21 citations · 22 across the 5 of their papers we have counts for
6 papers
Simulation-Based Inference for Probabilistic Galaxy Detection and Deblending
Ismael Mendoza, Derek Hansen, Runjing Liu +6
Stage-IV dark energy wide-field surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will observe an unprecedented number density of galaxies. As…
Learning Physical Models that Can Respect Conservation Laws
Derek Hansen, Danielle C. Maddix, Shima Alizadeh +2
Recent work in scientific machine learning (SciML) has focused on incorporating partial differential equation (PDE) information into the learning process. Much of this work has foc…
A probabilistic model of ocean floats under ice
Derek Hansen, Drew Yarger
The Argo project deploys thousands of floats throughout the world's oceans. Carried only by the current, these floats take measurements such as temperature and salinity at depths o…
Scalable Bayesian Inference for Detection and Deblending in Astronomical Images
Derek Hansen, Ismael Mendoza, Runjing Liu +4
We present a new probabilistic method for detecting, deblending, and cataloging astronomical sources called the Bayesian Light Source Separator (BLISS). BLISS is based on deep gene…
Normalizing Flows for Knockoff-free Controlled Feature Selection
Derek Hansen, Brian Manzo, Jeffrey Regier
Controlled feature selection aims to discover the features a response depends on while limiting the false discovery rate (FDR) to a predefined level. Recently, multiple deep-learni…
A Randomized Missing Data Approach to Robust Filtering and Forecasting
Dobrislav Dobrev, Derek Hansen, Pawel Szerszen
We put forward a simple new randomized missing data (RMD) approach to robust filtering of state-space models, motivated by the idea that the inclusion of only a small fraction of a…