Increasing data sizes necessitate fast and efficient algorithms for analyzing them. Regression is one such essential tool that is used widely in computer science. In this talk, I will focus on the "p-norm regression problem", which is a generalization of the standard "linear regression problem", and captures several important problems including the maximum flow problem on graphs. Historically, obtaining fast, high-accuracy algorithms for this problem has been challenging due to the lack of smoothness and strong convexity of the function, however, recent breakthroughs have been able to get around these issues. I will present an overview of how these algorithms work and discuss some generalizations of these techniques to other regression problems.