I’m a senior researcher at Infobip, where I spend my days figuring out how people actually interact with AI systems and how to make those conversations better.

What I Actually Do

When you chat with an AI assistant that helps you find what you’re looking for or make a decision, someone has to design:

  • how that conversation flows,
  • measure whether it’s actually helpful,
  • and make sure it’s not doing anything weird or unfair.

That’s me.

So my work pretty much boils down into three areas:

  • Conversational recommender systems — Building chat-based, agentic systems with LLMs as their workhorse that help users find things or make decisions through natural back-and-forth dialogue.

  • AI evaluation — Figuring out if these systems actually work. Are they accurate? Reliable? Do they do what we claim they do? How to quantify all of this?

  • Trustworthy AI — Making sure AI systems play fair, explain themselves when needed, and don’t quietly discriminate or leak private data.

How I Got Here

Right after college, I started in academia, researching how electromagnetic fields from phones and antennas interact with the human body. I also taught a ton of courses on wireless systems. This led to earning a PhD in 2023. I developed a faster way to calculate how much RF energy gets absorbed by complex body surfaces.

Then I decided to move away from academia. I switched to retail data science. I led a team that helped supermarket chains across the Balkans figure out what products to stock, how to price them, and where to put them on shelves. Lots of forecasting, lots of ML engineering, lots of spreadsheets, lots of spreadsheets…

I realized I missed doing research — but this time on problems that evolve much more quickly, and where results don’t take years to matter. I wanted to work in a field where ideas move fast, get tested immediately, and are applied at scale. AI seemed perfect: everyone is building, experimenting, and pushing it forward at the same time. That energy and the ability to see research turn into real systems used by real people is what drew me into AI research.

Things I Find Interesting

  • When AI judges other AI (and the biases that creep in)
  • Recommending through conversation instead of item grids
  • How search and retrieval actually work under the hood
  • Predicting things over time (harder than it looks)
  • Working with 3D point clouds, meshes, the whole geometry mess
  • Teaching neural networks to respect physics