U.S. Department of Energy

Pacific Northwest National Laboratory

Research Projects

This research is developing a statistical framework for generating composite signatures that will enable investigators in bioforensic programs to integrate disparate data forms and trace them back to their sources. The first set includes traditional experimental forensic approaches including... read more
This project aims to develop signature discovery applications based on new "compressive sensing" algorithms, which reconstruct full resolution observations or signatures from data streams that are incomplete, corrupted, or under-sampled. The project addresses the problem of developing a signature-... read more
We are developing a process for studying the drift and degradation of signatures arising from sensor platforms with the express aim of improving confidence in the signature-building process. Implementation of this process will allow us to understand the effect that controllable and uncontrollable... read more
This project involves the development of a software library to encapsulate the statistical model layer of (BLAST) calculations by providing an abstraction of currently hard-coded elements in the NCBI distribution of the BLAST algorithm source code. Through this abstraction, researchers will provide... read more
A primary challenge facing biomarker scientists is the need to integrate expert-driven and data-driven approaches to biomarker discovery. To date, exclusive reliance on either approach has proven to be unsatisfactory. Researchers will tackle this problem using an extensive set of proteomic and... read more
In this project, researchers are developing a framework for discovering signature features of complex or poorly characterized phenomena. This involves a two-stage framework. In the first stage, a greedy strategy is used to discover new features that are potentially relevant to the underlying... read more
This project will develop a signature construction methodology for applications that include both exploratory data analysis of large volumes of measurement data and physics-based forward models that describe how the data are related to the phenomenon of interest. Often, cutting edge sensors produce... read more
Finding a time-ordered signature within large graphs is a computationally complex problem due to the combinatorial explosion of potential patterns. The Graph Analytic Approach for Discovering Irregular Events Nascent and Temporal (GRADIENT) project is designed to search and understand that problem... read more
Hierarchical signature detection uses an integrated approach that leverages both disaggregation techniques and Bayesian evidence accrual methods to accelerate detection tasks in distributed systems defined by high-throughput, multi-INT data. Approach Hierarchical signature detection (HSD)... read more
This project develops, validates, and demonstrates the use of model-based data signatures in a variety of nuclear intent analytic challenges. Detecting and identifying the intent to develop (or further) a nuclear weapons program requires inputs from diverse sets of expertise, and requires... read more
Researchers are developing methods, measures, and algorithms to partially automate the construction of signature discovery workflows and to identify new composite signatures. A workflow built to serve one domain (e.g., bioforensics or biomarker discovery) may have individual analytic components,... read more
In this project, researchers are developing and maintaining an analytic framework that will integrate a set of interoperable tools, methods, and data repositories for use by researchers across multiple projects. The flexible and extensible architecture being developed will implement the signature... read more
Signature systems designed to detect, predict, or characterize a phenomenon of interest are developed by scientists and engineers in a wide variety of contexts. Regardless of the domain, every signature discovery effort needs a transparent approach for evaluating the quality of the resultant... read more

Application Projects

Employing genetic algorithms and a variety of regression models, our methodology globally searches a vast problem space to find relevant biometric features for a re-identification task, tackling the central problem for one of the SDI seed projects. For this seed project, our approach separated... read more
Cloud feedbacks remain one of the largest uncertainties in climate models and contribute to key modeling errors in atmospheric processes like dynamics and precipitation. A more quantitatively rigorous identification of phase from remote sensors can improve process model simulations of the radiative... read more
Advancing the understanding of microbiome conditions in the context of the most commonly performed bariatric surgery in the United States, the Roux-en-Y (RYGB) gastric bypass. A microbiome is "the ecological community of commensal, symbiotic, and pathogenic microorganisms that literally share our... read more
We are characterizing a semi-arid soil microbiome and elucidating its response to environmental perturbation. Building on existing site knowledge, additional data on the organismal composition of the microbiome (metagenome sequencing), and its respiratory activity (CO2 evolution and enzymatic... read more
  Approach The SINT project uses SDI tools on large, heterogeneous, and messy data sets to discover signatures of procurement networks. We are employing SDI's GraphScape software to compare networks over time, across companies, and spanning industries. We are conducting exploratory data analysis to... read more
In the event that the International Monitoring System detects an apparent underground explosion that is lacking the necessary radionuclide signatures necessary to confirm a nuclear event, what signatures should be exploited to determine whether the event was a chemical rather than nuclear explosion... read more
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