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BIOGRAPHY

MAJ Nathaniel D. Bastian, Ph.D.


Senior Research Scientist | Adjunct Assistant Professor

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 Nathaniel D. Bastian, PhD is an FA49 (Operations Research and Systems Analysis) Officer in the United States Army. He currently serves as the Chief Artificial Intelligence Solution Architect at the Department of Defense (DoD) Joint Artificial Intelligence Center (JAIC). In this role, MAJ Bastian provides strategic direction and technical leadership for artificial intelligence (AI) activities in direct support of the Office of the DoD Chief Information Officer digital modernization efforts, as well as the technical advisement, expertise and oversight of data science and AI engineering functions across the JAIC Directorates, leading to the operationalization of AI-enabled products, capabilities, policies, plans and programs in support of the Military Departments, Joint Staff, Combatant Commands, Office of the Secretary of Defense, and other DoD Components to solve novel, complex problems that span the DoD enterprise and accelerate the adoption of AI to achieve mission impact at scale. MAJ Bastian also serves as Senior Research Scientist and Adjunct Assistant Professor within the Intelligent Cyber-Systems and Analytics Research Laboratory (ICSARL) at the Army Cyber Institute (ACI) at the U.S. Military Academy (USMA), where he conducts research in the areas of autonomous cyber decision-support systems and AI system assurance (security, safety, fairness, privacy, robustness, reliability, and resilience). MAJ Bastian will return to USMA as an FA47 (Academy Professor) Officer at the ACI.

MAJ 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. MAJ 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. MAJ Bastian is an active member of MORS, INFORMS, ACM, IEEE, SIAM, AAAI and AAAS.

MAJ 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). MAJ Bastian also serves as 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. He also serves as an Adjunct Professor teaching online graduate courses at several universities. MAJ 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.

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
  • Computational stochastic optimization and learning for making inferences and decisions under uncertainty

  • Game-theoretic network science, graph mining and social network analysis for 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
Selected Publications

Bastian, N. (2020). Building the Army’s Artificial Intelligence Workforce. The Cyber Defense Review, 5(2): XX-XX.

Wilkinson, C., Bastian, N. & Kwon, M. (2020). Beyond Traditional Architecture for MDO Applications: the Erlang VM and its Potential. Proceedings of the 2020 SPIE Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II (Ed. Pham et al.), pp. XX-XX. SPIE Defense and Commercial Sensing (Volume: DL 11413).

Bastian, N., Lunday, B., Fisher, C. & Hall, A. (2020). Models and Methods for Workforce Planning Under Uncertainty: Optimizing U.S. Army Cyber Branch Readiness and Manning. Omega, 92(102171): 1-13.

Bastian, N. & Hall, A. (2020). Military Workforce Planning and Manpower Modeling. In Natalie Scala & James Howard (Ed.), Handbook of Military and Defense Operations Research (pp. 83-112). Boca Raton, FL: CRC Press.

Robbins, M., Jenkins, P., Bastian, N. & Lunday, B. (2020). Approximate Dynamic Programming for the Aeromedical Evacuation Dispatching Problem: Value Function Approximation using Multiple Level Aggregation, Omega, 91(102020): 1-17.

Kiely, T. & Bastian, N. (2020). The Spatially Conscious Machine Learning Model. Statistical Analysis and Data Mining: The ASA Data Science Journal, 13(1): 31-49.

Maxwell, P., Alhajjar, E. & Bastian, N. (2019). Intelligent Feature Engineering for Cybersecurity. Proceedings of the 2019 IEEE International Conference on Big Data, pp. 5005-5011. IEEE.

Bastian, N., Fisher, C., Hall, A. & Lunday, B. (2019). Solving the Army’s Cyber Workforce Planning Problem using Stochastic Optimization and Discrete-Event Simulation Modeling. Proceedings of the 2019 Winter Simulation Conference (Ed. Mustafee et al.), pp. 738-749. IEEE.

Shetty, S., Ray, I., Celik, N., Mesham, M., Bastian, N. & Zhu, Q. (2019). Simulation for Cyber Risk Management – Where are we, and where do we want to go. Proceedings of the 2019 Winter Simulation Conference (Ed. Mustafee et al.), pp. 726-737. IEEE.

Bastian, N. (2019). Information Warfare and its 18th and 19th Century Roots. The Cyber Defense Review, 4(3): 31-37.

Fulton, L. & Bastian, N. (2019). Multi-Period Stochastic Programming Portfolio Optimization for Diversified Funds. International Journal of Finance and Economics, 24(1): 313-327.

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.