systems.

I also work with businesses on data and AI. I am a Business Transformation Consultant with Quandatics, a data and AI consultancy, and a Data Science & Machine Learning Trainer with Quandatics Academy. Before that I did actuarial and finance data work. If that side is what brought you here, see Work With Me.

On paper the research topics are multi-agent reinforcement learning, market microstructure, and quantitative finance. In practice they blur together. The part that interests me is the same in each of them: what the model assumes, what information the agent is given, and how much of the outcome is already decided before any learning happens.

Two questions keep coming back. What information does an agent actually need to act well? And when does a change in regime matter more than a change in method?

I am still building up my foundations in stochastic and environmental modelling, and I am not claiming expertise there yet. I mention it because I want the research record to eventually connect the reinforcement learning and finance work back to more classical modelling, including some current wastewater treatment plant research.

I hold a BSc. (Hons) in Applied Mathematics with Computing from UTAR. I keep a running notebook of derivations and working explanations in statistics, regression, optimization, and reinforcement learning; most of it ends up in the notes section here.