Search Results for author:
Found 12 papers, 4 papers with code
U-Trustworthy Models.Reliability, Competence, and Confidence in Decision-Making
Conventionally, trustworthy AI literature relies on the probabilistic framework and calibration as prerequisites for trustworthiness.
SHAPing the Gas: Understanding Gas Shapes in Dark Matter Haloes with Interpretable Machine Learning
In this study we explore a machine learning approach for modelling the dependence of gas shapes on dark matter and baryonic properties.
Cosmology and Nongalactic Astrophysics
PoPE: A population-based approach to model spatial structure of astronomical systems
We present a novel population-based Bayesian inference approach to model the average and population variance of spatial distribution of a set of observables from ensemble analysis of low signal-to-noise ratio measurements.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters
We study methods for reconstructing Bayesian uncertainties on dynamical mass estimates of galaxy clusters using convolutional neural networks (CNNs).
Cosmology and Nongalactic Astrophysics
Dark Energy Survey Year 1 Results: Cosmological Constraints from Cluster Abundances and Weak Lensing
We perform a joint analysis of the counts and weak lensing signal of redMaPPer clusters selected from the Dark Energy Survey (DES) Year 1 dataset.
Cosmology and Nongalactic Astrophysics
Stellar Property Statistics of Massive Halos from Cosmological Hydrodynamics Simulations: Common Kernel Shapes
The highest resolution simulations find $\gamma \simeq -0. 8$ for the $z=0$ shape of $p(\ln M_{\star,\rm BCG}\,|\, M_{\rm halo}, z)$ and also that the fractional scatter in total stellar mass is below $10\%$ in halos more massive than $10^{14. 3} M_{\odot}$.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
The Role of Machine Learning in the Next Decade of Cosmology
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters
Our first model, CNN$_\text{1D}$, infers cluster mass directly from the distribution of member galaxy line-of-sight velocities.
Cosmology and Nongalactic Astrophysics
ActiveRemediation: The Search for Lead Pipes in Flint, Michigan
We detail our ongoing work in Flint, Michigan to detect pipes made of lead and other hazardous metals.
Driving with Data: Modeling and Forecasting Vehicle Fleet Maintenance in Detroit
Understanding the existence of patterns and trends in this data could be useful to a variety of stakeholders, particularly as Detroit emerges from Chapter 9 bankruptcy, but the patterns in such data are often complex and multivariate and the city lacks dedicated resources for detailed analysis of this data.
Computers and Society
A Data Science Approach to Understanding Residential Water Contamination in Flint
This is the nation's largest dataset on lead in a municipal water system.
Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing
In this analysis, we find that lead service lines are not the only factor that is predictive of the risk of lead contamination of water.
