Madeleine Udell automates and accelerates machine learning with NSF CAREER award

Big datasets are everywhere: in science, health, commerce, and government, data is becoming easier and cheaper to collect.

Yet extracting value from this data is a challenge. Every step requires human intervention: cleaning the data, identifying useful features, and choosing a machine learning model. MS&E Professor Madeleine Udell recently received a National Science Foundation (NSF) CAREER award to help solve that problem.

The goal of Prof. Udell's project is to develop new methods to accelerate and automate the basic machine learning (ML) workflow. Automation frees data scientists from data cleaning and parameter twiddling to concentrate on solving the right problems and collecting the right data. The project will help democratize machine learning and promote data-driven decision making by developing automated methods to clean data and to choose ML models, including open source software packages, that make these methods widely available and easy to use. Prof. Udell will also advance these goals by training data scientists how to use these models and understand their potential risks.

Learn more about Stanford's nine recent NSF CAREER award winners

Article text adapted from the NSF website.