Machine Learning & Analytics Group

The Machine Learning & Analytics Group engages in research, development, and deployment of scalable, high performance, and data-intensive machine learning, analytics, and visualization technologies in collaboration with other researchers from diverse scientific disciplines. These methods and their application help to uncover new knowledge in scientific data from simulations, experiments and observations.

We accomplish this mission by

  • Focusing RD&D efforts at all stages of the data understanding pipeline.
  • Collaborating closely with science stakeholders to maximize science impact.
  • Tightly integrating and coordinating interaction between research, development, and deployment activities.


April 20, 2022

Oliver Ruebel, Talita Perciano, and E. Wes Bethel contributed to a recently paper published in the Proceedings of the National Academy of Science where the team, along with researchers from the Pacific Northwest National Laboratory and the University of Washington, developed deep learning techniques for analyzing in situ high-speed atomic force microscopy (HS-AFM) and transmission electron microscopy data. More information.

March 1, 2022

Talita Perciano is part of the Computing Sciences team that developed HYPPO, a software that leverages prediction uncertainty to optimize deep learning models for science. More information.

January 18, 2022

After nearly 22 years first as the Group Lead for the Visualization Group, then the Data Analytics and Visualization Group, and now the Machine Learning and Analytics Group, Wes Bethel steps down and welcomes Michael Mahoney, the new incoming Group Lead for the Machine Learning and Analytics Group.

November 15, 2021

Wes Bethel and Gunther Weber are part of the Organizing Committee for the SC21 Workshop "In Situ Infrastructures for Extreme-scale Analysis and Visualization (ISAV'21)", now in its 6th year. More information.

November 14, 2021

Wes Bethel and Burlen Loring are part of an SC21 Tutorial on the subject of HPC In Situ Processing. More information.

October 25, 2021

Work led by Talita Perciano and her summer student Mercy Amankwah promises to advance the field of quantum image processing. More information.

August 30, 2021

Dani Ushizima and her collaborators at ALS receives the Halbach Award for the development of a machine-learning-based application to stabilize the transverse beam size and enhance the photon-beam performance at ALS; read more here.

August 18, 2021

Talita Perciano is being recognized for providing exceptional mentorship to students participating in various internships provided by Berkeley Lab; read more here.

August 2, 2021

Dani Ushizima and the Center for Recognition and Inspection of Cells (CRIC) have taken a major step toward applying machine learning techniques to women's health: their newly published database of images of cervix cytology offers innovative opportunities for the use of machine learning for biomedical purposes; read more here.

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