Research Projects

Development of novel machine learning algorithms for leveraging various grades of air quality sensors (with different noise assumptions) and accurately inferring a spatially fine-grained air quality map of a megacity is attempted. Implementation of novel fusion techniques to leverage multi-modal data from sources including, but not limited to: satellite retrievals, traffic data, high-resolution (spatial and temporal) meteorological data, population density, geophysical variables (e.g. distance to road, land use pattern, non-vehicle point sources, etc. Implementation of data-driven air quality forecasting models leveraging various meteorological data, and advances in machine learning