Rajesh Ranganath: Recipient of 2018 Savage Award from the International Society for Bayesian Analysis at the Joint Statistical Meetings
Rajesh Ranganath, Assistant Professor of Computer Science and Data Science at NYU’s Center for Data Science, has been awarded the 2018 Savage Award for outstanding doctoral dissertation in Bayesian theory and methods at the Joint Statistical Meeting.
The dissertation for which Rajesh received the award focuses on generic Bayesian computation. As he describes it, “Imagine if you observe something, say you get to measure how blood flows in your brain, and there’s hidden structure that you would like to answer a question about. How do you compute to find that hidden structure, how do you do it accurately — how do you do it easily?”
Rajesh is now looking into definitions of risk for cardiovascular disease. Current treatment guidelines suggest treating conditions such as high cholesterol based on ten-year risk of having cardiovascular disease. But Rajesh is addressing how we reach that notion of risk: “We are asking what the right computational tools are for that, how we can do that using all the available data, in a way that answers the right clinical question.”
Rajesh is also looking at developing methods to use an external random event to estimate a causal effect. In his words, “If you have randomization, you can ask for the main causal effect, because you can flip a coin and give people one treatment versus another. You know the treatment was randomized because you flipped [the coin] randomly, there are no other variables that could influence treatment. Sometimes the world is nice to you and a random event happens. You can use that randomness to try to estimate causal effects.”
Rajesh’s central focus is making probabilistic and causal inferences for healthcare and, more broadly, other areas of science. “I like learning about neuroscience, I like learning about healthcare, and I am starting to look into problems in physics. Data is central to many of these areas. If you want to do something that is useful and learn about many different things, data science is a nice place to be.”
By Mary Oliver
