Shipra Agrawal

Shipra Agrawal is an Associate Professor at the Department of Industrial Engineering and Operations Research, Columbia University, with affiliations to the Data Science Institute and the Department of Computer Science. Her research bridges optimization and machine learning, focusing on decision-making in uncertain environments.

• PhD in Computer Science from Stanford University (2011)
• Researcher at Microsoft Research India (2011–2015)

Her work addresses online optimization, reinforcement learning, and game theory, aiming to develop algorithms that balance exploration and exploitation for long-term goals. Applications include internet advertising, revenue management, and resource allocation.

Recent publications examine dynamic pricing models, regret bounds in reinforcement learning, and convex knapsack optimization. Her research has been supported by NSF CAREER, Google Faculty Research, and Amazon Research Awards.

• NSF CAREER Award CMMI-1846792 (2019)
• Google Faculty Research Award (2017)
• Amazon Research Award (2017)

She has advised PhD students who now hold positions at institutions like Google DeepMind, Amazon, and Facebook. Agrawal serves as an associate editor for Management Science, INFORMS Journal on Optimization, and Journal of Machine Learning Research, and co-chaired major conferences such as COLT 2024 and AISTATS 2025.