See through the
hype and noise.
Emerging AI technologies promise a lot, but turning innovative ideas into practical solutions is often harder than it seems.
I explore what’s possible, understand the limitations, and experiment with different approaches to find what actually works in the real world.
Building tools for a better future.
Throughout history, human ingenuity has created tools that extend our abilities, solve problems, and improve lives.
Artificial intelligence has the potential to accelerate that progress, opening up possibilities we could only imagine before.
The challenge is turning those possibilities into something useful. Let’s explore what we can build together.
Understand
Identify the real problems worth solving. Understand the challenges, their underlying causes, and the solutions that already exist.
Explore
Investigate different approaches. Adapt existing technologies where possible, or experiment with new methods when conventional solutions fall short.
Build
Turn promising ideas into practical solutions. Test them in real-world conditions, learn from the results, and continuously refine and improve.
About
From computational research to practical AI.
With a PhD in Electrical and Electronic Engineering, my research focused on computational modelling of electromagnetic fields and the simulation of optical devices. Along the way, I also explored parallel computing and CUDA, gaining knowledge that has become increasingly relevant as AI development accelerates.
That technical foundation continues to shape an interest in emerging AI research — understanding how different technologies work, following their development, and experimenting with ways to apply them beyond the laboratory.
Current work focuses on applying AI techniques to real-world supply chain challenges. Every business operates differently, with its own processes, constraints, and requirements. Effective solutions must therefore be built around the actual needs of the business, rather than forcing businesses to adapt to the technology.
With the rapid advancement of large language models (LLMs), developing customised software and experimenting with new solutions has become significantly faster and more accessible. Ideas that once required substantial development resources can now be prototyped, tested, and refined in a fraction of the time.
This presents an exciting opportunity to rethink existing workflows, explore new possibilities, and make the transition towards an AI-assisted future.