Research
NaraQ treats research as part of engineering, not separate from it. The methods we develop — in machine learning, optimisation, and computational finance — are built to be tested, verified, and put to work in real systems.
Our research is applied rather than speculative. We are interested in results that hold up under scrutiny and remain useful over time.
Where we focus
Learning systems
Machine learning methods built for reliability and interpretability, not just benchmark performance.
Optimisation and decision-making
Algorithms for allocation and decision problems where structure and constraints matter.
Computational finance
Quantitative methods for market analysis, risk, and portfolio construction.
We publish as work matures.
As our research produces results worth sharing, they will appear here.