We are exploring advances in sampling design for computational modeling to produce the maximal amount of information about unknown processes with the minimal number of samples. Applications include uncertainty quantification for stockpile stewardship and weapons design, improved weather and climate ensembles, optimization of the energy grid, and improved sensitivity analysis in materials science and high-energy physics.
Kailkhura, B., et al., 2018. "A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms." J. Mach Learn Res. 19. LLNL-JRNL-743060.
Song, H., et al., 2018. "Triplet Network with Attention for Speaker Diarization." Interspeech. LLNL-PROC-755003.