My research focuses on developing machine learning-aided causal inference methods for addressing complex, high-stakes challenges prevalent in healthcare and public health. My collaborators and I have used my research to address challenges in healthcare, public health, and social sciences. Decision-making in these critical domains is fraught with difficulties stemming from but not limited to the intricate interplay of factors, including the heterogeneity of causal effects across subpopulations, the substantial costs associated with suboptimal decisions, and the inherent complexities in the available data, all of which complicate the assessment of risk-benefit tradeoffs. In pursuit of more effective solutions, my work is centered around the development of causal inference methodologies that are: (i) Accurate, (ii) Trustworthy, and (iii) Domain-conscious

Research Summary

I specialize in advancing methodologies for causal inference, particularly within the complex landscape of healthcare and public health. My work develops methodologies that are:

• Accurate - to ensure accurate estimation of heterogeneous causal effects even when confronted with limited data, offering decision-makers a reliable foundation upon which to base their choices.

• Trustworthy - to empower domain experts to comprehend the inner workings of the causal inference process. This not only enables experts to validate the underlying assumptions but also guarantees stakeholders' safety.

• Domain-conscious - to bridge the research-to-practice gap and yield solutions that are readily implementable. I leverage the context and domain knowledge to tailor solutions specific to a subject matter.

I firmly believe that the most impactful and implementable contributions arise when methodological advancements are deeply rooted in the relevant context. 

Work Experience

Johns Hopkins Bloomberg School of Public Health

Meta

Amazon

Amazon

The Urban Institute

IBM India Research Laboratory

Arista Networks