About Omega Makena Mwebia

I work at the intersection of quantitative modelling, machine learning, and complex systems.

My professional work involves building quantitative and machine-learning models, primarily in F#, for problems where statistical structure, uncertainty, and changing data matter.

Alongside this work, I conduct independent research into how structure can be discovered and used when data is limited, noisy, or continuously changing. This includes Scarcity, my ongoing research programme on relationship discovery and learning under data scarcity, as well as quantitative research into financial markets and systematic modelling.

I am particularly interested in problems where the obvious solution is to use more data or a larger model, but where the more interesting question is whether the underlying structure of the system can be understood first.

My research interests include:

• quantitative finance and market microstructure;
• financial and macroeconomic modelling;
• time-series and statistical machine learning;
• relationship and structure discovery;
• dynamic and evolving systems;
• causal and predictive modelling;
• learning under data and compute constraints.

I build systems to investigate these questions, but I am more interested in what the experiments establish than in how large or sophisticated the system appears.