“Berkeley Lab is unique because its machine learning expertise is reasonably well established, and its tradition of team science means that we can work with researchers to apply these methods to scientific problems.”

“Although much of the time and effort spent in the software maintenance is not reflected in our research publication list, it is more than rewarding to see the wide use of this software in both the high-end scientific world and the commercial world.”

“I think one of the things Berkeley Lab does well is allow people to make collaborations that advance science much more efficiently.”

Berkeley Lab’s research into machine learning builds on its foundational work in mathematics to develop methods that are consistent with physical laws, robust in the presence of noisy or biased data, and capable of being interpreted and explained in scientifically meaningful ways.

Berkeley Lab Research Scientist Mariam Kiran uses deep reinforcement learning and innovative multi-objective optimization techniques to train network controllers to predict network traffic and improve traffic engineering.

First developed about 80 years ago, machine learning is a type of AI centered on programs — called algorithms — that can teach themselves different ways of processing data after they are trained on sample datasets.