A key difference in how we in CASC use ML compared to the commercial sector is that a working model is rarely the ultimate goal. High-sensitivity predictive models lead to new insights and enable us to form new hypotheses about physical phenomena.
We are developing techniques that reveal the interpretable components in these often opaque models as well as approaches for effective communication between model and domain user. This strategy calls for novel techniques that combine human understanding and machine intelligence.
Explainable artificial intelligence (AI) lies at the intersection of ML, statistics, visualization, human–computer interaction, and more. This emerging research area is rapidly becoming not only a crucial capability for LLNL but also a core strength. CASC’s integrated research teams jointly tackle these challenges, earning widespread recognition for their contributions.
Feature Importance Estimation
In this paper accepted at AAAI 2021, a research team describes PRoFILE, a novel feature importance estimation method.
Meet a Data Science Expert
Jay Thiagarajan's research involves different types of large-scale, structured data that require the design of unique ML techniques.
Meet a Data Viz Expert
Harsh Bhatia, PhD, was a Lawrence Graduate Scholar and an LLNL postdoctoral researcher before joining CASC full time in 2017.
AI and Aging Materials
AI-driven data analytics provide opportunities to accelerate materials design and optimization.
AI Accelerators in HPC
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.