I am not sure it counts as "research" but I spent part of the morning touring the future space of the NYU Center for Data Science, currently occupied by Forbes Magazine. The space is excellent, and can be renovated to meet our needs beautifully. The real question is whether we can understand our needs faster than the design schedule.
In the afternoon, Foreman-Mackey and I discussed the difference between frequentist and Bayesian estimates of parameter uncertainty. There are regimes in which they agree, and we couldn't quite agree on what those are. Certainly in the super-restrictive case of Gaussian-shaped likelihood function (Gaussian-shaped in parameter space), and (relatively) uninformative priors, the uncertainty estimates converge. But I think the convergence is more general than this.