Training in computer science and international economics

My route into research runs through both computing and economics. I completed a Bachelor of Computer Science with Honours at Universitas Gadjah Mada, where coursework in machine learning, information retrieval and data mining led to an undergraduate thesis on opinion mining: I collected and labelled English-language Airbnb reviews and implemented TF–IDF weighting and a Naive Bayes classifier myself, rather than calling a library estimator.

I then worked for three years as a software engineer in Yogyakarta, building Python and SQL ETL pipelines on an ERP platform, integrating point-of-sale data across modules and standardising the schemas that joined them. With the support of the Stipendium Hungaricum Scholarship, I went on to an MSc in International Economy and Business at Corvinus University of Budapest, in the International Economic Analysis specialisation, where I wrote a master’s thesis using cross-country panel data to study the correlates of internet adoption.

Alongside and after my master’s studies I have worked in applied data roles — first as a part-time data analyst in Budapest, and currently developing a content-recommendation system in Indonesia. I am now preparing for doctoral study. My master’s thesis could establish associations but not causal effects; the direction I want to pursue is how causal machine learning and robust, distribution-shift-aware methods can produce more credible evidence on how digital technologies — AI in particular — are adopted, and what they change.