SCS Researchers Top Leaderboard in DARPA AutoML Evaluations

Saswati Ray, a senior research analyst in the Auton Lab, has consistently outscored competitors in DARPA's program for building automated machine learning systems.

Researchers led by Saswati Ray, a senior research analyst in the School of Computer Science's Auton Lab, have once again received top scores among teams participating in the Defense Advanced Research Project Agency's program for building automated machine learning (AutoML) systems.

The Data-Driven Discovery of Models (D3M) program seeks to automate the process of building predictive models for complex systems, with the goal of speeding scientific discovery by enabling subject matter experts to build models with little or no help from data scientists. More than 10 teams, most from academic centers, participate. Four or five times each year, DARPA evaluates each team's algorithm for building this AutoML pipeline by applying it to several previously unseen problems.

Over the last two years, Ray's algorithms have consistently outscored all others in these tests, even though her code is shared with the other teams after each evaluation.

"She remains the reigning Queen of AutoML," said Artur Dubrawski, research professor of computer science and director of the Auton Lab. "We've gotten used to Saswati doing this, but to continue being number one in such a tight contest for so long is like winning seven or eight Stanley Cups or Super Bowls in a row."

In the latest evaluation, a sub-team including the Auton Lab's Jarod Wang, Cristian Challu and Kin Gutierrez also topped the leader board in a component category — building collections of "primitives" for performing ML-related tasks such as data conditioning/preprocessing or classification. Their new time series forecasting algorithm pushed their collection to the top spot, Dubrawski said.

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