My research studies the interactions between machine learning and a variety of contexts, ranging from crowdsourcing to game theory, and algorithmic fairness. Recently, I have focused on understanding the ethical aspects of algorithmic decision making in the public health domain.
My recent focus since joining the CRCS in the last fall has been on understanding the ethical challenges in networks based problems such as societal interventions
for suicide and Tuberculosis. Even more recently, and motivated by the on-going pandemic, I have been studying agent based models to understand the dynamics of
the spread of COVID-19 as well as applying game theory and reinforcement learning to derive policies that can be used in the context.