Machine learning on a mission
CASC logo overlaid on abstract graphic of ones and zeros with the earth superimposed over it
Center for Applied Scientific Computing

Machine Learning at CASC

CASC is looking for creative team members at all stages of their careers. We invite you to browse the information below and apply to our open positions. Please contact us with any questions.

Machine learning (ML) is revolutionizing scientific applications—developing new drugs, understanding cancer, creating fusion energy, inventing smart materials, and more.

At LLNL, ML has permeated virtually all aspects of our research. Our teams develop, adapt, and apply the latest advances to some of the most complex problems while using the some of the world’s most powerful supercomputers and advanced experiments.

Whether the need is representation learning to bridge the gap between computational models and large-scale experiments, computer vision and inverse problems to understand everything from satellite imagers to airport security scans, or fundamental research on ML safety and interpretability to promote trust and understanding, our unique research environment couples fundamental ML research with high-impact scientific endeavors.

Multidisciplinary teams working closely together are pushing the limits of what is considered possible. Driven by some of society’s most important challenges and enabled by ML, the future of large-scale science is happening first at LLNL.

Grid of images generated by AttGAN

Attribute-Guided Adversarial Training

More research accepted at AAAI 2021 enables DNNs to be robust against a wide range of naturally occurring perturbations.

NeurIPS logo

Two NeurIPS Acceptances

The 34th Conference on Neural Information Processing Systems features research on the reliability of DL for mission-critical applications.

part of illustration of the proposed UM-GNN showing a poisoned graph

Uncertainty-Matching GNNs

This paper, accepted at AAAI 2021, introduces the Uncertainty Matching Graph Neural Network aimed at improving the robustness of GNN models.

part of the Cifar-10C dataset shown as candlesticks

Feature Importance Estimation

In this paper accepted at AAAI 2021, a research team describes PRoFILE, a novel feature importance estimation method.

screen shot of part of the poster PDF

Advancing ML for Mission-Critical Applications

As our booth and poser at the 2020 LLNL Computing Virtual Expo explained, our work is a partnership between ML, HPC, and Lab programs.

charts showing comparisons of the marginals of posteriors

Calibration with NN Surrogates

Calibration of an agent-based epidemiological model (EpiCast) uses simulation ensembles for different U.S. metro areas. Peer review pending.

Graphs showing business opening policy optimization results

ML Optimization in Epidemiology

A pandemic-relevant paper investigates optimal policy recommendations in healthcare. Peer review pending.

Brian in video chat, picture in picture, with moderator

AI Accelerators in HPC

CASC group leader Brian Van Essen talks to the Next Platform about the convergence of HPC and AI tech.

Rushil Anirudh in front of a flowering tree

Meet an ML Expert

Rushil Anirudh, PhD, reconstructs computed tomography images using convolutional neural networks from partially scanned objects.

diagram of deep learning workflow

Two ICML Acceptances

Two papers featuring CASC scientists were accepted in the 2020 International Conference on Machine Learning (ICML).

data visualization that looks like two mountain peaks, seen from the side and top

Livermore Big Artificial Neural Network Toolkit

LBANN is an open-source, HPC-centric, DL training framework optimized to compose multiple levels of parallelism.

composite image of NIF target bay and supercomputer

DL-Based Surrogate Models

Surrogate models supported by neural networks could lead to new insights in complicated physics problems.