Sponsored by: Computer Science Department
Intended Audience(s): Public
Categories: Lectures & Seminars
Abstract: As machine learning methods become embedded in society, it has become clear that the data used, objectives selected, and questions we ask are all critical. My work looks at data and machine learning from a public health and equity lens. First, this motivates the design and development of data mining and machine learning methods to address challenges related to data and goals of public health, such as generating better hyper-local features to represent environmental attributes addressing challenges of sparsity, irregularity and representativeness of data. Second, principles of community and equity inspire innovations in machine learning. In this realm my work has leveraged causal models and machine learning to address realistic challenges of data collection and model use across environments, such as domain adaptation that improves prediction in under-represented populati