Course Title: Panelist 1

Workshop Theme: Data Management and Reproducible Research

Abstract:

Instructor Bio:

Heather Wojton is Director of Data Strategy and Chief Data Officer at IDA, a role she assumed in 2021. In this position, Heather provides strategic leadership, project management, and direction for the corporation’s data strategy. She is responsible for enhancing IDA’s ability to efficiently and effectively accomplish research and business operations by assessing and evolving data systems, data management infrastructure, and data-related practices.

Heather joined IDA in 2015 as a researcher in the Operational Evaluation Division of IDA’s Systems and Analyses Center. She is an expert in quantitative research methods, including test design and program evaluation. She held numerous research and leadership roles before being named an assistant director in the Operational Evaluation Division.

As a researcher at IDA, Heather led IDA’s test science research program that facilitates data-driven decision-making within the Department of Defense (DOD) by advancing statistical, behavioral, and data science methodologies and applying them to the evaluation of defense acquisition programs. Heather’s other accomplishments include advancing methods for test design, modeling and simulation validation, data management and curation, and artificial intelligence testing. In this role, she worked closely with academic and DOD partners to adapt existing test design and evaluation methods for DoD use and develop novel methods where gaps persist.

Heather has a doctorate in experimental psychology from the University of Toledo and a bachelor’s degree in research psychology from Marietta College, where she was a member of the McDonough International Leadership Program. She is a graduate of the George Washington University National Security Studies Senior Management Program and the Maxwell School National Security Management Course at Syracuse University.

Course Title: Panelist 2

Workshop Theme: Data Management and Reproducible Research

Abstract:

Instructor Bio:

Dr. Laura Freeman is a Research Associate Professor of Statistics and dual hatted as the Director of the Intelligent Systems Lab, Virginia Tech National Security Institute and the Director of the Information Sciences and Analytics Division, Virginia Tech Applied Research Corporation (VT-ARC).   Her research leverages experimental methods for conducting research that brings together cyber-physical systems, data science, artificial intelligence (AI), and machine learning to address critical challenges in national security.  She develops new methods for test and evaluation focusing on emerging system technology.  In her role with VT-ARC she focuses on transitioning emerging research in these areas to solve challenges in Defense and Homeland Security. She is also a hub faculty member in the Commonwealth Cyber Initiative and leads research in AI Assurance. She is the Assistant Dean for Research for the College of Science, in that capacity she works to shape research directions and collaborations in across the College of Science in the Greater Washington D.C. area. Previously, Dr. Freeman was the Assistant Director of the Operational Evaluation Division at the Institute for Defense Analyses.  Dr. Freeman has a B.S. in Aerospace Engineering, a M.S. in Statistics and a Ph.D. in Statistics, all from Virginia Tech.  Her Ph.D. research was on design and analysis of experiments for reliability data.

Course Title: Panelist 3

Abstract:

Instructor Bio:

Dr. Jane Pinelis is the Chief of AI Assurance at the Department of Defense Joint Artificial Intelligence Center (JAIC). She leads a diverse team of testers and analysts in rigorous test and evaluation (T&E) as well as Responsible AI (RAI) implementation for JAIC capabilities, as well as development of AI Assurance products and standards that will support testing of AI-enabled systems across the DoD.

Prior to joining the JAIC, Dr. Pinelis served as the Director of Test and Evaluation for USDI’s Algorithmic Warfare Cross-Functional Team, better known as Project Maven. She directed the developmental testing for the AI models, including computer vision, machine translation, facial recognition and natural language processing. Her team developed metrics at various levels of testing for AI capabilities and provided leadership empirically-based recommendations for model fielding. Additionally, she oversaw operational and human-machine teaming testing, and conducted research and outreach to establish standards in T&E of systems using artificial intelligence.

Dr. Pinelis has spent over 10 years working predominantly in the area of defense and national security. She has largely focused on operational test and evaluation, both in support of the service operational testing commands and also at the OSD level. In her previous job as the Test Science Lead at the Institute of Defense Analyses, she managed an interdisciplinary team of scientists supporting the Director and the Chief Scientist of the Department of Operational Test and Evaluation on integration of statistical test design and analysis and data-driven assessments into test and evaluation practice. Before, that, in her assignment at the Marine Corps Operational Test and Evaluation Activity, Dr. Pinelis led the design and analysis of the widely publicized study on the effects of integrating women into combat roles in the Marine Corps. Based on this experience, she co-authored a book, titled “The Experiment of a Lifetime: Doing Science in the Wild for the United States Marine Corps.”

In addition to T&E, Dr. Pinelis has several years of experience leading analyses for the DoD in the areas of wargaming, precision medicine, warfighter mental health, nuclear non-proliferation, and military recruiting and manpower planning.

Her areas of statistical expertise include design and analysis of experiments, quasi-experiments, and observational studies, causal inference, and propensity score methods.

Dr. Pinelis holds a BS in Statistics, Economics, and Mathematics, an MA in Statistics, and a PhD in Statistics, all from the University of Michigan, Ann Arbor.

Course Title: Panelist 4

Abstract:

Instructor Bio:

Calvin Robinson is a Data Architect within the Information and Applications Division at NASA Glenn Research Center. He has over 10 years of experience supporting data analysis and simulation development for research, and currently supports several key data management efforts to make data more discoverable and aligned with FAIR principles. Calvin oversees the Center’s Information Management Program and supports individuals leading strategic AIML efforts within the Agency. Calvin holds a BS in Computer Science and Engineering from the University of Toledo.

Course Title: Using R Markdown & the Tidyverse to Create Reproducible Research

Abstract: R is one of the major platforms for doing statistical analysis and research.  This course introduces the powerful and popular R software through the use of the RStudio IDE.  This course covers the use of the tidyverse suite of packages to import raw data (readr), do common data manipulations (dplyr and tidyr), and summarize data numerically (dplyr) and graphically (ggplot2).  In order to promote reproducibility of analyses, we will discuss how to code using R Markdown – a method of R coding that allows one to easily create PDF and HTML documents that interweave narrative, R code, and results.
List of packages to install: tidyverse, GGally, Lahman, tinytex

Instructor Bio: Justin Post is a Teaching Associate Professor and the Director of Online Education in the Department of Statistics at North Carolina State University. Teaching has always been his passion and that is his main role at NCSU. He teaches undergraduate and graduate courses in both face-to-face and distance settings.  Justin is an R enthusiast and has taught many short courses on R, the tidyverse, R shiny, and more.

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Course Title: Categorical Data Analysis

Workshop Theme: Analysis Tools and Techniques

Abstract:

Categorical data is abundant in the 21st century, and its analysis is vital to advance research across many domains. Thus, data-analytic techniques that are tailored for categorical data are an essential part of the practitioner’s toolset. The purpose of this short course is to help attendees develop and sharpen their abilities with these tools. Topics covered in this short course will include binary and multi-category logistic regression, ordinal regression, and classification, and methods to assess predictive accuracy of these approaches will be discussed. Data will be analyzed using the R software package, and course content loosely follow Alan Agresti’s excellent textbook “An Introduction to Categorical Data Analysis, Third Edition.”

Instructor Bio: Chris Franck is an Assistant Professor in the Department of Statistics at Virginia Tech.

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Course Title: A Practitioner’s Guide to Advanced Topics in DOE

Workshop Theme: Design of Experiments

Abstract: Having completed a first course in DOE and begun to apply these concepts, engineers and scientists quickly learn that test and evaluation often demands knowledge beyond the use of classical designs.  This one-day short course, taught by an engineer from a practitioner’s perspective, targets this problem.  Three broad areas are covered:

• Optimal designs address questions such as how to accommodate constraints in the design space, specify unique models, and fractionate general factorial designs. Choices for methods revolve around objectives. For example, is the priority to best estimate model parameters or to reduce overall response prediction error?
• Split plot designs allow restrictions to randomization for hard-to-change factors. These designs, including optimal adaptations, are now supported by most commercial experimental design software and within the reach of the practitioner.
• Sequential design approaches strive to minimize resource requirements at the onset of a test. A phased approach includes a plan to add runs to achieve specific objectives following an initial test analysis.  The approach is especially effective in high-dimensional spaces where the influence of all factors is in question and/or model order is unknown.  One example is the use of screening designs where all factors are included in the first test with the expectation that many will be found insignificant.

The course format is to introduce relevant background material, discuss case studies, and provide software demonstrations.   Case studies and demonstrations are derived from a variety of sources, including aerospace testing and DOD T&E. Learn design approaches, design comparison metrics, best practices, and lessons learned from the instructor’s experience.   A first course in Design of Experiments is a prerequisite.

Instructor Bio: Drew Landman has 34 years of experience in engineering education as a professor at Old Dominion University.  Dr. Landman’s career highlights include13 years (1996-2009) as chief engineer at the NASA Langley Full-Scale Wind Tunnel in Hampton, VA.  Landman was responsible for program management, test design, instrument design and calibration and served as the lead project engineer for many automotive, heavy truck, aircraft, and unmanned aerial vehicle wind tunnel tests including the Centennial Wright Flyer and the Boeing X-48B and C.  His research interests and sponsored programs are focused on wind tunnel force measurement systems and statistically defensible experiment design primarily to support wind tunnel testing. Dr. Landman has served as a consultant and trainer in the area of statistical engineering to test and evaluation engineers and scientists at AIAA, NASA, Aerovironment, Airbus, Aerion, ATI, USAF, US Navy, US Marines and the Institute for Defense Analysis.  Landman founded a graduate course sequence in statistical engineering within the ODU Department of Mechanical and Aerospace Engineering.  He is currently co-authoring a text on wind tunnel test techniques.

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Course Title: Operational Cyber Resilience in Engineering and Systems Test

Workshop Theme: Test and Evaluation Methods for Emerging Technology

Abstract:

Cyber resilience is the ability to anticipate, withstand, recover from, and adapt to adverse conditions, stresses, attacks, or compromises on systems that use or are enabled by cyber resources. As a property defined in terms of system behavior, cyber resilience presents special challenges from a test and evaluation perspective. Typically, system requirements are specified in terms of technology function and can be tested through manipulation of the systems operational environment, controls, or inputs. Resilience, however, is a high-level property relating to the capacity of the system to recover from unwanted loss of function. There are no commonly accepted definitions of how to measure this system property. Moreover, by design, resilience behaviors are exhibited only when the system has lost critical functions. The implication is that the test and evaluation of requirements for operational resilience will involve creating, emulating, or reasoning about the internal systems states that might result from successful attacks.

This tutorial will introduce the Framework for Operational Resilience in Engineering and System Test (FOREST), a framework that supports the derivation of measures and metrics for developmental and operational test plans and activities for cyber resilience in cyber-physical systems. FOREST aims to provide insights to support the development of testable requirements for cyber resilience and the design of systems with immunity to new vulnerabilities and threat tactics. FOREST’s elements range from attack sensing to the existence and characterization of resilience modes of operation to operator decisions and forensic evaluation. The framework is meant to be a reusable, repeatable, and practical framework that calls for system designers to describe a system’s operational resilience design in a designated, partitioned manner that aligns with resilience requirements and directly relates to the development of associated test concepts and performance metrics.

The tutorial introduces model-based systems engineering (MBSE) tools and associated engineering methods that complement FOREST and support the architecting, design, or engineering aspects of cyber resilience. Specifically, it features Mission Aware, a MBSE meta-model and associated requirements and architecture analysis process targeted to decomposition of loss scenarios into testable resilience features in a system design. FOREST, Mission Aware, and associated methodologies and digital engineering tools will be applied to two case studies for cyber resilience: (1) Silverfish, a hypothetical networked munition system and (2) an oil distribution pipeline. The case studies will lead to derivations of requirements for cyber resilience and survivability, along with associated measures and metrics.

Instructor Bio:

Peter A. Beling is a professor in the Grado Department of Industrial and Systems Engineering and associate director of the Intelligent Systems Division in the Virginia Tech National Security Institute. Dr. Beling’s research interests lie at the intersections of systems engineering and artificial intelligence (AI) and include AI adoption, reinforcement learning, transfer learning, and digital engineering. He has contributed extensively to the development of methodologies and tools in support of cyber resilience in military systems. He serves on the Research Council of the Systems Engineering Research Center (SERC), a University Affiliated Research Center for the Department of Defense.

Tom McDermott is the Deputy Director and Chief Technology Officer of the Systems Engineering Research Center at Stevens Institute of Technology in Hoboken, NJ. He leads research on Digital Engineering transformation, education, security, and artificial intelligence applications. Mr. McDermott also teaches system architecture concepts, systems thinking and decision making, and engineering leadership for universities, government, and industry. He serves on the INCOSE Board of Directors as Director of Strategic Integration.

Tim Sherburne is a research associate in the Intelligent System Division of the Virginia Tech National Security Institute.  Sherburne was previously a member of the systems engineering staff at the University of Virginia supporting Mission Aware research through rapid prototyping of cyber resilient solutions and model-based systems engineering (MBSE) specifications. Prior to joining the University of Virginia, he worked at Motorola Solutions in various Software Development and Systems Engineering roles defining and building mission critical public safety communications systems.

Materials:
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