The Department of Industrial Engineering and Operations Research is pleased to announce that Professor Garud Iyengar and Ph.D. student Tianyu Wang have been named finalists in the 2025 M&SOM Data-Driven Research Challenge.
Their paper, "Optimizing Pharmaceutical Control with Multi-Task Contextual Bandits: Addressing Batch Heterogeneity for Improved Manufacturing Efficiency," was conducted with co-author Naz Pinar Taskiran (ChemE).
About Tianyu Wang:
Wang’s research lies at the interface of statistics, stochastic optimization, and machine learning. Recent research focuses on understanding the performance of data-driven optimization algorithms and their limits under heterogeneous data, with applications in trustworthy machine learning, supply chain management, and continuous manufacturing. He was recently selected as a 2025-2026 Deming Doctoral Fellow.
About Professor Iyengar:
Professor Iyengar’s research focuses on understanding uncertain systems and exploiting available information using data-driven control and optimization algorithms. He and his students have explored applications in many diverse fields, such as machine learning, systemic risk, asset management, operations management, sports analytics, and biology.
Iyengar’s research group is currently working on thermodynamics of sensing and memory in cells, an automatic defensive assignment and event detection in NBA games, a deep neural-network-based framework for interpretable robust decision making, attribution schemes for allocating payment in a multi-channel advertising, systemic risk associated with extreme weather, and an NLP-based model for predicting stock performance using news reports.
He is the Avanessians Director of the Data Science Institute at Columbia.
