Advisees – Dean Eckles

I’m fortunate to advise or coadvise (that’s most of these) 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. 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 also note that there are often many other relevant faculty for each program; having multiple potential advisors in a program is a good idea.

Current graduate students

Current post-docs

Madhav Kumar

Previous (co-)advisees

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

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?

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.

Bias and high-dimensional adjustment in observational studies of peer effects. JASA.
Are observational studies informative about social contagion? We use a massive randomized experiment in Facebook News Feed to identify peer effects in link sharing and compare these results to those using only observational data. The richest models are able to recover the experimental “gold standard” peer effect, at least for the most popular classes of behavior.

Estimating peer effects in networks with peer encouragement designs. PNAS.
We employ a novel experimental design to modulate how many likes and comments are received by 49 million Facebook users, allowing us to estimate network effects in adoption and use of communication technologies.