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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Published

• 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