Many real world applications can be framed as multi-objective optimization problems, where
we wish to simultaneously optimize for multiple criteria. Bayesian optimization techniques for …

Freshly synthesized r-process elements in kilonovae ejecta imprint absorption features on
optical spectra, as observed in the GW170817 binary neutron star merger. These spectral …

Parametric multiscale tumor models have been used nowadays as tools to understand and
predict the behavior of tumor onset, development, and decrease under treatments. In order …

This survey gives an overview of Monte Carlo methodologies using surrogate models, for
dealing with densities that are intractable, costly, and/or noisy. This type of problem can be …

We model the long-term X-ray and ultraviolet (XUV) luminosity of TRAPPIST-1 to constrain
the evolving high-energy radiation environment experienced by its planetary system. Using …

Landslide run-out modeling is a powerful model-based decision support tool for landslide
hazard assessment and mitigation. Most landslide run-out models contain parameters that …

In kilonovae, freshly synthesized r-process elements imprint features on optical spectra, as
observed in AT2017gfo, the counterpart to the GW170817 binary neutron star merger …

In surveying an extensive library of 18,133 hypothetical antiperovskites (X 3 BA), we address
the challenges posed by conventional experimental and computational screening methods …

We introduce the DNNLikelihood, a novel framework to easily encode, through deep neural
networks (DNN), the full experimental information contained in complicated likelihood …

We study actively labeling streaming data, where an active learner is faced with a stream of
data points and must carefully choose which of these points to label via an expensive …