Maja Rita Rudolph PhD Student, Columbia University, Machine Learning

About

I am a PhD student at Columbia University advised by Prof. David Blei. My research is focused on embeddings – methods for learning interpretable representations from data. The embedding models we develop lie at the intersection of Bayesian machine learning and deep learning. Bayesian modeling helps communicate modeling choices and to reason about uncertainty while neural networks provide the flexibility to model complex interactions in the data. In 2013, I graduated from MIT with a BS in Mathematics.

Introduction Video

This video shows a day as an intern at the SAP Innovation Center in Potsdam, Germany. Throughout a 2013 summer internship I worked on several machine learning systems and implemented an automatic decision tree visualization tool.