Year 1 undergraduate at Nanyang Technological University, reading Applied Computing in Finance. Incoming fintech intern building a Regulatory Knowledge Base powered by LLMs and multimodal AI, working over MAS (Monetary Authority of Singapore) regulatory corpora.
I build at the intersection of quantitative finance and applied AI — market microstructure, regulatory intelligence, and ML systems that reason over real financial data. Long-term direction: quantitative research and trading infrastructure.
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Languages
ML / Data
LLM Stack
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Web
Infra
Finance Toolkit
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- HDB Resale Price Predictor — end-to-end pipeline over Singapore HDB transaction data. OLS → Lasso → Random Forest (R² ≈ 0.94), plus SARIMAX for price-level forecasting.
- BoFA — Bond Portfolio Engine — sequential event processing and a domain query engine over dirty price, accrued interest, and PV. Python.
- RSI Strategy Backtester — vectorized backtester with drawdown analysis, equity curve, and signal visualization.
- Crypto Trading Bot — LSTM price models and ML-driven signal generation on OHLCV data.
- GPT-2 Text Generator — transformer generation experiments in PyTorch.
- Local LLM Toolkit — Ollama + Qwen3 + LangChain for private inference; prompt-engineering benchmarks across zero-shot, few-shot, and chain-of-thought.
- CareerDNA (hackathon) — career platform for Singapore fresh graduates with a Career DNA matching system; Next.js + FastAPI, aggregated job data from multiple sources.
- Portfolio — React + JavaScript, Framer Motion, embedded AI chatbot.
- Building LLM + multimodal infrastructure for regulatory document intelligence.
- Deepening into market microstructure, order flow, and execution research.
"The intersection of math, markets, and machines is where the real edge lives."