Mount Sinai Researchers Use Artificial Intelligence to Uncover the Cellular Origins of Alzheimer’s Disease and Other Cognitive Disorders

Deep learning models represent “an entirely new paradigm for studying dementia”

Mount Sinai Researchers Develop Machine Learning Model that Can Detect and Predict COVID-19 from Collected Data on Wearable Devices

Mount Sinai researchers have developed a machine learning algorithm that can determine if an individual has SARS-CoV-2 infections, the virus that causes COVID-19—with a high sensitivity and specificity—from the data collected by wearable devices.

Human-triggered California wildfires more severe than natural blazes

Irvine, Calif., June 6, 2022 – Human-caused wildfires in California are more ferocious than blazes sparked by lightning, a team led by scientists from the University of California, Irvine reported recently in the journal Nature Communications. The research could help scientists better understand fire severity and how likely a blaze is to kill trees and inflict long-term damage on an ecosystem in its path.

Finding fire and ice: Modeling the probability of methane hydrate deposits on the seafloor

A team of researchers from Sandia National Laboratories and the U.S. Naval Research Laboratory have developed a new system to model the likelihood of finding methane hydrate and methane gas that was tested in a region of seafloor off the coast of North Carolina. This test was published on March 14 in the scientific journal Geochemistry, Geophysics, Geosystems.

Alexa, do I have an irregular heart rhythm? First AI system for contactless monitoring of heart rhythm using smart speakers

University of Washington researchers have developed a new skill for a smart speaker that for the first time monitors both regular and irregular heartbeats without physical contact.

Machine Learning Trims Tuning Time for Electron Beam by 65 Percent

Linear accelerator operators use computer algorithms to automate some parts of the machine tuning process. These algorithms make fast decisions, but they have not previously incorporated fundamental physics or learned from past mistakes. A new machine learning algorithm learns both from experience and physics simulations to reduce the time needed for a part of the machine tuning process by 65 percent.

Scientists voice concerns, call for transparency and reproducibility in AI research

In an article published in Nature on October 14, 2020, scientists at Princess Margaret Cancer Centre, University of Toronto, Stanford University, Johns Hopkins, Harvard School of Public Health, Massachusetts Institute of Technology, and others, challenge scientific journals to hold computational researchers to higher standards of transparency, and call for their colleagues to share their code, models and computational environments in publications.

Making a Material World Better, Faster Now: Q&A With Materials Project Director Kristin Persson

Berkeley Lab’s Kristin Persson shares her thoughts on what inspired her to launch the Materials Project online database, the future of materials research and machine learning, and how she found her own way into a STEM career.

CFN Staff Spotlight: Xiaohui Qu Bridges the Data Science-Materials Science Gap

As a staff member in the Theory and Computation Group at Brookhaven Lab’s Center for Functional Nanomaterials, Qu applies various approaches in artificial intelligence to analyze experimental and computational nanoscience data.

Machine learning technique speeds up crystal structure determination

A computer-based method could make it less labor-intensive to determine the crystal structures of various materials and molecules, including alloys, proteins and pharmaceuticals. The method uses a machine learning algorithm, similar to the type used in facial recognition and self-driving cars, to independently analyze electron diffraction patterns, and do so with at least 95% accuracy.

Department of Energy Announces $21.4 Million for Quantum Information Science Research

The following news release was issued on Aug. 26, 2019 by the U.S. Department of Energy (DOE). It announces funding that DOE has awarded for research in quantum information science related to particle physics and fusion energy sciences. Scientists at DOE’s Brookhaven National Laboratory are principal investigators on two of the 21 funded projects.