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Shipra Agrawal’s research spans several areas of optimization and machine learning, including data-driven optimization under partial, uncertain, and online inputs, and related concepts in learning, namely multi-armed bandits, online learning, and reinforcement learning. She is also interested in prediction markets and game theory. Application areas of her interests include internet advertising, recommendation systems, revenue management, and resource allocation problems.
In the uncertain and dynamic environment of modern businesses, decision makers are often faced with the challenge of making decisions that are not just good or profitable for today, but also put them into better position to face the constraints and uncertainties of tomorrow. In order to achieve this, the decision maker needs to utilize the past observations and data to understand the nature of uncertainties, explore different choices in order to gather useful data for future, and optimize for the long term goals. Agrawal’s research combines optimization and learning techniques to design globally optimal decision-making schemes for such complex, uncertain environments. Her work is highly interdisciplinary, and lies on the intersection of operations research and data science, spanning the areas of optimization, machine learning, algorithm design, and game theory.
Agrawal received a PhD in Computer Science from Stanford University in June 2011, and was a researcher at Microsoft Research India from July 2011 to August 2015. She is a member of ACM Future of Computing Academy and serves as an associate editor for Management Science journal.