Decision Analytics Lab aims to develop computationally tractable methods to solve large-scale decision problems.  It uses  interdisciplinary approaches, which lie in the intersection of  Large Scale Optimization and Artificial Intelligent (AI).  The research goal is to leverage the strengths of both disciplines to ensure interpretable, robust and reliable decisions. These techniques are applied to high stake societal problems in healthcare, energy and humanitarian logistics.

Key focus of our Lab is to develop:

    • Efficient discrete optimization based robust matching algorithms for Causal Inference
    • Scalable optimization techniques for solving large-scale decision problems
    • Pragmatic decision-making framework to handle inherent uncertainty and complexity in multi-criteria decision-making process
    • Bigdata analytics framework to solve public health problems