Advisees – Dean Eckles

I’m fortunate to advise, coadvise (that’s most of these), or generally just collaborate and chat about research with some excellent graduate students and postdoctoral fellows here at MIT. Check out their websites below.

For prospective students

I’ve (co)advised students in the MIT Sloan PhD program (primarily in the Marketing group) and the MIT IDSS Social and Engineering Systems PhD program, including students who are also enrolled in the Interdisciplinary Doctoral Program in Statistics.

I don’t meet with prospective students for the purpose of discussing their application, but do meet with admitted students. I would encourage you to apply to those programs, and suggest that if you are planning to apply to one to also consider the other as well. Note that there are often many other relevant faculty for each program; having multiple potential advisors in a program is a good idea.

Current (co-)advisees

Current post-docs

Previous (co-)advisees

Miranda Ahlers (SM)
Sanzeed Anwar (MEng)
Kiran Garimella (postdoc)
David Holtz (PhD)
Eaman Jahani (PhD)
Amin Rahimian (postdoc)
Jad Sassine (PhD)
Joseph Schlessinger (SM)
Amir Tohidi (PhD)
Jeremy Yang (PhD)
Yuan Yuan (PhD)

Featured work

Long ties, disruptive life events and economic prosperity. PNAS.
50 years after Granovetter’s “The Strength of Weak Ties”, we provide new evidence of the connection between network structure and economic prosperity. We also show how life events, like switching high schools, can result in more long ties, even decades later. This paper is accompanied by new public data on the Facebook networks of people in US and Mexican zip codes .

Algorithmic transparency and assessing effects of algorithmic ranking. Testimony before the US Senate Subcommittee on Communications, Media, and Broadband.
This addresses the questions: How does algorithmic ranking in social media work? How can we assess algorithmic impact & amplification? What can policy-makers do here?

Seeding with costly network information. Operations Research.
Because of their position in a social network, some people can be much more influential than others, and so marketers and others often want to target them as “seeds”. But, outside of specific cases, one doesn’t fully observe the social network, but must probe it (with costly surveys or crawling). We develop techniques for selecting good seeds in arbitrary networks while limiting how much we need to probe the network.

Providing normative information increases intentions to accept a COVID-19 vaccine. Nature Communications.
When does sharing information about others’ behaviors cause more of that same behavior? This is both a central question in the social sciences and also important for effecting preventative health behaviors, including vaccination. We use a randomized experiment embedded in the international survey on COVID-19 we conducted with Meta and JHU to address this, providing evidence that these descriptive norms increase intentions to get vaccinated.