ORAU
Learning-based Modeling Approach for Information Asset Valuation and Selection
ORAU, Adelphi, Maryland, United States
Overview
Learning-based Modeling Approach for Information Asset Valuation and Selection — a research opportunity at ORAU in collaboration with the DEVCOM Army Research Laboratory (ARL).
This opportunity aims to develop methods for information assets selection and content filtering from high-dimensional data, focusing on context-aware and adaptive learning models and confidence quantification of models.
About the Research This research opportunity develops novel methods for information assets selection and content filtering from high dimensional data. It will develop and validate an approach for context-aware and adaptive learning models for selecting the most relevant and valuable information assets from high-dimensional streaming data, with quantification of confidence levels on the models. The project focuses on utilizing state-of-the-art machine learning algorithms to dynamically adapt to, or learn from, human or agent actions and contextual situations and environments.
Organization DEVCOM Army Research Laboratory
Reference Code ARL-C-CISD-300020
Description About the Research: This research opportunity is to develop novel methods for information assets selection and content filtering from high dimensional data. Specifically, the opportunity will develop and validate an approach for context aware and adaptive learning models for selecting the most relevant and valuable information assets from high dimensional streaming data and quantification approaches to confidence levels on the models. Specifically, this project focuses on utilizing state-of-the-art machine learning algorithms to dynamically adapt to, or learn from human or agent actions and contextual situations and environments.
Keywords:
AI, Machine Learning, Computational modeling, Optimization, Distribution theories, Statistical Inference, Modeling, and Simulation
ARL Advisor:
Jade Freeman
ARL Advisor Email:
jade.l.freeman2.civ@mail.mil
ARL Co-Advisor:
Jesse M Milzman
ARL Co-Advisor Email:
jesse.m.milzman.civ@army.mil
About CISD The Computational and Information Sciences Directorate (CISD) conducts research in a variety of disciplines relevant to achieving and implementing the so-called digital battlefield. Problems address the sensing, distribution, analysis, and display of information in the modern battle space. CISD research focuses on four major areas: communications, atmospheric modeling, battlefield visualization, and computing.
About ARL-RAP The Army Research Laboratory Research Associateship Program (ARL-RAP) is designed to significantly increase the involvement of creative and highly trained scientists and engineers from academia and industry in scientific and technical areas of interest and relevance to the Army. Scientists and Engineers at the CCDC Army Research Laboratory (ARL) help shape and execute the Army's program for meeting the challenge of developing technologies that will support Army forces in meeting future operational needs by pursuing scientific research and technological developments in diverse fields such as: applied mathematics, atmospheric characterization, simulation and human modeling, digital/optical signal processing, nanotechnology, material science and technology, multifunctional technology, combustion processes, propulsion and flight physics, communication and networking, and computational and information sciences.
Application Materials A complete application includes:
Curriculum Vitae or Resume
Three References Forms
An email with a link to the reference form will be available in Zintellect to the applicant upon completion of the online application. Please send this email to persons you have selected to complete a reference.
References should be from persons familiar with your educational and professional qualifications (include your thesis or dissertation advisor, if applicable).
Transcripts
Transcript verifying receipt of degree must be submitted with the application. Student/unofficial copy is acceptable.
If selected by an advisor the participant will also be required to write a
research proposal
to submit to the ARL-RAP review panel for :
Research topic should relate to a specific opportunity at ARL (see Research Areas)
The objective of the research topic should be clear and have a defined outcome
Explain the direction you plan to pursue
Include expected period for completing the study
Include a brief background such as preparation and motivation for the research
References of published efforts may be used to improve the proposal
A link to upload the proposal will be provided to the applicant once the advisor has made their selection.
Questions about this opportunity?
Please email ARLFellowship@orau.org
Point of Contact
ARL
Eligibility Requirements
Degree: Master’s Degree or Doctoral Degree.
Academic Level(s): Any academic level.
Discipline(s):
Computer, Information, and Data Sciences
Artificial Intelligence (including Robotics, Computer Vision, and Human Language Processing)
Computer Architecture and Grids
Computer Science - Languages and Systems
Computer Science - Theoretical Foundations
Computer Science (general)
Computer Systems Analysis
Computer Systems Design (including Signal Processing)
Databases, Information Retrieval, and Web Search
Graphics and Visualization
Human Computer Interaction
Information Science and Technology
Information Security and Assurance
Networks and Communications
Operating Systems and Middleware
Scientific Computing and Informatics
Software Engineering
Mathematics and Statistics
Age: Must be 18 years of age
Seniority level Internship
Employment type Full-time
Job function Human Resources
Industries Government Administration
#J-18808-Ljbffr
This opportunity aims to develop methods for information assets selection and content filtering from high-dimensional data, focusing on context-aware and adaptive learning models and confidence quantification of models.
About the Research This research opportunity develops novel methods for information assets selection and content filtering from high dimensional data. It will develop and validate an approach for context-aware and adaptive learning models for selecting the most relevant and valuable information assets from high-dimensional streaming data, with quantification of confidence levels on the models. The project focuses on utilizing state-of-the-art machine learning algorithms to dynamically adapt to, or learn from, human or agent actions and contextual situations and environments.
Organization DEVCOM Army Research Laboratory
Reference Code ARL-C-CISD-300020
Description About the Research: This research opportunity is to develop novel methods for information assets selection and content filtering from high dimensional data. Specifically, the opportunity will develop and validate an approach for context aware and adaptive learning models for selecting the most relevant and valuable information assets from high dimensional streaming data and quantification approaches to confidence levels on the models. Specifically, this project focuses on utilizing state-of-the-art machine learning algorithms to dynamically adapt to, or learn from human or agent actions and contextual situations and environments.
Keywords:
AI, Machine Learning, Computational modeling, Optimization, Distribution theories, Statistical Inference, Modeling, and Simulation
ARL Advisor:
Jade Freeman
ARL Advisor Email:
jade.l.freeman2.civ@mail.mil
ARL Co-Advisor:
Jesse M Milzman
ARL Co-Advisor Email:
jesse.m.milzman.civ@army.mil
About CISD The Computational and Information Sciences Directorate (CISD) conducts research in a variety of disciplines relevant to achieving and implementing the so-called digital battlefield. Problems address the sensing, distribution, analysis, and display of information in the modern battle space. CISD research focuses on four major areas: communications, atmospheric modeling, battlefield visualization, and computing.
About ARL-RAP The Army Research Laboratory Research Associateship Program (ARL-RAP) is designed to significantly increase the involvement of creative and highly trained scientists and engineers from academia and industry in scientific and technical areas of interest and relevance to the Army. Scientists and Engineers at the CCDC Army Research Laboratory (ARL) help shape and execute the Army's program for meeting the challenge of developing technologies that will support Army forces in meeting future operational needs by pursuing scientific research and technological developments in diverse fields such as: applied mathematics, atmospheric characterization, simulation and human modeling, digital/optical signal processing, nanotechnology, material science and technology, multifunctional technology, combustion processes, propulsion and flight physics, communication and networking, and computational and information sciences.
Application Materials A complete application includes:
Curriculum Vitae or Resume
Three References Forms
An email with a link to the reference form will be available in Zintellect to the applicant upon completion of the online application. Please send this email to persons you have selected to complete a reference.
References should be from persons familiar with your educational and professional qualifications (include your thesis or dissertation advisor, if applicable).
Transcripts
Transcript verifying receipt of degree must be submitted with the application. Student/unofficial copy is acceptable.
If selected by an advisor the participant will also be required to write a
research proposal
to submit to the ARL-RAP review panel for :
Research topic should relate to a specific opportunity at ARL (see Research Areas)
The objective of the research topic should be clear and have a defined outcome
Explain the direction you plan to pursue
Include expected period for completing the study
Include a brief background such as preparation and motivation for the research
References of published efforts may be used to improve the proposal
A link to upload the proposal will be provided to the applicant once the advisor has made their selection.
Questions about this opportunity?
Please email ARLFellowship@orau.org
Point of Contact
ARL
Eligibility Requirements
Degree: Master’s Degree or Doctoral Degree.
Academic Level(s): Any academic level.
Discipline(s):
Computer, Information, and Data Sciences
Artificial Intelligence (including Robotics, Computer Vision, and Human Language Processing)
Computer Architecture and Grids
Computer Science - Languages and Systems
Computer Science - Theoretical Foundations
Computer Science (general)
Computer Systems Analysis
Computer Systems Design (including Signal Processing)
Databases, Information Retrieval, and Web Search
Graphics and Visualization
Human Computer Interaction
Information Science and Technology
Information Security and Assurance
Networks and Communications
Operating Systems and Middleware
Scientific Computing and Informatics
Software Engineering
Mathematics and Statistics
Age: Must be 18 years of age
Seniority level Internship
Employment type Full-time
Job function Human Resources
Industries Government Administration
#J-18808-Ljbffr