“Andrew is a great colleague, smart and inventive, keeps his promises, and retains his focus on the functional purposes of software systems (what the users should be able to do to solve their problems) rather than getting sidetracked into the technical details of how the software works.”
About
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It's time to pack your bags. Don't forget your boots, hat and sparkly shoes! See you in Dallas for the DOE Cybersecurity and Technology Innovation…
It's time to pack your bags. Don't forget your boots, hat and sparkly shoes! See you in Dallas for the DOE Cybersecurity and Technology Innovation…
Liked by Andrew Cowell
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PNNL is proud to support the Pacific Northwest Hydrogen Hub, which just received it first traunche of funding. We're helping to make clean hydrogen…
PNNL is proud to support the Pacific Northwest Hydrogen Hub, which just received it first traunche of funding. We're helping to make clean hydrogen…
Liked by Andrew Cowell
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The Data Sciences & Machine Intelligence group at PNNL is seeking a multifaceted Data Scientist 1:
The Data Sciences & Machine Intelligence group at PNNL is seeking a multifaceted Data Scientist 1:
Liked by Andrew Cowell
Experience & Education
Publications
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Assessment of User Home Location Geoinference Methods
IEEE International Conference on Intelligence and Security Informatics
This study presents an assessment of multiple approaches to determine the home and/or other important locations to a Twitter user. In this study, we present a unique approach to the problem of geotagged data sparsity in social media when performing geoinferencing tasks. Given the sparsity of explicitly geotagged Twitter data, the ability to perform accurate and reliable user geolocation from a limited number of geotagged posts has proven to be quite useful. In our survey, we have achieved…
This study presents an assessment of multiple approaches to determine the home and/or other important locations to a Twitter user. In this study, we present a unique approach to the problem of geotagged data sparsity in social media when performing geoinferencing tasks. Given the sparsity of explicitly geotagged Twitter data, the ability to perform accurate and reliable user geolocation from a limited number of geotagged posts has proven to be quite useful. In our survey, we have achieved accuracy rates of over 86% in matching Twitter user profile locations with their inferred home locations derived from geotagged posts.
Other authors -
Knowledge Encapsulation Framework for Technosocial Predictive Modeling
Security Informatics/Springer
Analysts who use predictive analytics methods need actionable evidence to support their models and simulations. Commonly, this evidence is distilled from large data sets with significant amount of culling and searching through a variety of sources including traditional and social media. The time/cost effectiveness and quality of the evidence marshaling process can be greatly enhanced by combining component technologies that support directed content harvesting, automated semantic annotation, and…
Analysts who use predictive analytics methods need actionable evidence to support their models and simulations. Commonly, this evidence is distilled from large data sets with significant amount of culling and searching through a variety of sources including traditional and social media. The time/cost effectiveness and quality of the evidence marshaling process can be greatly enhanced by combining component technologies that support directed content harvesting, automated semantic annotation, and content analysis within a collaborative environment, with a functional interface to models and simulations. Existing evidence extraction tools provide some, but not all, the critical components that would empower such an integrated knowledge management environment. This paper describes a novel evidence marshaling solution that significantly advances the state of the art. Its embodiment, the Knowledge Encapsulation Framework (KEF), offers a suite of semi-automated and configurable content harvesting, vetting, annotation and analysis capabilities within a wiki-enabled and user-friendly visual interface that supports collaborative work across distributed teams of analysts. After a summarization of related work, our motivation, and the technical implementation of KEF, we will explore the model for using KEF and results of our research.
Other authorsSee publication -
Construction and Validation of a Neurophysiotechnological Framework for Imagery Analysis
Human-Computer Interaction International (HCII)
Intelligence analysts are bombarded with enormous volumes of imagery, which they must visually filter through to identify relevant areas of interest. Interpretation of such data is subject to error due to (1) large data volumes, implying the need for faster and more effective processing, and (2) misinterpretation, implying the need for enhanced analyst/system effectiveness. This paper outlines the Revolutionary Accelerated Processing Image Detection (RAPID) System, designed to significantly…
Intelligence analysts are bombarded with enormous volumes of imagery, which they must visually filter through to identify relevant areas of interest. Interpretation of such data is subject to error due to (1) large data volumes, implying the need for faster and more effective processing, and (2) misinterpretation, implying the need for enhanced analyst/system effectiveness. This paper outlines the Revolutionary Accelerated Processing Image Detection (RAPID) System, designed to significantly improve data throughput and interpretation by incorporating advancing neurophysiological technology to monitor processes associated with detection and identification of relevant target stimuli in a non-invasive and temporally precise manner. Specifically, this work includes the development of innovative electroencephalographic (EEG) and eye tracking technologies to detect and flag areas of interest, potentially without an analyst’s conscious intervention or motor responses, while detecting and
mitigating problems with tacit knowledge, such as anchoring bias in real-time to reduce the possibility of human error.Other authors -
Supporting Mutual Understanding in a Visual Dialogue between Analyst and Computer
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
The Knowledge Associates for Novel Intelligence (KANI) project is developing a system of automated “associates” to actively support and participate in the information analysis task. The primary goal of KANI is to use automatically extracted information in a reasoning system that draws on the strengths of both a human analyst and automated reasoning. The interface between the two agents is a key element in achieving this goal. The KANI interface seeks to support a visual dialogue with…
The Knowledge Associates for Novel Intelligence (KANI) project is developing a system of automated “associates” to actively support and participate in the information analysis task. The primary goal of KANI is to use automatically extracted information in a reasoning system that draws on the strengths of both a human analyst and automated reasoning. The interface between the two agents is a key element in achieving this goal. The KANI interface seeks to support a visual dialogue with mixed-initiative manipulation of information and reasoning components. The interface must achieve mutual understanding between the analyst and KANI of the other's actions. Toward this mutual understanding, KANI allows the analyst to work at multiple levels of abstraction over the reasoning process, links the information presented across these levels to make use of interaction context, and provides querying facilities to allow exploration and explanation.
Other authorsSee publication
Patents
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Automatic Identification of Online Abstract Groups
Issued US 8,700,629
Online abstract groups, in which members aren't explicitly connected, can be automatically identified by computer-implemented methods. The methods involve harvesting records from social media and extracting content-based and structure-based features from each record. Each record includes a social-media posting and is associated with one or more entities. Each feature is stored on a data storage device and includes a computer-readable representation of an attribute of one or more records. The…
Online abstract groups, in which members aren't explicitly connected, can be automatically identified by computer-implemented methods. The methods involve harvesting records from social media and extracting content-based and structure-based features from each record. Each record includes a social-media posting and is associated with one or more entities. Each feature is stored on a data storage device and includes a computer-readable representation of an attribute of one or more records. The methods further involve grouping records into record groups according to the features of each record. Further still the methods involve calculating an n-dimensional surface representing each record group and defining an outlier as a record having feature-based distances measured from every n-dimensional surface that exceed a threshold value. Each of the n-dimensional surfaces is described by a footprint that characterizes the respective record group as an online abstract group.
Other inventorsSee patent -
Isolating Desired Content, Metadata or Both from Social Media
Issued US 8,239,425
Desired content, metadata, or both can be isolated from the
full content of social media websites having content-rich
pages. Achieving this can include obtaining from the content rich
pages a language-independent representation having a
hierarchical structure of nodes and then generating a node
representation for each node. Feature vectors for the nodes
are generated and a label is assigned to each node representation
according to a schema. Assignment can occur…Desired content, metadata, or both can be isolated from the
full content of social media websites having content-rich
pages. Achieving this can include obtaining from the content rich
pages a language-independent representation having a
hierarchical structure of nodes and then generating a node
representation for each node. Feature vectors for the nodes
are generated and a label is assigned to each node representation
according to a schema. Assignment can occur by
executing a trained classification algorithm on the feature
vectors. The schema has schema elements and each schema
element corresponds to a label. For each schema element, all
node representations having matching labels are gathered and
then one node representation is elected from among those
with matching labels to be assigned to a schema element field
in a template. The template can be applied to extract desired
content, metadata, or both according to the schema from all
the content-rich pages.Other inventors -
Hypothesis analysis methods, hypothesis analysis devices, and articles of manufacture
Issued US 8140464
Abstract: Hypothesis analysis methods, hypothesis analysis devices, and articles of manufacture are described according to some aspects. In one aspect, a hypothesis analysis method includes providing a hypothesis, providing an indicator which at least one of supports and refutes the hypothesis, using the indicator, associating evidence with the hypothesis, weighting the association of the evidence with the hypothesis, and using the weighting, providing information regarding the accuracy of the…
Abstract: Hypothesis analysis methods, hypothesis analysis devices, and articles of manufacture are described according to some aspects. In one aspect, a hypothesis analysis method includes providing a hypothesis, providing an indicator which at least one of supports and refutes the hypothesis, using the indicator, associating evidence with the hypothesis, weighting the association of the evidence with the hypothesis, and using the weighting, providing information regarding the accuracy of the hypothesis.
Other inventorsSee patent
Projects
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Exploiting the Online Social Landscape
This research addresses the scientific and engineering challenges involved with quickly adapting to new online social technologies.
Other creatorsSee project -
Technosocial Predictive Analytics
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Technosocial Predictive Analytics support a multi-perspective approach to predictive analysis through integrated reasoning, drawing knowledge insights from both the natural and social sciences. More specifically, Technosocial Predictive Analytics defines, develops, and evaluates novel modeling algorithms that integrate domain knowledge about interacting physical and human factors. In so doing, Technosocial Predictive Analytics enables its modeling algorithms with ancillary capabilities aimed at…
Technosocial Predictive Analytics support a multi-perspective approach to predictive analysis through integrated reasoning, drawing knowledge insights from both the natural and social sciences. More specifically, Technosocial Predictive Analytics defines, develops, and evaluates novel modeling algorithms that integrate domain knowledge about interacting physical and human factors. In so doing, Technosocial Predictive Analytics enables its modeling algorithms with ancillary capabilities aimed at acquiring knowledge inputs and enhancing cognitive access.
Other creatorsSee project
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Join now to viewMore activity by Andrew
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We're looking for our next Associate Laboratory Director for Physical and Computational Sciences at PNNL. If you are ready for an executive role…
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As many of you know, my principal deputy, Brian Epley has been promoted to CIO at the Department of Commerce. That means we're hiring a new PDCIO!…
As many of you know, my principal deputy, Brian Epley has been promoted to CIO at the Department of Commerce. That means we're hiring a new PDCIO!…
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The upcoming #DOECyberCon event is bursting with thrilling sessions and dynamic speakers! Next Wednesday, be sure to catch the morning keynote speech…
The upcoming #DOECyberCon event is bursting with thrilling sessions and dynamic speakers! Next Wednesday, be sure to catch the morning keynote speech…
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🌊🏆 Thrilled to announce our double win with iAwards of Sustainability and Environmental and merit iAwards of Government and Public Sector for our…
🌊🏆 Thrilled to announce our double win with iAwards of Sustainability and Environmental and merit iAwards of Government and Public Sector for our…
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Researchers at Pacific Northwest National Laboratory are working to combat the hazards of fentanyl. In the Domestic Preparedness article “Fentanyl…
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It's been an incredible year working towards this announcement with colleagues from the 17 Department of Energy National Laboratories and exceptional…
It's been an incredible year working towards this announcement with colleagues from the 17 Department of Energy National Laboratories and exceptional…
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Check out Northeastern University Seattle unique Econ and Data Science masters program!
Check out Northeastern University Seattle unique Econ and Data Science masters program!
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Two wonderful days with my “graph tribe” for https://graphex.mit.edu at the beautiful MIT Endicott house. Thank you MIT Lincoln Laboratory for…
Two wonderful days with my “graph tribe” for https://graphex.mit.edu at the beautiful MIT Endicott house. Thank you MIT Lincoln Laboratory for…
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🎉 Congrats to Ben Bond-Lamberty, a colleague and Earth Scientist at the @Pacific Northwest National Laboratory. He is one of 3 new Laboratory…
🎉 Congrats to Ben Bond-Lamberty, a colleague and Earth Scientist at the @Pacific Northwest National Laboratory. He is one of 3 new Laboratory…
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We've unveiled a new artificial intelligence initiative. Frontiers in AI Science, Security, and Technology (FASST), will leverage DOE’s…
We've unveiled a new artificial intelligence initiative. Frontiers in AI Science, Security, and Technology (FASST), will leverage DOE’s…
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