Call for Contributed Talk and Poster Abstracts
Young professional authors and researchers are encouraged to submit abstracts that highlight or apply one of the workshop themes regarding defense and aerospace for the 2020 workshop. Academics, scientists, statisticians, and engineers active in the field will be present to evaluate selected entries. This also provides the opportunity to network and engage with the defense, aerospace, academic community, and industry practitioners. Contributed abstracts will be invited to provide a 15 minute presentation or asked to participate in a poster session. Contributed abstracts will also be eligible for consideration of a best contributed paper or best contributed poster award.
Workshop Overview
Defense and Aerospace Test and Analysis (DATA) Workshop is the result of a multi-organization collaboration with the Director of Operational Test & Evaluation within the Office of the Secretary of Defense, NASA, the Institute for Defense Analyses (IDA), and the Section on Statistics in Defense and National Security (SDNS) of the American Statistical Association (ASA). The workshop is strategically designed to strengthen the community leveraging rigorous statistical approaches to test design and analysis of data in defense and aerospace. The three-day workshop will have a mix of applied problems, unique methodological approaches, and tutorials from leading academics. The goal is to facilitate collaboration among all involved, including expanding our impact to other government agencies.
Theme Description
The DoD and NASA continue to develop and acquire some of the world’s most advanced and sophisticated systems and technology. Together we face similar challenges in ensuring that these systems undergo adequate and efficient test and evaluation prior to use. Sessions will feature case study examples highlighting advancements in T&E methodologies we have made as a community. Topics covered may include modeling and simulation validation, uncertainty quantification, the use of design of experiments, Bayesian analysis, amongst others. Sessions will also discuss the current challenges and areas where continued research and advancement are needed.
Theme Description
Sessions will discuss how we can quantify confidence that machine learning and/or artificial intelligence algorithms function as intended and are free of vulnerabilities, either intentionally or unintentionally designed or inserted as part of the data/algorithm. Topics may include test and evaluation metrics and methods for AI algorithms. Threat portrayal for ML/AI algorithms. Designing robust ML/AI algorithms. Test and evaluation metrics and methods for cyber-physical systems that incorporate AI algorithms.
Theme Description
Sessions will discuss metrics and test methodologies for testing cyber-physical systems with an emphasis on characterizing cyber resiliency. Identification of cybersecurity threats (IP and non-IP) across the critical operational missions. Methods for developing and testing secure architectures. Methods for identifying critical system components that enable attack vectors. Development and testing of countermeasures to cyber-attacks. Test methods for cyber-physical systems that include machine learning algorithms.
Theme Description
Mission outcomes are determined by how effectively operators interact with systems under operational conditions. Sessions will discuss methods used to collect and model the quality of human-system interaction and its impact on operational performance.
Theme Description
Sessions will cover simulation, prediction, uncertainty quantification, and inference for physical and physical-statistical modeling of geophysical processes. Examples of topics that will be discussed include problems in Earth Science (models of ice sheet evolution, atmospheric and ocean processes, the carbon cycle, land surface processes, natural hazards), astronomy and cosmology (exoplanet detection, galactic formation, cosmic microwave background), and Planetary Science (planetary atmospheres, formation, etc.).
Guide for Authors:
To submit your abstract, please complete the form below. Authors are invited to submit an abstract of no longer than 500 words.
Important Dates:
Deadline for submission: January 31, 2020
Notification of acceptance: February 14, 2020
Disclaimer: PHOTOGRAPHY AND/OR VIDEOTAPING
Upon acceptance, you understand and recognize that you may be photographed, filmed, videotaped, and/or tweeted and you hereby give DATAWorks the right to take pictures and/or recordings of you. You also grant the right for DATAWorks to use your image, recording, name, and affiliated institution, without compensation, for exhibition in any medium. If you wish to abstain from all processes, please notify Elizabeth Lee and Macy Mathews, dataworks@testscience.org, stating your objection to the above statement.