In the morning, researchers from across the Flatiron Institute gathered for a discussion of statistical inference, which is a theme that cuts across the different departments. Justin Alsing (Flatiron) led the discussion, asking for advice on his project to model global ozone over the last few decades. He has data that spans latitude, altitude, and time, and the ozone levels can be affected by many things other than long-term degradation by pollutants. So he wants to build a non-linear, data-driven model of confounders but still come to strong conclusions about the long-term trends. There was discussion of many relevant methods, including large linear models (regularized strongly), independent components analysis, latent variable models, neural networks, and so on. It was a wide-ranging and valuable discussion. The CCB at Flatiron has some valuable mathematics expertise, which could be important to all the Flatiron departments.