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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.
Attribute-Guided Adversarial Training
More research accepted at AAAI 2021 enables DNNs to be robust against a wide range of naturally occurring perturbations.
More research accepted at AAAI 2021 enables DNNs to be robust against a wide range of naturally occurring perturbations.
Two NeurIPS Acceptances
The 34th Conference on Neural Information Processing Systems features research on the reliability of DL for mission-critical applications.
The 34th Conference on Neural Information Processing Systems features research on the reliability of DL for mission-critical applications.
Uncertainty-Matching GNNs
This paper, accepted at AAAI 2021, introduces the Uncertainty Matching Graph Neural Network aimed at improving the robustness of GNN models.
This paper, accepted at AAAI 2021, introduces the Uncertainty Matching Graph Neural Network aimed at improving the robustness of GNN models.
Feature Importance Estimation
In this paper accepted at AAAI 2021, a research team describes PRoFILE, a novel feature importance estimation method.
In this paper accepted at AAAI 2021, a research team describes PRoFILE, a novel feature importance estimation method.
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.
As our booth and poser at the 2020 LLNL Computing Virtual Expo explained, our work is a partnership between ML, HPC, and Lab programs.
Calibration with NN Surrogates
Calibration of an agent-based epidemiological model (EpiCast) uses simulation ensembles for different U.S. metro areas. Peer review pending.
Calibration of an agent-based epidemiological model (EpiCast) uses simulation ensembles for different U.S. metro areas. Peer review pending.
ML Optimization in Epidemiology
A pandemic-relevant paper investigates optimal policy recommendations in healthcare. Peer review pending.
A pandemic-relevant paper investigates optimal policy recommendations in healthcare. Peer review pending.
AI Accelerators in HPC
CASC group leader Brian Van Essen talks to the Next Platform about the convergence of HPC and AI tech.
CASC group leader Brian Van Essen talks to the Next Platform about the convergence of HPC and AI tech.
Meet an ML Expert
Rushil Anirudh, PhD, reconstructs computed tomography images using convolutional neural networks from partially scanned objects.
Rushil Anirudh, PhD, reconstructs computed tomography images using convolutional neural networks from partially scanned objects.
Two ICML Acceptances
Two papers featuring CASC scientists were accepted in the 2020 International Conference on Machine Learning (ICML).
Two papers featuring CASC scientists were accepted in the 2020 International Conference on Machine Learning (ICML).
Livermore Big Artificial Neural Network Toolkit
LBANN is an open-source, HPC-centric, DL training framework optimized to compose multiple levels of parallelism.
LBANN is an open-source, HPC-centric, DL training framework optimized to compose multiple levels of parallelism.
DL-Based Surrogate Models
Surrogate models supported by neural networks could lead to new insights in complicated physics problems.
Surrogate models supported by neural networks could lead to new insights in complicated physics problems.