Want to get tenure? Stay away from interdisciplinary research.

This blog post is part of a series called Is Academia Broken? This is the first in the series and it discusses the perils of doing interdisciplinary research for early career academics. You can find the second blog post of the series here.

Inter-disciplinary, multi-disciplinary, cross-disciplinary research: it is much lauded in academia. The reason is clear: cross-fertilization of ideas is undeniably a good thing. Interdisciplinary research allows different fields and cultures to borrow each others’ methods, approaches, and results. After all, scholars do not do research in a vacuum. And as the boundaries separating departments and disciplines fade, collaboration among them naturally increases. While interdisciplinary research is indeed a good thing for academia, I would like to argue that it is a bad choice to jumpstart an academic career.

Here’s my story. From my undergraduate degree through to my postdoc, I was pushed to take classes in other departments. My undergraduate degree was in Astrophysics and I took two or three classes in Computer Science. This is rather normal. A lot of physicists are (and need to be) good with computers. I liked these classes enough to apply for a Masters in Computer Science which I completed right after my Bachelor. I then worked two research jobs. The first at CINECA, Italy, where I did Astronomical Data Visualization (a great way to blend Astrophysics and Computer Science). The second one at CERN, Switzerland, where I worked with data repositories, digital libraries, natural language processing, Open Access. In my years at CERN, I started getting more and more interested in data and information science. I applied and got into a Ph.D. program in Information Studies at UCLA where I worked with Christine Borgman - easily one of the top Information Scientists in the world.

Anything that falls under the umbrella of Data Science and Information Science is intrinsically interdisciplinary and the classes I took at UCLA were as interdisciplinary as it gets. During my first two years as a Ph.D., I took classes such as

  • Methods for social network analysis (Sociology)

  • Critical studies of architecture (Architecture)

  • Geographic thought and the concept of belonging (Geography)

  • Thinking about thinking (Cognitive Science)

  • Formal Modeling and Simulations in Social Sciences (Complex Systems)

  • Data and Media Arts (Design)

A pretty mixed bag, huh? (Full list here). While it all sounds a bit eccentric, these were the most formative, nurturing years of my life (I will discuss this in detail in a separate post). These classes instructed me on research methods I did not know about: for my dissertation (Pepe 2010), I used graph theory (physics), survey research (sociology), and “complex systems” methods. These classes also let me meet, collaborate, and publish papers with scholars in other disciplines: sociology/identity (Pepe 2012), social media analysis (Shuai 2012), semantic web theory (Rodriguez 2010), and even music research (Rodriguez 2008).

In the final year of my Ph.D., I was lucky enough to meet a Professor of Astronomy at Harvard - Alyssa Goodman - who was passionate about data, visualization, digital libraries, Open Access, and Open Science. We immediately struck a chord and she offered me a Postdoc as the in-house information scientist at the Center for Astrophysics. So, a return to Astrophysics, some people thought! Well, not really, because at Harvard I also became a fellow of the most excellent Berkman Center for Internet and Society (Law) and Institute for Quantitative Social Science (Social Sciences).