Course Title: Gaussian Process Modeling, Design and Optimization
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 supernova via radiative shock hydrodynamics; modeling the evolution a spreading epidemic. The course material will emphasize deriving and implementing methods over proving theoretical properties.
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.
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Course Title: Software Testing
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, techniques 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, as well as testing for software vulnerabilities. Existing tools for test automation and test suite construction will be presented.
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.
Course Title: Introduction to Neural Networks for Deep Learning with Tensorflow
This short-course discusses the practical application of neural networks from a lay person's perspective. We will dive into hands-on case studies in which we will build, train, and analyze neural network models. The course will start with foundational machine learning concepts, move onto the basics of neural networks, and then explore more complex neural network variants and architectures for deep learning applications. Deep learning techniques are becoming more prevalent throughout the development of autonomous and AI-enabled systems, and this course will provide students with the foundational intuition needed to understand these systems.
Topics that will be covered include: introduction to machine learning using Python, neural networks, convolutional neural networks, tensorflow
*Pre-requisites: This course is designed for a mixed audience with varying levels of analytics experience. The format is offers attendees both the opportunity to actively participate in hands-on Python coding exercises, but also affords non-programmers a thorough introduction to the topic area. Knowledge of graphical and statistical methods for dealing with multidimensional datasets is helpful but not necessary.
Roshan Patel is a systems engineer and data scientist working at CCDC Armament Center. His role focuses on systems engineering infrastructure, statistical modeling, and the analysis of weapon systems. He holds a Masters of Computer Science from Rutgers University, where he specialized in operating systems programming and machine learning. At Rutgers, Mr. Patel was a part-time lecturer for systems programming and data science seminars. Mr. Patel is the current AI lead for the Systems Engineering Directorate at CCDC Armaments Center.