20 citations · 43 across the 13 of their papers we have counts for
14 papers
A Novel Alternating Joint Longitudinal Model for Post-ICU Hemoglobin Prediction
Gabriel Demuth, Curtis Storlie, Matthew A. Warner +3
Anemia is common in patients post-ICU discharge. However, which patients will develop or recover from anemia remains unclear. Prediction of anemia in this population is complicated…
A Hierarchical Bayesian Model for Stochastic Spatiotemporal SIR Modeling and Prediction of COVID-19 Cases and Hospitalizations
Curtis B. Storlie, Ricardo L. Rojas, Gabriel O. Demuth +18
Most COVID-19 predictive modeling efforts use statistical or mathematical models to predict national- and state-level COVID-19 cases or deaths in the future. These approaches assum…
Bayesian Calibration of Computer Models with Informative Failures
Peter W. Marcy, Curtis B. Storlie
There are many practical difficulties in the calibration of computer models to experimental data. One such complication is the fact that certain combinations of the calibration inp…
Modeling Material Stress Using Integrated Gaussian Markov Random Fields
Peter W. Marcy, Scott A. Vander Wiel, Curtis B. Storlie +2
The equations of a physical constitutive model for material stress within tantalum grains were solved numerically using a tetrahedrally meshed volume. The resulting output included…
Sequential Optimization in Locally Important Dimensions
Munir A. Winkel, Jonathan W. Stallings, Curt B. Storlie +1
Optimizing an expensive, black-box function is challenging when its input space is high-dimensional. Sequential design frameworks first model with a surrogate…
A Bayesian Approach to Multi-State Hidden Markov Models: Application to Dementia Progression
Jonathan P Williams, Curtis B Storlie, Terry M Therneau +2
People are living longer than ever before, and with this arises new complications and challenges for humanity. Among the most pressing of these challenges is of understanding the r…
Prediction of Individual Outcomes for Asthma Sufferers
Curtis B Storlie, Megan E Branda, Michael R Gionfriddo +2
We consider the problem of individual-specific medication level recommendation (initiation, removal, increase, or decrease) for asthma sufferers. Asthma is one of the most common c…
Prediction and Inference with Missing Data in Patient Alert Systems
Curtis B. Storlie, Terry M. Therneau, Rickey E. Carter +4
We describe the Bedside Patient Rescue (BPR) project, the goal of which is risk prediction of adverse events for non-ICU patients using ~200 variables (vitals, lab results, assessm…
Spatiotemporal Modeling of Node Temperatures in Supercomputers
Curtis B Storlie, Brian J Reich, William N Rust +4
Los Alamos National Laboratory (LANL) is home to many large supercomputing clusters. These clusters require an enormous amount of power (~500-2000 kW each), and most of this energy…
Upscaling Uncertainty with Dynamic Discrepancy for a Multi-scale Carbon Capture System
K. Sham Bhat, David S. Mebane, Curtis B. Storlie +1
Uncertainties from model parameters and model discrepancy from small-scale models impact the accuracy and reliability of predictions of large-scale systems. Inadequate representati…
Modeling and Predicting Power Consumption of High Performance Computing Jobs
Curtis Storlie, Joe Sexton, Scott Pakin +3
Power is becoming an increasingly important concern for large supercomputing centers. Due to cost concerns, data centers are becoming increasingly limited in their ability to enhan…
A Frequentist Approach to Computer Model Calibration
Raymond K. W. Wong, Curtis B. Storlie, Thomas C. M. Lee
This paper considers the computer model calibration problem and provides a general frequentist solution. Under the proposed framework, the data model is semi-parametric with a nonp…
Calibration of Computational Models with Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA
Curtis B. Storlie, William A. Lane, Emily M. Ryan +2
It has become commonplace to use complex computer models to predict outcomes in regions where data does not exist. Typically these models need to be calibrated and validated using…
Stochastic identification of malware with dynamic traces
Curtis Storlie, Blake Anderson, Scott Vander Wiel +3
A novel approach to malware classification is introduced based on analysis of instruction traces that are collected dynamically from the program in question. The method has been im…