Nathaniel D. Bastian
Adjunct Professor, Data Analytics / Business Analytics
Nathaniel D. Bastian, Ph.D. is a leader, practitioner, researcher, and educator of mathematical, computational, analytical, data-driven, and decision-centric methods to support the improvement and enhancement of decision-making in cyber security, national defense, military operations, human resources and manpower, healthcare, logistics, energy and finance. He is a decision analytics professional with expertise 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 research, design, develop, and deploy intelligent decision-support models, tools and systems for descriptive, predictive and prescriptive analytics. He has authored over 60 refereed journal and conference papers, several book chapters, and one textbook. He is the recipient of numerous academic awards, honors and grants, to include a Fulbright Scholarship and National Science Foundation Graduate Research Fellowship. He serves as an Associate Editor for four journals, as well as Referee for over 20 journals. He is an active member of MORS, INFORMS, ACM, IEEE, SIAM, and AAAI.
- Computational stochastic optimization and robust learning for making inferences and decisions under uncertainty
- Multiple objective optimization and federated machine learning for distributed resource allocation decision-making
- Optimization, simulation, statistical computing, machine/deep learning, intelligent systems, big data analytics
- Decision science, business analytics, applied econometrics, production economics, engineering management
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
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.
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.