PNNL Chief Scientist for Computing Jim Ang will be part of a DOE Office of Science virtual discussion regarding industry collaborations on AI hardware.

Four research staff from PNNL are part of an international team that earned top honors for a journal paper focused on a new algorithm-evaluation approach for buildings.

PNNL researchers received the “Best Paper Award” at the 39th IEEE International Conference on Computer Design.

PNNL researchers contributed to the International Conference on Data Science and Advanced Analytics 2021 Best Paper Award-winning publication.

Tumeo joins other computer scientists in a contributed article on graph processing systems to the Communications of the ACM.

PNNL computer scientist Ang Li serves as one of the principal investigators for a project selected to receive DOE funding.

Slaven Peles, PNNL computational scientist and leader of a national high-performance computing project for power grid analysis, spoke about the project with the host of the Let’s Talk Exascale podcast.

George Karniadakis receives award highlighting 30 years of work developing computational methods to solve real-world problems.

The MIT-sponsored competition encourages community approaches to developing new solutions for analyzing graphs and sparse data; PNNL has placed a winner in each year.

Ang Li and Antonino Tumeo will present hardware to balance machine learning workloads at a top computer architecture conference.

PNNL data scientist Carlos M. Ortiz Marrero has co-edited a volume that highlights work at the intersection of quantum computation and mathematics.

Sonja Glavaski and Kevin Schneider, both electrical engineers at PNNL, have been named as IEEE fellows. IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

A student computing security research project guided by PCSD computer scientists Ang Li and Kevin Barker placed third among dozens of entries in the student research poster session at SC19, a premier annual conference for high-performance c

Through her role in the Department of Energy’s Advanced Scientific Computing Research-supported ExaLearn project, Jenna Pope is developing deep learning approaches for finding optimal water cluster structures for a variety of applications.