Nathaniel D. Bastian
Instructor of Supply Chain & Information Systems
Dr. Bastian is a decision analytics professional whose expertise lies in the scientific discovery and translation of actionable insights into effective decisions using algorithms, techniques, tools and technologies from operations research, data science, artificial intelligence, systems engineering, and economics to design, develop, deploy and operationalize intelligent decision-support systems and models for descriptive, predictive and prescriptive analytics. He is an author of over 50 journal articles and conference proceedings, several book chapters, and one textbook. Dr. Bastian is the recipient of numerous academic awards and honors to include a Fulbright Scholarship and National Science Foundation Graduate Research Fellowship, as well as multiple research grants. He serves as an Associate Editor for five journals, as well as Referee for over 20 journals. Dr. Bastian is an active member of MORS, INFORMS, ACM, IEEE, SIAM, AAAI and AAAS.
Dr. Bastian currently serves on the Board of Directors of the Military Operations Research Society (MORS), as well as on the Council of the Military and Security Society within the Institute for Operations Research and the Management Sciences (INFORMS). He also serves as a Member of the Information Science and Technology (ISAT) Study Group with the Defense Advanced Research Projects Agency (DARPA). Dr. Bastian also serves as a Senior Research Scientist and Adjunct Assistant Professor within the Intelligent Cyber-Systems and Analytics Research Laboratory at the Army Cyber Institute at the U.S. Military Academy, a Research Affiliate with the Artificial Intelligence Group within the Research and Exploratory Development Department at the Johns Hopkins University Applied Physics Laboratory, a Research Affiliate and Adjunct Assistant Professor of Operations Research at the Air Force Institute of Technology, as well as a Research Affiliate with Penn State University’s Institute for Computational and Data Sciences. Dr. Bastian previously served as a Visiting Research Fellow at the Johns Hopkins University Applied Physics Laboratory, as well as a Distinguished Visiting Professor at the National Security Agency.
- Optimization, simulation, statistical computing, machine/deep learning, intelligent systems, big data analytics
- Decision science, business analytics, applied econometrics, production economics, engineering management
- Computational stochastic optimization and learning for making inferences and decisions under uncertainty
- Game-theoretic network science, graph mining and social network analysis in real-world, complex networks
- Multiple objective optimization and data envelopment analysis for resource allocation decision-making
- Productivity and cost-effectiveness analysis using econometrics for organizational performance improvement
Ph D, Industrial Engineering and Operations Research, Pennsylvania State University, 2016
ME, Industrial Engineering, Pennsylvania State University, 2014
MS, Econometrics and Operations Research, Maastricht University, 2009
BS, Engineering Management (Electrical Engineering) with Honors, U.S. Military Academy, 2008
DAAN 881 – Data Driv Dec Mkng (3)
Application & interpretation of analytics for real-life decision making. DAAN 881 Data-Driven Decision Making (3) The theory and application of several quantitative decision-making tools will be studied. The usefulness of these tools will be illustrated using projects and case studies throughout the course. Emphasis will be placed on the application of the tools and techniques and the results they generate. Finding patterns in data and appropriately grouping them are essential in the extraction of information in large datasets. This course will use Principal Component Analyses to transform highly correlated sets of data by means of orthogonal transformation. Cluster analysis will be used to properly group data when working with large datasets. When the outcomes involve categorical variables, Logistic regression techniques will be used to estimate the probabilistic values of the output. The decision space will be divided into smaller regions using Regression tree analyses. When factors are too numerous and highly collinear, Partial Least Square Regression methods will be performed.Public access datasets in the healthcare, transportation and finance industries will be used to demonstrate the applications and the limitations of these techniques.
BAN 888 – Implemnt Analytics (3)
Sets business analytics in real-world context. Explores project life cycle from business problem framing to model lifecycle management. BAN 888 Implementing Analytics for Business (3) The capstone course for the Business Analytics option in the Data Analytics MPS degree program, this course sets analytics problem solving in a real-world context, including communication to non-statistically trained executives. Key topical areas are derived from the common activities of the business analyst and include business problem framing, analytics problem framing, data sourcing, cleaning and integration, analysis methodology selection, model building, model deployment and model lifecycle management including benefit assessment. Topics align with the body of knowledge in the Institute for Operations Research and the Management Sciences (INFORMS) Certified Analytics Professional Study Guide. Students explore each topic in a real world context, by developing solutions to cases in a team setting. Each team selects a case and works through all elements of the analytics body of knowledge, with group presentations on problem framing, analytics model selection and development, and model lifecycle management in a business setting.