Building quant models, agentic systems, and full-stack tools — one curious question at a time.
“Why? How? What if?”
Taped above my monitor. Applies to a stack trace, a market signal, or an application essay.
Statistics, to make sense of an uncertain world. Code, to put it to use.
Keep problems small, prototype with real data, iterate. Listen before proposing — most solutions fail by solving the wrong problem. I'm comfortable deciding under uncertainty, and changing course when I'm wrong.
Hands-on with data — from understanding the problem to shipping the fix.
Projects that blend statistical intuition, machine learning, and thoughtful product craft.
Ensemble ML signal generation (LightGBM + XGBoost) for the Indonesian Stock Exchange. Walk-forward CV with embargo, SHAP explainability, MetaLabeler for confidence filtering. Backtested on 2020–2024 LQ45 data.
FastText + LSTM classifier for Indonesian-language text, plus a fine-tuned IndoBERT comparison. Research/educational — not a clinical tool.
Currently living in my notes app and coffee chats.
Keeping an agent from drifting over long autonomous runs. I'm testing a self-hosted stack that observes itself and learns from its traces — the hard part is knowing when to trust its judgment.
Most ML systems are tested on data from the same distribution as training. In production — especially markets — that distribution keeps shifting. The real question is detecting it early enough to act.
Most ML tooling assumes English-first data and deep markets. Bahasa Indonesia NLP and the Indonesian equity market mean thin data and constant regime change — constraints that force cleaner thinking.