  Many synoptic surveys are observing large parts of the sky multiple times.
The resulting lightcurves provide a wonderful window to the dynamic nature of
the universe. However, there are many significant challenges in analyzing these
light curves. These include heterogeneity of the data, irregularly sampled
data, missing data, censored data, known but variable measurement errors, and
most importantly, the need to classify in astronomical objects in real time
using these imperfect light curves. We describe a modeling-based approach using
Gaussian process regression for generating critical measures representing
features for the classification of such lightcurves. We demonstrate that our
approach performs better by comparing it with past methods. Finally, we provide
future directions for use in sky-surveys that are getting even bigger by the
day.
