Garud Iyengar’s research is focused 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.
Iyengar received a B Tech in electrical engineering from the Indian Institute of Technology in 1993 and a PhD in electrical engineering from Stanford University in 1998. He is a member of Columbia’s Data Science Institute.