Projects

This section is dedicated to showcasing the wide range of computational techniques and tools I employ to analyze, model, and visualize urban systems and phenomena.

 From computer vision and remote sensing applications to natural language processing and spatial statistics, I leverage various technologies and algorithms to extract meaningful insights from vast amounts of urban data. By harnessing the power of machine learning, GIS, and other computational methods, I aim to uncover hidden patterns, predict future trends, and inform data-driven decision-making in urban contexts.

 Through a series of projects and case studies, I demonstrate how computational approaches can be applied to diverse urban challenges, from understanding the spatial distribution of urban growth and inequality to optimizing transportation networks and predicting the impacts of climate change on cities. 

Localization

Computer Vision

Remote Sensing

Natural Language Processing

Time Series Forecasting

Global

Local

Spatial Regression and multivariate relationships