Sandhya Saisubramanian

Selected Research Projects
AI for smart and connected communities
The goal of this project is to build intelligent decision support systems that reason under large, complex and stochastic real-world settings. We use a combination of sampling, optimization techniques, and game-theoretic approaches to achieve this. Our emergency response system and traffic patrolling systems have been deployed. Media Coverage.

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Relevant papers: IAAI 2014, AAAI 2015, SPARK 2017, ITSC 2019.
Robust model reduction for risk-aware planning in large and stochastic environments
Many practical planning problems are intractable, even under full observability. We aim to design efficient model reduction techniques that balance the trade-off between model simplicity and risk awareness, to produce fast and high quality solutions. We employ a combination of heuristic search, sampling, and online planning to achieve near-optimal results, without compromising on the runtime.

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Relevant papers: AAMAS 2018, AISafety 2019, IROS 2019.
Interpretable decision support
Decision support for real-world problems often employs complex reasoning models to improve the accuracy of decisions. It is important that these systems are transparent and the results are interpretable so that the decision-maker can leverage them to make better decisions and know when to trust the system. The goal of this project is to design reasoning models for decision support that are accurate and interpretable.

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Relevant papers: AIES 2020.