Abstract
Extreme data rates at the Large Hadron Collider (LHC) provide a unique opportunity for advancing fundamental physics but are also a grand challenge for online and offline algorithms. After briefly describing recent advances for online analysis at the LHC, I will focus on new techniques for accelerating data analysis offline. Fast algorithms are becoming increasingly important offline as the size and complexity of our data continue to grow with increasingly constrained computing resources. As an example, I will show how generative adversarial neural networks show great promise for reducing the time to perform inference by orders of magnitude.