Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales. - Ian T. Foster; Mark Ainsworth; Bryce Allen; Julie Bessac; Franck Cappello; Jong Youl Choi; Emil M. Constantinescu; Philip E. Davis; Sheng Di; Zichao Wendy Di; Hanqi Guo 001; Scott Klasky; et al. (Euro-Par, 2017)

Efficient computation of derivatives for solving optimization problems in R and Python using SWIG-generated interfaces to ADOL-C. - Kshitij Kulshreshtha; Sri Hari Krishna Narayanan; Julie Bessac; K. Macintyre (Optim. Methods Softw., 2018)

Global Sensitivity Analysis for Statistical Model Parameters. - Joseph L. Hart; Julie Bessac; Emil M. Constantinescu (SIAM/ASA J. Uncertain. Quantification, 2019)

Image Segmentation for Dust Detection Using Semi-supervised Machine Learning. - Manzhu Yu; Julie Bessac; Ling Xu; Aryya Gangopadhyay; Yingxi Rona Shi; Jianwu Wang 001 (IEEE BigData, 2020)

SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors. - Kai Zhao 008; Sheng Di; Xin Liang 001; Sihuan Li; Dingwen Tao; Julie Bessac; Zizhong Chen; Franck Cappello (IEEE BigData, 2020)

Statistical Treatment of Inverse Problems Constrained by Differential Equations-Based Models with Stochastic Terms. - Emil M. Constantinescu; Noémi Petra; Julie Bessac; Cosmin G. Petra (SIAM/ASA J. Uncertain. Quantification, 2020)

Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODE. - Johann Rudi; Julie Bessac; Amanda Lenzi (MSML, 2021)

Exploring Lossy Compressibility through Statistical Correlations of Scientific Datasets. - David Krasowska; Julie Bessac; Robert Underwood; Jon C. Calhoun; Sheng Di; Franck Cappello (DRBSD@SC, 2021)

Online data analysis and reduction: An important Co-design motif for extreme-scale computers. - Ian T. Foster; Mark Ainsworth; Julie Bessac; Franck Cappello; Jong Choi 001; Sheng Di; Zichao Wendy Di; Ali Murat Gok; Hanqi Guo 001; Kevin A. Huck; Christopher Kelly; Scott Klasky; et al. (Int. J. High Perform. Comput. Appl., 2021)

Power grid frequency prediction using spatiotemporal modeling. - Amanda Lenzi; Julie Bessac; Mihai Anitescu (Stat. Anal. Data Min., 2021)

Understanding the Effects of Modern Compressors on the Community Earth Science Model. - Robert Underwood; Julie Bessac; Sheng Di; Franck Cappello (DRBSD@SC, 2022)

Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification. - Daniel Getter; Julie Bessac; Johann Rudi; Yan Feng (CoRR, 2022)

A Lightweight, Effective Compressibility Estimation Method for Error-bounded Lossy Compression. - Arkaprabha Ganguli; Robert Underwood; Julie Bessac; David Krasowska; Jon C. Calhoun; Sheng Di; Franck Cappello (CLUSTER, 2023)

Black-box statistical prediction of lossy compression ratios for scientific data. - Robert Underwood; Julie Bessac; David Krasowska; Jon C. Calhoun; Sheng Di; Franck Cappello (Int. J. High Perform. Comput. Appl., 2023)

Neural networks for parameter estimation in intractable models. - Amanda Lenzi; Julie Bessac; Johann Rudi; Michael L. Stein (Comput. Stat. Data Anal., 2023)

Uncertainty Visualization Challenges in Decision Systems with Ensemble Data & Surrogate Models. - Sam Molnar; J. D. Laurence-Chasen; Yuhan Duan; Julie Bessac; Kristi Potter (UncertaintyVis@VIS, 2024)

Online learning of quadratic manifolds from streaming data for nonlinear dimensionality reduction and nonlinear model reduction. - Paul Schwerdtner; Prakash Mohan 004; Aleksandra Pachalieva; Julie Bessac; Daniel O'Malley; Benjamin Peherstorfer (CoRR, 2024)

Navigating Uncertainty: Challenges in Visualizing Ensemble Data and Surrogate Models for Decision Systems. - Kristi Potter; Sam Molnar; J. D. Laurence-Chasen; Yuhan Duan; Julie Bessac; Han-Wei Shen (IEEE Computer Graphics and Applications, 2025)

Operator Inference Aware Quadratic Manifolds with Isotropic Reduced Coordinates for Nonintrusive Model Reduction. - Paul Schwerdtner; Prakash Mohan 004; Julie Bessac; Marc T. Henry de Frahan; Benjamin Peherstorfer (CoRR, 2025)

Enhancing interpretability in generative modeling: statistically disentangled latent spaces guided by generative factors in scientific datasets. - Arkaprabha Ganguli; Nesar Ramachandra; Julie Bessac; Emil M. Constantinescu (Mach. Learn., 2025)