Projects.

A pipeline that transforms unstructured corporate sustainability reports into structured, traceable, and auditable evidence. The system extracts disclosure claims from multimodal reporting materials, links each claim back to its source passage, and prepares the data for downstream consistency checking, greenwashing-gap detection, and empirical analysis — combining retrieval-augmented generation with schema-based ESG framework mapping (GRI, SASB, AASB S1/S2).

A cross-border sustainability-compliance verification tool for Chinese exporters navigating divergent regulatory regimes — including the EU (CBAM, Battery Regulation), Australia (AASB S2 / ASRS), and Hong Kong (HKEX, ISSB-aligned). Carbon Chain helps firms map their disclosures against each jurisdiction's requirements and surface compliance gaps before they become trade barriers. Entered in the China International College Students’ Innovation Competition.

An end-to-end machine-learning pipeline that predicts corporate credit risk for Chinese A-share firms from 18 firm-level features (financial ratios, market signals, and ESG scores) and explains the model through a four-layer SHAP analysis, built on a 42,108 firm-year panel (5,423 firms, 2015–2025). The target is the Merton/KMV distance-to-default rather than an accounting Z-score, which leaves room to test, by firm-grouped ablation, whether ESG carries incremental information about credit risk. Test R² reaches 0.683, and next-year distress classification reaches ROC-AUC 0.923.

An end-to-end multi-factor stock-selection and backtesting pipeline that scores the equity cross-section from eight factors (value, quality, momentum, liquidity, size, and three risk factors), combines them two ways (an IC-weighted linear composite and a LightGBM walk-forward model), and evaluates everything with rank-IC, ICIR, factor decay, and a monthly-rebalanced quantile backtest against a market benchmark. It runs on a China A-share panel of 498,893 stock-months across 5,546 firms (2015–2025, delisted firms included), with strict point-in-time discipline so no factor uses data the market had not yet seen.

A reproducible conservation-analytics study of how Chinese White Dolphin occurrence and core habitat changed across 2012–2022 in the western waters of Hong Kong, and how that change aligns with large-scale reclamation for the Hong Kong–Zhuhai–Macau Bridge and the airport's Three-Runway System. It turns AFCD marine-mammal monitoring and EPD water-quality archives into a single model-ready panel, then runs an effort-controlled trend model, water-quality diagnostics, and spatial habitat-shift maps. After controlling for survey effort, the on-effort sighting rate fell about 12.4% per year, while the 50% core-use area contracted from 45 to 26 km² and shifted southwest, away from the reclamation front.