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Reetik Sahu

Reetik Sahu
Postdoctoral Scholar

Reetik Sahu am a postdoctoral scholar in the Center for Computational Science and Engineering in the Computational Research Division at the Lawrence Berkeley National laboratory. His current research interests include optimization and building science-driven policy-making frameworks. He is currently working on building computationally-efficient data-driven models to predict groundwater levels in California using machine learning techniques. This research aims to provide insights into building robust prediction models and will enable groundwater sustainability agencies to make informed and timely decisions for groundwater management.

 He received his Ph.D in Computational Science and Environmental engineering from MIT in 2018. As a research graduate he worked on building optimal energy contracts for hydroelectric power plants and building non-cooperative decision framework to improve the understanding of interactions between farmers and groundwater.



  • J. Mueller, J.Park, R. Sahu, C. Varadharajan, B. Arora, B. Faybishenko, and D. Agarwal. Surrogate optimization of deep neural networks for groundwater predictionsSubmitted for publication, 2019

  • Reetik Sahu, Dennis B. McLaughlin. Managing groundwater as a common pool resource: A multi-compartmental approach. Working paper
  • Reetik Sahu, Dennis B. McLaughlin. A spatially distributed game theoretic analysis of groundwater common pool problem. Working paper

Conference presentations

  • Reetik Sahu, Jangho Park. Juliane Mueller, Charulekha Varadhrajan, Boris Faybishenko, Bhavna Arora and Deb Aggarwal. Predicting daily groundwater levels with deep learning models. In AGU Fall Meeting, 2019
  • Reetik Sahu, Dennis B. McLaughlin. Managing a Common Pool Resource: Real Time Decision-Making in a Groundwater Aquifer. In AGU December, 2017
  • Reetik Sahu, Dennis B. McLaughlin. Optimal reservoir control using Model Predictive Control. In AGU December, 2016