Machine Learning Helps Stabilize Synchrotron Light

November 19, 2019

Researchers at the Advanced Light Source (ALS) at Berkeley Lab have shown that machine learning can predict and correct noisy fluctuations in synchrotron light sources and correct them before they occur. The breakthrough solves a decades old issue and will make new, higher powered light sources even more useful to scientists.

Learning to Look for Elusive Inoviruses

July 22, 2019

Using a machine learning approach, a team led by the Joint Genome Institute at Berkeley Lab has opened a new window on the number and diversity of elusive inoviruses. Trawling caches of metagenomic data, the researchers' machine learning method identified 10,000 probable inovirus sequences in six broad families.

Predicting Watershed Behaviors

July 30, 2019

Studying watersheds using learning techniques can lead to better predictions about downstream water supply, quality

Putting AI Tools in the Hands of Scientists

July 29, 2019

Some 175 researchers attended the DL4Sci School to acquire new tools and skills that advance deep learning studies

Understanding Extreme Weather Events

July 24, 2019

Berkeley Lab scientists share their cutting edge machine learning and deep learning studies of climate and extreme weather

Mining for Hidden Knowledge

July 3, 2019

Berkeley Lab study finds that text mining of scientific literature can lead to new discoveries

To Pump or Not to Pump?

June 3, 2019

Berkeley Lab scientists aim to create a new machine learning tool for smarter groundwater management

The Nature of Dark Matter

May 14, 2019

Using deep learning a Lab-led team aims to enhance the use of gravitational lensing to study dark matter

The Future of Scientific Machine Learning

April 15, 2019

Early, mid-career scientists identify applied math challenges, sow seeds of future collaborations

The Quest to Convert CO₂ into Fuels

April 8, 2019

Materials Project's machine learning approach opens new vistas in photocathode research

Identifying Suicide Risks in Veterans

April 4, 2019

Berkeley Lab team uses deep learning to help VA address veterans' psychological, medical challenges

Better Simulations on the Cheap

March 10, 2019

ExaLearn will interpolate details from sparse data to create surrogate models, first for cosmology

Detecting Global Climate Patterns

February 25, 2019

ClimateNet will use deep learning to shed light on the changing behavior of climate and extreme weather

Combing Cosmological Data for the Big Picture

January 25, 2019

Berkeley Lab researcher wins Large Synoptic Survey Telescope machine-learning competition

Tracing the Nature of Neutrinos

December 21, 2018

IceCube research garners best paper award at IEEE machine learning conference

Identifying Extreme Weather

November 20, 2018

Berkeley Lab-led team shares 2018 Gordon Bell Prize for deep learning climate application

Optimizing Traffic Models

October 28, 2018

Berkeley Lab team using machine learning in smart and sustainable mobility solutions

Supporting Sustainable Farming

October 28, 2018

Harnessing the power of machine learning and microbiology

Topology, Physics & Machine Learning Take on Climate Research Data Challenges

September 4, 2018

New data analytics tools could dramatically impact large-scale science data projects

Probing the Fabric of the Cosmos

September 5, 2018

Deep Learning and 3D simulations to assist scientists exploring the physics of the universe

Learning from Living Cells

August 23, 2018

Infrared beams, machine learning show cell types in a different light

A Groundwater Early Warning System

August 13, 2018

Berkeley Lab researchers devise system to monitor contaminant plumes

Berkeley Lab-BIDS Fellows Share Machine Learning Expertise

August 30, 2018

Partnership Enriches ML4Sci Workshop and California Water Data Hackathon

Pinpointing Earthquake Impacts

June 28, 2018

Using ML techniques, new simulations can break down potential major quake impacts by building location and size

The Challenges of Big Data and Advanced Computing Solutions

July 12, 2018

Berkeley Lab's Katherine Yelick contributes expert testimony to Congressional Committee hearing

A ‘Google for Science’

June 19, 2018

Berkeley Lab team automates metadata discovery to search scientific images and data

New ECP Co-Design Center to Focus on Exascale Machine Learning

July 20, 2018

Berkeley Lab One of Eight National Labs Participating in 'ExaLearn'

Physicists, Machine Learning Experts Team Up to Tackle TrackML

June 11, 2018

ML challenge aims to quickly reconstruct the paths of millions of electrically charged particles created in colliders.

Biofuels Bonanza

May 29, 2018

Predicting microbial biofuel production to speed up bioengineering

Extreme Weather

March 29, 2018

Deep learning at 15 pflops to identify extreme weather at scale

Metagenomic Clustering

March 12, 2018

Making sense of a genomic ‘data deluge’

Teaching Computers to Guide Science

March 6, 2018

Berkeley Lab, UC Berkeley's “Iterative Random Forests” deliver science insights

‘Minimalist Machine Learning’

February 21, 2018

Machine learning algorithm analyzes experimental images from limited training data, speeding up the deployment of learning tools

Targeting More Effective Cancer Drugs

August 16, 2017

Machine learning model predicts protein binding for better drug candidates

Machine Learning Enhances Predictive Modeling of 2D Materials

March 2, 2017

Using machine learning algorithms could reduce the time it takes to accurately predict the physical, chemical, and mechanical properties of nanomaterials from years to months

Unlocking Mysteries of the Universe

January 30, 2018

Teaching machines to analyze simulations of exotic subatomic ‘soup’

JCAP Study Uses Neural Net to Predict Materials' Optical Properties