Much of astronomy uses pixelized data, but the size and complexity of these data often strain the capability of existing data analysis techniques. I will present algorithms built on advances in statistics and machine learning that allow more science to be done with the same pixels. Digital tracking searches for Kuiper belt objects (KBOs) involve series of images where the KBO is undetectable in each image, but detectable in the series. By forward modelling the position of the KBO in each image in a “joint-fit”, the KBOs’ trajectories can be measured precisely enough to constrain their dynamics. Probabilistic cataloguing (PCat) is a Bayesian method to detect and measure point sources in crowded images. In extremely crowded fields with one star per 10 pixels, PCat finds stars four times fainter than DAOPHOT, a commonly used pipeline. Finally, I will discuss a variational autoencoder trained on galaxy spectra from the Sloan Digital Sky Survey. This autoencoder learns a six dimensional latent space that naturally separates different classes of galaxy and captures variation in spectral line widths and ratios.