Research Project Title:
Optimizing Low-Cost Air Pollution Sensor Networks within Urban Environments
abstract:With growing urbanization, the associated air pollution continues to be a threat to societies and a leading cause of disease, death and climate change. Without comprehensive systems of data collection, the gravity of air pollution cannot be accurately measured, thus limiting the development of mitigation tactics against this phenomenon. The optimized distribution of sensors in cities provide higher resolution data of pollutant levels, helping further reveal major causes. Leveraging the linear regression of Gaussian processes and the improved prediction of mutual information, we will use London's sensor network data and develop models to determine the optimal placement of existing sensors within this network, then expand these to reveal the air pollution levels of a variety of pollutants.
By participating in this SuperUROP, I hope to gain extensive research experience that combines my interests in environmental sciences and urban planning, while also applying the lessons learned throughout my CS courses at MIT. My previous UROP experiences involving applied ML to environmental resiliency, along with my classes in algorithms, ML and modeling, have prepared me to tackle this SuperUROP's environmentally-focused optimization problem.