Finding new ways to collect data – and a willingness to share it – are the hallmarks of a career in academia for two data scientists at Stanford.
Fiber optic cables that convey data at high speeds across the globe area is a well-known feature of modern technology. Now, university data scientists have found a unique use for them: monitoring earthquakes.Distributed across Stanford’s telecom infrastructure, the cables have become a seismic array that has already collected data on over 1,000 Bay Area earthquakes, says Eileen Martin, a recent alumnus of Stanford’s Institute for Computational and Mathematical Engineering, now Assistant Professor at Virginia Tech, whose research is focused on seismology. Martin and Nilah Monnier Ioannidis, a postdoctoral scholar concentrating on data science and genomics at Stanford, sat down to discuss the pivotal role of data in their research for the Women in Data Science podcast.
Despite coming from different fields, both researchers tout the importance of data in academic research. Genomic sequencing requires vast amounts of data, but privacy concerns mandate important restrictions, Ioannidis says. Consequently, she is collaborating with outside institutions that have already amassed large stores of genomic data to understand its role in the field of genomics. Kaiser Permanente is among those collaborations; the company has already done a large-scale genomics study for Northern California. Martin says that being open with other researchers and sharing ideas is a real plus in the field. Ioannidis echoes these sentiments. While Martin acknowledges the risk that another researcher will use the shared information, she adds, “We’re all busy trying to do our own experiments.” Their advice for students looking to pursue a career in data science within academia: look for new experimental techniques because there will always be an interesting math or computing problem to solve.