The use of big data in the health industry to contribute to advancing the field is not a new practice. But it is one that has been constantly evolving, especially to support evidence-based policy and practice for population-level impact, This also means that special focus on specific groups of the population is essential to understand varied impacts. This women's day, we try to bring to light the overlap of big data and gender studies within the purview of health. With abundant health data in repositories available for use, the practice of making sense of this data through different lenses becomes imperative. Our guest in this episode does this exactly. She looks at big data and how emerging patterns tell us more about the intersection of gender and health. 

Nabamallika Dehingia is a doctoral fellow at the Center for Gender Equity and Health at the University of California, San Diego. Her interests lie in gender equity and health research, the use of machine learning algorithms for applied gender analysis. She is someone who is extremely well-versed in quantitative data analysis, monitoring and evaluation of community-based health interventions. Nabamallika is also interested in using advanced statistical techniques to answer critical questions related to public health.

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