contact: maja at cs dot columbia dot edu
Maja Rudolph is a Senior Research Scientist at the Bosch Center for AI. She works on machine learning research questions derived from engineering problems: for example, how to model driving behavior, how to forecast the operating conditions of a device, or how to find anomalies in the sensor data of an assembly line.
The methods she develops 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 2018, Maja completed her Ph.D. in Computer Science at Columbia University, advised by David Blei. Together they worked on embeddings - methods for learning interpretable representations of data. She holds a MS in Electrical Engineering from Columbia University and a BS in Mathematics from MIT.