64 citations · 66 across the 9 of their papers we have counts for
9 papers
Staying on Track: Efficient Trajectory Discovery with Adaptive Batch Sampling
Arindam Fadikar, Abby Stevens, Mickael Binois +3
Bayesian optimization (BO) is a powerful framework for estimating parameters of expensive simulation models, particularly in settings where the likelihood is intractable and evalua…
AERO: An autonomous platform for continuous research
Valérie Hayot-Sasson, Abby Stevens, Nicholson Collier +10
The COVID-19 pandemic highlighted the need for new data infrastructure, as epidemiologists and public health workers raced to harness rapidly evolving data, analytics, and infrastr…
Gearing Gaussian process modeling and sequential design towards stochastic simulators
Mickael Binois, Arindam Fadikar, Abby Stevens
This chapter presents specific aspects of Gaussian process modeling in the presence of complex noise. Starting from the standard homoscedastic model, various generalizations from t…
Towards Improved Uncertainty Quantification of Stochastic Epidemic Models Using Sequential Monte Carlo
Arindam Fadikar, Abby Stevens, Nicholson Collier +6
Sequential Monte Carlo (SMC) algorithms represent a suite of robust computational methodologies utilized for state estimation and parameter inference within dynamical systems, part…
NSF RESUME HPC Workshop: High-Performance Computing and Large-Scale Data Management in Service of Epidemiological Modeling
Abby Stevens, Jonathan Ozik, Kyle Chard +2
The NSF-funded Robust Epidemic Surveillance and Modeling (RESUME) project successfully convened a workshop entitled "High-performance computing and large-scale data management in s…
Developing Distributed High-performance Computing Capabilities of an Open Science Platform for Robust Epidemic Analysis
Nicholson Collier, Justin M. Wozniak, Abby Stevens +6
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among domain expert…
Trajectory-oriented optimization of stochastic epidemiological models
Arindam Fadikar, Mickael Binois, Nicholson Collier +3
Epidemiological models must be calibrated to ground truth for downstream tasks such as producing forward projections or running what-if scenarios. The meaning of calibration change…
Lazy Estimation of Variable Importance for Large Neural Networks
Yue Gao, Abby Stevens, Rebecca Willet +1
As opaque predictive models increasingly impact many areas of modern life, interest in quantifying the importance of a given input variable for making a specific prediction has gro…
Aequitas: A Bias and Fairness Audit Toolkit
Pedro Saleiro, Benedict Kuester, Loren Hinkson +5
Recent work has raised concerns on the risk of unintended bias in AI systems being used nowadays that can affect individuals unfairly based on race, gender or religion, among other…