The American Society for Quality (ASQ) has recognized Laboratory Fellow and Pacific Northwest National Laboratory (PNNL) Statistician Greg Piepel with the William G. Hunter Award.
PNNL team has developed and implemented a generalizable computational framework to study the resilience of the multilayered London Rail Network to the compound threat of intense flooding and a targeted cyberattack.
PNNL scientists have created a tool called WatchOwl to collect more than 4 million tweets per day related to the COVID-19 pandemic. The tool analyzes tweets related to interventions like social distancing and movement restrictions.
Radiation from natural sources in the environment can limit the performance of superconducting quantum bits, known as qubits. The discovery has implications for quantum computing and for the search for dark matter.
PNNL researchers Lisa Bramer and Sarah Reehl were on a team that received a patent for its work with electron microscopy. Electron microscopy allows scientists to make nanoscale observations of materials.
The National Association of Mathematics named PNNL Data Analyst Brett Jefferson best speaker for his presentation on an innovative mathematical finding that could be used to improve electric grid coverage.
A new book by PNNL biochemist Erick Merkley details forensic proteomics, a technique that directly analyzes proteins in unknown samples, in pursuit of making proteomics a widespread forensic method when DNA is missing or ambiguous.
At a conference featuring the most advanced computing hardware and software, ML in its various guises was on full display and highlighted by Nathan Baker’s featured invited presentation.
A multi-institute research team is exploring ways to improve residential walls across America, making homes warmer and drier and delivering significant energy savings.
B3? E4? Remember the board game Battleship? One player suggests a set of coordinates to another, hoping to find the elusive location of an unseen vessel.That is a good place to start in assessing the search for dark matter.
Researchers at PNNL are applying deep learning techniques to learn more about neutrinos, part of a worldwide network of researchers trying to understand one of the universe’s most elusive particles.
Researchers at PNNL construct a novel approach that requires less field work while delivering critical information on building code compliance and energy efficiency in new homes.