Data scientist focused on multivariate time series forecasting, reinforcement learning, and agentic AI with Graph RAG. I work at the intersection of theory and practice: I have industry experience in market research and data analysis, and I like to take ideas from research and put them to work. I care about getting things right. I'm curious, I learn by doing, and I bring steady enthusiasm to the work.
Selected Publications & Projects
FinAI @ ICLR 2026 — financial AI workshop (Rio, April 2026).
Forthcoming book on LangChain and LangGraph (Wikidocs).
Climate visualization for South Korea (GitHub Page).
Work Experience
Blog
Can we really get alpha from market data?
Efficient Market Hypothesis, Micro Alphas, and why probabilistic forecasting matters for turning signals into positions.
What works for forecasting macro economic series with deep learning?
Data quirks of macro series, which model families work (and which don’t), and why it’s rarely one-size-fits-all.
Could multivariate time series have their own representations?
Identifiable innovations, diagonal dynamics, and iVDFM: factor recovery, interventions, and probabilistic forecasting.
Can we make a more risk-aware portfolio agent from utility theory?
Recursive (Epstein–Zin) utility with Monte Carlo certainty equivalents in PPO/A2C, on Korean ETF splits.
Effective Bird Sound Classification
Mel spectrograms and EfficientNet for bird sound: why the mel scale helps and how to keep the pipeline simple.
Creating and Evaluating Synthetic Tabular Data
Sequential synthesis for tabular data, plus three checks: propensity scores, CI overlap, and quasi-identifier risk.