Workshop Theme: Progress and Challenges in Program Evaluation
Course 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 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.  His research focuses in Bayesian model selection and averaging, objective Bayes, and spatial statistics.  Much of his work has a specific emphasis in health applications

Workshop Theme: Progress and Challenges in Program Evaluation
Course Abstract: This course details statistical techniques at the interface between geostatistics, machine learning, mathematical modeling via computer simulation, calibration of computer models to data from field experiments, and model-based sequential design and optimization under uncertainty (a.k.a. Bayesian Optimization). The treatment will include some of the historical methodology in the literature, and canonical examples, but will primarily concentrate on modern statistical methods, computation and implementation, as well as modern application/data type and size. The course will return at several junctures to real-word experiments coming from the physical, biological and engineering sciences, such as studying the aeronautical dynamics of a rocket booster re-entering the atmosphere; modeling the drag on satellites in orbit; designing a hydrological remediation scheme for water sources threatened by underground contaminants; studying the formation of super-nova via radiative shock hydrodynamics; modeling the evolution a spreading epidemic. The course material will emphasize deriving and implementing methods over proving theoretical properties. The presentation will be in Rmarkdown, which means that all slides and analyses will be reproducible, and be accompanied by a full scale, and fully open source textbook, also authored in Rmarkdown.
Instructor Bio: Robert Gramacy is a Professor of Statistics in the College of Science at Virginia Polytechnic and State University (Virginia Tech). Previously he was an Associate Professor of Econometrics and Statistics at the Booth School of Business, and a fellow of the Computation Institute at The University of Chicago. His research interests include Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, sequential design, and optimization under uncertainty.  Professor Gramacy is a computational statistician. He specializes in areas of real-data analysis where the ideal modeling apparatus is impractical, or where the current solutions are inefficient and thus skimp on fidelity. Such endeavors often require new models, new methods, and new algorithms. His goal is to be impactful in all three areas while remaining grounded in the needs of a motivating application. His aim is to release general purpose software for consumption by the scientific community at large, not only other statisticians.  Professor Gramacy is the primary author on six R packages available on CRAN, two of which (tgp, and monomvn) have won awards from statistical and practitioner communities.

Workshop Theme: Modeling the Human-System Interaction
Course Abstract: Understanding the connections and dependencies that exist in our data is becoming ever more important. This one-day course on network analysis will introduce many of the basic concepts of networks, including descriptive statistics (e.g., centrality, prestige, etc.), community detection, and an introduction to nonparametric inferential tests. Additionally, cutting-edge methods for creating so-called “psychometric networks” that focus on the connections between variables will be covered. Throughout, we will discuss visualization methods that can highlight the nature of connections between entities in the network, whether they are observations, variables, or both.
Instructor Bio: Doug Steinley is a Professor in the Psychological Sciences Department at the University of Missouri. His research focuses on multivariate statistical methodology, with a primary interest in cluster analysis and social network analysis. His research in cluster analysis focuses on both traditional cluster analytic procedure (e.g., k-means cluster analysis) and more modern techniques (e.g., mixture modeling). In that the formulation of the general partitioning problem can be thought of in a graph theoretic nature, his research also involves combinatorics and social network analysis.

Workshop Theme: Machine Learning and Artificial Intelligence
Course Abstract: This short course discusses the applications of machine learning from a lay person’s perspective and presents the landscape of approaches and their utility. We then dive into a technical hands-on workshop implementing classification algorithms to build predictive models, tune them, and interpret their results. Applications include forecasting behaviors and events. Topics that will be covered include: introduction to machine learning & its applications, introduction to classification and supervised machine learning, classification algorithms, and classification performance metrics.
*Pre-requisites: Attendees must be comfortable using R to manipulate data and must know how to create basic visualizations with ggplot2.
Instructor Bio: Coming Soon

Workshop Theme:
Course Abstract: Systematically testing software for errors and demonstrating that it meets the system specification is a necessary component of assuring trustworthiness of information systems. Software testing is often costly and time consuming when conducted correctly, but the consequence of poor quality testing is even higher, especially for critical systems. This short course will provide an introduction to software testing including the process of testing within the software development lifecycle, as well as techniques and considerations in choosing test cases for constructing comprehensive test suites to achieve coverage of the code and/or input space as relevant to the system under test. Existing tools for test automation and test suite construction will be presented.
Instructor Bio: Erin Lanus is a Research Assistant Professor at the Hume Center for National Security and Technology at Virginia Tech. She has a Ph.D. in Computer Science with a concentration in cybersecurity  from Arizona State University. Her experience includes work as a Research Fellow at University of Maryland Baltimore County and as a High Confidence Software and Systems Researcher with the Department of Defense. Her current interests are software and combinatorial testing, machine learning in cybersecurity, and artificial intelligence assurance.