About

The three-minute version

Show a computer a photo of a wedding in Ohio and it will tell you about the dress, the cake, the first dance. Show it a Barong dance in Bali and ask which figure represents good, and it will still answer; fluently, immediately, and often wrongly. It has learned what confident text looks like, not what it is looking at.

This happens because the models are trained on whatever was on the internet, and the internet is not the world. Roughly speaking, the machine has seen a million Ohio weddings and a handful of Barong dances.

My work is to prove that precisely enough that it can be fixed. We build tests. The one I care most about, Seeing Culture, does something most tests do not: it makes the model show its work. First answer the question about the artifact; then point to that artifact in the picture. A model that names the right thing while circling the wrong part of the image did not know the answer; it guessed well. Ordinary accuracy scores cannot tell those two apart. Ours can.

The result is not subtle. On our benchmark GPT-o3 answers 91% of across-culture questions correctly, and yet its grounding never rises above 32.5 mean IoU. It says the right word and points at the wrong thing.

How I got here

I grew up in Bursa, in northwest Türkiye, and studied electronics engineering at Uludağ University, with exchange semesters in Siena and Naples. After that a software engineering traineeship in Valencia, a master’s back in Bursa on vehicle detection, a year as a machine learning engineer at Turkish Technic in İstanbul, and mentoring at a developer festival in London.

Since 2020 I have been in Singapore: five years at NTU and A*STAR’s Institute for Infocomm Research on a SINGA scholarship, with three months at the University of Bristol in Dima Damen’s group, and since March 2025 a Research Scientist at SMU.

Bursa → Siena and Naples → Valencia → Bursa → İstanbul → London → Singapore → Bristol → Singapore.

I am Turkish, I live in Singapore, and I work on Southeast Asia. I am an outsider to every culture in the Seeing Culture benchmark, which is why it was built with people who are not: the co-authors, students and annotators from the countries in it. Cultural AI built only by people who visited is the problem, not the fix.

What I think the field should do

This is the checklist that I would argue for:

1. Source data from the region, with the people of the region. Representation is a data collection decision, not a fine-tuning trick.
2. Test concepts, not translations. If the concept inventory is Western, translating the language changes nothing about what is measured.
3. Demand evidence, not just answers. Grounding or segmentation separates understanding from lucky guessing.
4. Report the gap, not the average. A single accuracy number hides exactly the disparity that matters.
5. Treat cultural knowledge and cultural perception as different problems. Fixing one does not fix the other.

Elsewhere

I run Turquoise Dot, a meetup for Turkish and Turkic researchers and tech professionals in Singapore, and I keep a list of cultural VLM resources current as the field moves.

If any of this is useful to you, email me or book a 30-minute chat.