Using Rhino Grasshopper and C#, the 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.

As an extension of Street-Block Urbanity Prediction project, the project predicts urban characteristics by learning relational patterns embedded in street block geometry through Graph Neural Networks.

A parametric city was generated in Grasshopper using street networks, building geometry, and density inputs to produce adaptable urban forms, with solar and view analyses.

Parametric analysis of solar radiation in Bryant Park for 1 year duration, analyzed with Grasshopper + Ladybug and DeCodingSpace plugin.

GeoEstateChat explores how LLMs connects real estate with multi-scale geospatial analysis. It investigates how user intent can mediate between spatial data, analytical workflows, and real-estate reasoning.

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.

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.

A Python package that helps row-wise merging of messy tables reliably. It prepares tables for merging, finds matching columns, suggests merge keys, and explains why merges succeed or fail.