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Dr. Umrawal received a Ph.D. in Industrial Engineering with a focus on Operations Research and a concentration in Computational Engineering from Purdue University. His Ph.D. research involved developing efficient machine learning algorithms for influence maximization (promotional marketing) on social networks.
Dr. Umrawal’s research focuses on the design and analysis of intelligent systems for strategic decision-making, resource allocation, and trustworthy AI. He integrates methods from machine learning, operations research, and causal inference to address challenges in algorithmic marketing (e.g., influence maximization and discount allocation), online learning (e.g., subset selection via combinatorial bandits), mechanism design for digital platforms (e.g., dynamic taxation for attention allocation), and the safety and controllability of generative AI models (e.g., intent-hiding games and causal reasoning in LLMs). His work bridges theoretical rigor with practical impact across marketing analytics, operations strategy, and public policy. His research has been published in venues such as AAAI, UAI, ALT, ICAPS, CDC, CLeaR, VLDB, CIKM, SIGMETRICS, IMPS, IEEE Transactions, and ACM Transactions.
He also holds an MS in Economics from Purdue University and an MS in Statistics from the Indian Institute of Technology (IIT) Kanpur.
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