معرفی
Anish Agarwal is an Assistant Professor in the Department of Industrial Engineering and Operations Research at Columbia University's School of Engineering and Applied Science. He is an affiliated member of the Data Science Institute (DSI), Financial and Business Analytics, Foundations of Data Science, and Smart Cities centers. His research focuses on causal inference, econometrics, and high-dimensional statistics, with applications to data-driven decision-making in engineering and social systems.
Agarwal holds a PhD in Electrical Engineering and Computer Science (EECS) from MIT, advised by Alberto Abadie, Munther Dahleh, and Devavrat Shah. Prior to Columbia, he was a postdoctoral scientist at Amazon's Core AI team and the Simons Institute at UC Berkeley. He has consulted for TauRx Therapeutics, Uber Technologies, and Boston Consulting Group on topics like experiment design and causal inference.
His research interests include designing causal frameworks for complex systems, integrating econometric methods with machine learning, and addressing challenges in high-dimensional data analysis. Notable contributions include synthetic control methodologies, data auctions with externalities, and frameworks for combinatorial interventions.
- Awards:
- INFORMS George B. Dantzig Best Thesis Award (2nd Place)
- ACM SIGMETRICS Outstanding Thesis Award (2nd Place)
- Best Paper Awards at NSDI’23 and the American Statistical Association
- Grants & Collaborations: Work with industry partners on experiment design and causal inference frameworks.
Agarwal's work bridges theoretical advancements with practical applications in healthcare, economics, and engineering systems, emphasizing scalable and robust methods for decision-making under uncertainty.
