Over the last few years, I've become increasingly interested in the places where mathematics, computation and the physical world overlap. Mathematics is the part I never get tired of — the moment an equation turns out to describe something real still feels a little like magic. Statistics and production engineering were my way into that world, particularly through problems where uncertainty, complex systems and imperfect information have real consequences. But the more I learn, the less interested I am in staying inside one discipline.
Markets are one of my favourite of those problems. Fintech and quantitative finance are where probability stops being an exercise and starts costing or saving real money: pricing risk, reading behaviour in noisy data, deciding under uncertainty with a clock running. It's why I founded StockAI, where every purchase order is a bet on demand placed with real working capital.
Lately, a lot of that curiosity has been moving toward bioinformatics, genetics and computational biology. There's something fascinating about treating biological systems as information systems: sequences as data, evolution as a process that leaves statistical traces, and genomes as enormous structures that we can only begin to understand by combining algorithms, probability and computation. I'm especially interested in the computational side of genetics — how relatively simple algorithms and statistical ideas can be used to extract structure from biological data at a scale where intuition alone stops being useful.
That interest has also pulled me toward neuroscience, brain-computer interfaces and the broader question of how biological systems encode information. I find myself naturally moving between subjects that, on the surface, have very little to do with each other: mathematics, statistics, machine learning, genetics, neuroscience, physics, finance and complex systems. What connects them for me is the same underlying question: how can we use mathematics and computation to understand systems that are too complex to understand by looking at them directly?
I like learning by going down the rabbit hole. If something catches my attention, I want to understand not only how to use it, but why it works, what assumptions it makes, where it breaks, and what exists underneath it. That is probably why I've always been more drawn to fields where there is still a lot left to figure out.
I'm not particularly interested in collecting technologies or disciplines for the sake of having them on a résumé. I'm interested in understanding things deeply enough that I can eventually connect ideas that normally live in separate worlds.
Right now, bioinformatics and fintech are the worlds I'm most curious to explore — and I want to be among the people building the technology the next decades will run on.