Home : Research : Research Team : Bastian
BIOGRAPHY

MAJ Nathaniel D. Bastian, Ph.D.


Operations Research Scientist || Assistant Professor, Army Cyber Institute

EDUCATION
Ph.D. Industrial Engineering and Operations Research, Pennsylvania State University, 2016
M.Eng. Industrial Engineering, Pennsylvania State University, 2014
M.S. Econometrics and Operations Research, Maastricht University, 2009
B.S. Engineering Management (Electrical Engineering) with Honors, U.S. Military Academy, 2008
 

MAJ Nathan Bastian, Ph.D. is an FA49 (Operations Research / Systems Analysis) Officer in the U.S. Army. As an Operations Research Scientist within the Futures Team of the Cyber Research Division at the Army Cyber Institute, Nathan is the Founder and Lead Research Scientist of ACI’s Intelligent Cyber-Systems and Analytics Research Laboratory (ICSARL).

Nathan also serves as an Assistant Professor of Operations Research and Data Science at the U.S. Military Academy, where he teaches and advises cadets within the Department of Systems Engineering, Department of Electrical Engineering and Computer Science, and Department of Mathematical Sciences. He also conducts basic and interdisciplinary applied research with faculty colleagues across the Academy, to include within the: Department of Systems Engineering (Operations Research Center), Department of Electrical Engineering and Computer Science (Cyber Research Center), Department of Mathematical Sciences (Network Science Center; Center for Data Analysis and Statistics), and Department of Social Sciences (Office of Economic and Manpower Analysis).

Nathan 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.

Nathan’s 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, industrial engineering, and economics to design, develop, deploy and operationalize decision-support models for descriptive, predictive and prescriptive analytics. He particularly enjoys research efforts that involve decision-making problems under uncertainty.

Expertise
  • Optimization, simulation, statistical computing, machine/deep learning, intelligent systems, big data analytics

  • Decision science, business analytics, applied econometrics, production economics, engineering management



Research Areas
  • Multiple objective optimization, stochastic programming, and approximate dynamic programming

  • Monte Carlo methods, deep reinforcement learning, pattern recognition, and artificial intelligence at scale

  • Productivity analysis, efficiency measurement, cost-effectiveness analysis, and econometric modeling

  • Network science, graph mining and social network analysis in real-world, complex networks



Selected Publications

Robbins, M., Jenkins, P., Bastian, N. & Lunday, B. (2018). Approximate Dynamic Programming for the Aeromedical Evacuation Dispatching Problem: Value Function Approximation using Multiple Level Aggregation, Omega, Article in Press, 1-17.

Fulton, L. & Bastian, N.. (2018). Multi-Period Stochastic Programming Portfolio Optimization for Diversified Funds. International Journal of Finance and Economics, Online First, 1-15.

Bastian, N.., Swenson, E., Ma, L., Suk Na, H. & Griffin, P. (2017). Incentive Contract Design for Food Retailers to Reduce Food Deserts in the US. Socio-Economic Planning Sciences, 60: 87-98.

Paradarami, T., Bastian, N.. & Wightman, J. (2017). A Hybrid Recommender System Using Artificial Neural Networks. Expert Systems with Applications, 83: 300-313.

Bastian, N., Ekin, T., Kang, H., Griffin, P., Fulton, L. & Grannan, B. (2017). Stochastic Multi-Objective Auto-Optimization for Resource Allocation Decision-Making in Fixed-Input Health Systems. Health Care Management Science, 20(2): 246-264.

Ekin, T., Kocadagli, O., Bastian, N., Fulton, L. & Griffin, P. (2016). Fuzzy Decision-Making in Health Systems: A Resource Allocation Model. EURO Journal on Decision Processes, 4(3): 245-267.

Bastian, N. & Griffin, P. (2016). Multi-Criteria Network Design in Health and Humanitarian Logistics. In A. Ravi Ravindran (Ed.), Multiple Criteria Decision Making in Supply Chain Management (pp. 161 – 189). Boca Raton, FL: CRC Press.

Swenson, E., Bastian, N. & Nembhard, H. (2016). Data Analytics in Health Promotion: Health Market Segmentation and Classification of Total Joint Replacement Surgery Patients. Expert Systems with Applications, 60: 118-129.

Bastian, N., Griffin, P., Spero, E. & Fulton, L. (2016). Multi-Criteria Logistics Modeling for Military Humanitarian Assistance and Disaster Relief Aerial Delivery Operations. Optimization Letters, 10(5): 921-953.

Griffin, P., Nembhard, H., DeFlitch, C., Bastian, N., Kang, H. & Munoz, D. (2016). Healthcare Systems Engineering. Hoboken, NJ: John Wiley & Sons, Inc.

Bastian, N., McMurry, P., Fulton, L., Griffin, P., Cui, S., Hanson, T., & Srinivas, S. (2015). The AMEDD Uses Goal Programming to Optimize Workforce Planning Decisions. INFORMS Journal of Applied Analytics (formerly Interfaces), 45(4): 305-324.

Grannan, B., Bastian, N. & McLay, L. (2015). A Maximum Expected Covering Problem for Locating and Dispatching Two Classes of Military Medical Evacuation Air Assets. Optimization Letters, 9(8): 1511-1531.


For a full list of publications, please refer to Nathan’s Google Scholar page.