Carnegie Mellon University hosted more than 120 researchers June 6–10 for Statistical Challenges in Modern Astronomy VI, bringing together experts in statistical methods and machine learning with astronomers and cosmologists to discuss pressing inference problems facing this data-rich field.

Topics at the meeting ranged from the study of exoplanets and the analysis of astronomical time series to the estimation of key cosmological parameters using the subtle signals found in the weak lensing of galaxies. Emphasis was placed on the participation of not only statisticians and computer scientists with expertise in the analysis of astronomical data, but also experts in the use of new analysis methods of particular interest and promise for astronomy. These included variational inference and deep learning.

In addition to invited and contributed talks, there were more than 40 poster presentations.