Through Exponential Regression and Logistic Regression, the project predicts how much we can find out about a city, only through the analysis of Street Block Geometry:

The project uses K-Mean clustering for grouping Manhattan's residential buildings with different themes based on 11 different residential dataset.

Using parametric modeling, this project calculates and maps the walking time from each building in Manhattan to its nearest subway station, based on shortest-path analysis of the pedestrian street network.

Thesis proposal for developing an LLM-powered system that automates the querying, analysis, and visualization of NYC's urban datasets, bridging technical geospatial workflows with intuitive natural language interaction.

Architectural design project that redefines Hong Kong's cruciform tower typology through in-depth parametric analysis of building codes, design grammar, and view corridors.

Architectural design project reimagining Hong Kong's podium tower through generative iterations, exploring spiral geometries and walkability analysis.