Dr. Katherine Livins, recent graduate of the CIS Ph.D. program, was awarded a prestigious spot in the postdoctoral training program called “Insight.” This program involves summer intensive technical training to bridge academic and scientific methods to industry application, along with subsequent placement in a major data science / industry context. We asked Dr. Livins about her experiences so far.
What features of insight are most interesting to you?
“The program basically gives you three weeks to develop a data-driven project. What you do with these three weeks is up to you, as long as it results in a data product of some sort. Of course, many large data sets out there are based on human activity, and I personally chose to try to predict social performance on a popular website based on a variety of activity patterns (including a bunch of linguistic patterns). It’s been a great opportunity to see how many cognitive features play out in natural online behavior.”
What exciting career prospects emerge from Insight training?
“Insight puts you in contact with a variety of companies interested in hiring data scientists. After completing your project you hit the road and start demoing to a selection of them. The idea is to connect fellows to data teams, which hopefully results in jobs.”
What is the community like? Are there other scientists delving into data science?
“The group is made up of 35 PhDs from across the sciences and a variety of schools. It’s a collaborative group that includes experts in statistics, analytics, machine learning, and data visualization, though most are new to industry. The program has been around for a couple of years though, and previous fellows have ended up all over the valley. It’s creating a nice network of Insight-friends that are more than willing to help fellows move into whatever position they’re looking for.”
Dr. Livins CIS Dissertation is entitled “Shaping Relations: The Effects of Visuospatial Priming on Structured Thought.” Her research in the CIS program supported training in both experimental and computational methods. The result is a great mix for pursuing industry and data science work.