Nuala’s Impressions from the Astrostatistics Workshop

The following post was written by Nuala McCullagh who is a graduate student in the Physics & Astronomy department at Johns Hopkins.

Nuala McCullagh sitting on a bench

Nuala McCullagh

I was thrilled to have the opportunity to visit SAMSI for the Massive Datasets program for three weeks in September. One of the most positive aspects of my visit was my exposure to several flavors of diversity, the most salient of which was diversity of expertise and discipline. As a graduate student in the Physics & Astronomy department at Johns Hopkins, I have been active in promoting diversity within my department. Physics and astronomy, along with most math and science fields, have traditionally lacked racial and gender diversity, and while the benefits of diversity are well established and generally accepted, it can still be difficult to convince scientists that it is an issue they should care about. The benefits of the diversity I observed at SAMSI were very clear, and my experience there really reinforced my belief that diversity can inspire creativity and productivity.

At the opening workshop, we heard talks from experts in statistics, computer science, applied math, neuroscience, environment & climate science, high energy physics, and astronomy. While the conference covered a wide range of disciplines, there was a common thread of having to deal with massive datasets. I was surprised to learn about the similarities between my work in cosmology and work in other fields such as climate studies and neuroscience. Hearing about the problems and solutions in those fields have helped me think about my own problems in a different way.

At the astrostatistics workshop, we heard about large galaxy surveys, computer simulations, multi-dimensional datasets, time-domain astronomy, and more. It was helpful to hear about the different statistical problems with massive datasets in the context of astronomy, and interesting to see the similarities and differences between them. For example, just within cosmology, the statistical problems that arise when working with large dark matter simulations are different from those that arise in detecting weak lensing in galaxy surveys. Meanwhile, people who study exoplanets work with large simulations with many parameters, much like the simulations in cosmology. Hearing about the various statistical problems astronomers have encountered allowed me to make connections between different areas in astronomy that I would not have noticed otherwise.

I appreciated the opportunity to learn about a wide variety of problems concerning massive data. It was interesting to note the statistical similarities in seemingly disparate scientific problems. It was also reaffirming to see the positive impact that diversity can have in inspiring creativity and productivity in science.