My work has always revolved around complex data: collecting information from different sources, bringing it together into coherent systems, processing and validating it, and turning it into something useful. Today, much of that work takes the form of data pipelines and automated data systems for products and business processes.

I design and build these systems end to end, from data acquisition and extraction through transformation, enrichment, validation, automation, and delivery. I use conventional algorithms where clear rules are the better tool and AI where interpretation, language, or ambiguity make it useful. The focus is on building systems that are reliable, transparent, and maintainable enough to keep working in real use.

My background in experimental research is an important part of how I got here. Scientific work meant dealing with physiological signals, behavioral data, imaging, and other heterogeneous sources, often combining several types of information within the same project. It also required designing ways to collect and process that data, analyzing whether the results made sense, identifying problems, and communicating complex findings clearly.
That experience still shapes how I work. I tend to break complex problems into manageable stages, make intermediate results inspectable, validate outputs rather than assume they are correct, and think explicitly about edge cases and failure modes. I work directly with clients to understand the problem and decide what is worth building before settling on the technical implementation.

I am based in Vienna and work with companies and teams internationally.

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Beyond work

I grew up in Jerusalem, studied in Tel Aviv, and moved to Vienna in 2015 to join the Center for Brain Research. Vienna has been home ever since. I like that it feels like a real city without being hectic, and that most things simply work. I have always appreciated well-functioning systems, and in that respect Vienna suits me particularly well.

Most of my time outside work is spent with my daughter. She was born very prematurely, and her care depended both on knowledge accumulated over decades and on continuous real-time monitoring. Experiencing that firsthand made the practical value of good data deeply personal.

When I do have time to myself, I enjoy running, live music, and history. I also like figuring out how systems work and explaining them to others. For example, I've designed and run a workshop that uses simple robotics experiments to explain neuroscience to children, using code to show how simple rules can turn into real-world behavior.