My research focuses on developing statistical methods and frameworks that help Marketers, Clinicians, and Policy-makers better conduct Randomized Controlled Trials (A/B testing) and also optimally target subjects and treatments in a budget-constrained, heterogeneous treatment effect environment. These questions fall in the line of literature related to Heterogeneous Treatment effects and Multi-Armed Bandits. I use a mix of Statistics, Machine Learning, and Causal Inference to solve the above problems.

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A Prescriptive Analytics Framework for Optimal Deployment using Heterogeneous Treatment effects - Management Information Systems Quarterly With Edward Mcfowland III, Ravi Bapna, Tianshu Sun

In Progress

Trading off regret and rewards in Multi-Armed Bandits With Edward Mcfowland III, Ravi Bapna
Can Social Referrals Outperform Algorithmic Targeting With Ravi Bapna, Colleen Manchester, Gautam Ray, Edward Mcfowland III
The effect of emerging online new media on traditional media consumption. the case of eSports and Traditional Sports