Lawrence Livermore National Laboratory

The Applied Statistics group's mission is to bring statistical rigor and innovation to bear on complex problems of national importance in a highly collaborative environment. The group is distinguished by its strong focus on interdisciplinary research, which is made possible by the opportunity to work with a diverse group of leading scientists and engineers on challenging problems of national importance. In addition to the statistical research staff, we host a number of post-doctoral researchers, students, and visiting scholars maintaining strong ties to the academic community as well as our partners at other national laboratories.

The group's work ranges from introducing existing statistical methodology in novel application areas to developing new statistical theory motivated by unique and challenging data sets. Some examples of active research areas include: design of computer experiments, climate modeling, cyber security, image and video analysis, energy analysis, computational biology, lasers and optics, uncertainty quantification, computer model validation/calibration, and statistical computing on HPC. A common theme is that statistical applications at LLNL require scaling statistics and machine learning algorithms to operate on extremely large data sets generated by state-of-the-art collection technologies.

Group members work with a wide variety of collaborators and sponsors on projects ranging from fast-paced consulting activities to multi-year collaborations. Statistical analysis and methodologies developed at LLNL have been used by government decision makers at the national and international levels, and the group has also formed successful partnerships in the energy and healthcare industries.

Kassandra Fronczyk, 925-422-2098
Group Leader

ContactPhoneDiscipline Expertise
Clifford Anderson-Bergman 925-422-4385
Francisco Beltran 925-423-9276 Bayesian modeling, climate models
Jason Bernstein 925-422-9816 Time series analysis, state space models, particle filtering, uncertainty quantification, mixed effects models
Brenton Blair 925-423-3217 Bayesian Modeling, Classification and Machine Learning
Grant Boquet 925-423-7608 Classification and machine learning, anomaly detection, sonar/radar signal processing, over-determined systems of PDEs, special functions, algebraic geometry and computational algebra.
Kassandra Fronczyk 925-422-2098 Bayesian statistics, Bayesian nonparametrics, reliability, uncertainty quantification, analysis of dose-response studies, toxicology and nanomedical data, sparse factor models, data science
Ana De Oliveira Sales 925-423-8358 Bayesian statistics and machine learning applications, Bayesian nonparametrics, online learning, adaptive design of computer experiments, computational biology, and bioinformatics.
Cory Lanker 925-423-3583 Bayesian modeling, computer experiments, machine learning/statistical learning, predictive analytics, statistical consulting
Jason Lenderman 925-422-2632 Bayesian networks, anomaly detection, natural language processing, distributed machine learning, probabilistic combinatorics, graph algorithms
Giuliana Pallotta 925-422-2778 Bayesian inference, statistical modeling, machine learning, big data, anomaly detection, reliability theory, statistical process control, situational awareness, information fusion.
Kathleen Schmidt 925-423-1384 Uncertainty quantification, Bayesian modeling, mixed-effects modeling