Hi all! I’m DYK👋🏻

About me

I study one sociotechnical question: how algorithmic and decentralized governance systems shape who participates, who is represented, and who is excluded — across accessibility audits of administrative platforms, empirical analysis of Korean pension fund proxy voting, and DAO governance measurement.

Going forward, I want to develop research that is both technically grounded and socially informed: combining empirical audit, causal inference, interpretable machine learning, and mechanism design to produce insights that matter for practice and policy.

Positions

I served as a Junior Ombudsperson at the Human Rights Center, Seoul National University, where I led an end-to-end accessibility audit of a university administration portal for students with disabilities — work recognized as an Outstanding Project with institution-level impact. This experience anchors my commitment to human-centered, participatory, and real-world deployment of technology.

In parallel, I am an M.S. student in Finance (Business Administration) at Seoul National University. Alongside coursework, I conduct interdisciplinary research bridging computer science and economics, including (i) constructing an alternative index for the Korean bond market using issuance and secondary trading data (AXI project), (ii) analyzing DAO governance and voting outcomes (published in Management Science), and (iii) developing interpretability visualizations for deep-learning models used in corporate M&A decisions.

Before this, I earned a B.S. in Computer Science & Engineering from Ewha Womans University, where my first accessibility project — building a music-coding iOS application for blind children at the HCI Lab — set the trajectory for my current sociotechnical work. I have also gained applied ML engineering experience across academia and industry, including an internship at NEXTLab (deploying on-device object detection with TensorFlow Lite and OpenCV) and end-to-end ML systems work.

Methods I use, and methods I want to learn

My current toolkit centers on empirical audit, causal inference on financial-market panel data, interpretable ML for high-stakes decisions, and on-chain data pipelines — collection, wallet labeling, and DuckDB/Parquet infrastructure that holds up at scale. Going forward, I want to keep building research-grade data systems: the kind where a hard question gets both the method and the data foundation that actually fit it.

Personal Interest

I’ve loved soccer as a player—and sometimes as a mobile-game athlete—so I joined the women’s soccer club right after I enrolled and stayed active until COVID-19.

During most of my twenties, I taught at a private coding academy. My students ranged from age four (double-clicking as a life skill) to age twenty (surviving the CS boom with dignity).