As author of her first publication, PNNL bioinformaticist Isabelle O’Bryon developed the first forensic proteomics method to more quickly detect ricin, a toxin often crudely made in home laboratories that can kill in trace amounts.
Sam Chatterjee, a senior operations research scientist at PNNL, was recently appointed as associate editor for the specialty section, “Water and the Built Environment” at the peer-reviewed, open access journal Frontiers in Water.
Two PNNL researchers are helping define the future of transparency and accountability for public and private use of autonomous and intelligent systems.
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.
Bill Cannon, senior scientist and biophysicist in the Computational Mathematics Group, was a co-author of a recent article published in Nature Partner Journals-Digital Medicine.
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.
Researchers at PNNL are contributing artificial intelligence, machine learning, and app development expertise to a U of W project that will ease challenges with urban freight delivery. The project will provide delivery drivers with a tool
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.
In today’s digital age, the rabbit hole of connected information can be not only a time sink, but downright overwhelming. Even for high-performance computers.
Francesca Grogan grew up in Southern California, gravitated to competitive swimming, and chose to stay close to her geographical roots for her undergraduate and postgraduate studies.
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.
