Publications (12)
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…
Characterization and valuation of uncertainty of calibrated parameters in stochastic decision models
Fernando Alarid-Escudero, Amy B. Knudsen, Jonathan Ozik +2
We evaluated the implications of different approaches to characterize uncertainty of calibrated parameters of stochastic decision models (DMs) in the quantified value of such uncer…
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…
Bayesian calibration of stochastic agent based model via random forest
Connor Robertson, Cosmin Safta, Nicholson Collier +2
Agent-based models (ABM) provide an excellent framework for modeling outbreaks and interventions in epidemiology by explicitly accounting for diverse individual interactions and en…
PSI/J: A Portable Interface for Submitting, Monitoring, and Managing Jobs
Mihael Hategan-Marandiuc, Andre Merzky, Nicholson Collier +10
It is generally desirable for high-performance computing (HPC) applications to be portable between HPC systems, for example to make use of more performant hardware, make effective…
A portfolio approach to massively parallel Bayesian optimization
Mickael Binois, Nicholson Collier, Jonathan Ozik
One way to reduce the time of conducting optimization studies is to evaluate designs in parallel rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versio…