Nathaniel Bastian

AI Foundations

• Stochastic and robust optimization algorithms
• Federated machine learning and inference
• Distributed decision-making under uncertainty
• Multimodal data processing and fusion
• Generative methods for synthetic data generation
• Meta-learning and ensemble methods

AI Applications

• Cybersecurity, Military, National Security, Internet of Things, Healthcare, Finance

Externally Funded AI Projects

• Generative Methods for Cyber (NSA)
• Evaluating Model Robustness to Machine Learning Data Contamination Attacks (Army C5ISR)
• Assessment of Intelligent and Assured Autonomous Cyber Decision-Support Systems (Army C5ISR)
• Enabling the Safe and Responsible Use of Reinforcement Learning (ARL)
• Principles of Robust Learning and Inference (ARL)
• Optimization for Learning, Inferencing, and Decision Making (ARL)

AI-Related Courses

• BAN 888 Implementing Analytics for Business
• DAAN 881 Data-Driven Decision Making

Webpage

directory.smeal.psu.edu/ndb141

Publications

1. Cobb, A., Jalaian, B., Bastian, N. & Russell, S. (2021). Robust Decision-Making in the Internet of Battlefield Things Using Bayesian Neural Networks. Proceedings of the 2021 Winter Simulation Conference (Ed. Kim et al.). IEEE. To appear.
2. Chalé, M. & Bastian, N. (2021). Challenges and Opportunities for Generative Methods in the Cyber Domain. Proceedings of the 2021 Winter Simulation Conference (Ed. Kim et al.). IEEE. To appear.
3. Goethals, P., Scala, N. & Bastian, N. (2021). Operations Research. In Daniel Bennett, Paul Goethals and Natalie Scala (Ed.), Mathematics in Cyber Research. Boca Raton, FL: CRC Press. To appear.
4. Cobb, A., Jalaian, B., Bastian, N. & Russell, S. (2021). Towards Safe Decision-Making via Uncertainty Quantification in Machine Learning. In William Lawless, Ranjeev Mittu, Donald Sofge, Thomas Shortell and Tom McDermott (Ed.), Systems Engineering and Artificial Intelligence. Springer. To appear.
5. Bastian, N. (2021). Artificial Intelligence for Defense Applications. Journal of Defense Modeling and Simulation, 18(3): 173-174.
6. De Lucia, M., Maxwell, P., Bastian, N., Swami, A., Jalaian, B. & Leslie, N. (2021). Machine Learning for Raw Network Traffic Detection. Proceedings of the 2021 SPIE Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III (Ed. Pham et al., 117460V), pp. 117460V-1 – 117460V-10, SPIE Defense + Commercial Sensing (Volume: 11746).
7. Painter, C. & Bastian, N. (2021). Generating Genetic Engineering Linked Indicator Datasets for Machine Learning Classifier Training in Biosecurity. Proceedings of the 2021 SPIE Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III (Ed. Pham et al., 1174624), pp. 1174624-1 – 1174624-6, SPIE Defense + Commercial Sensing (Volume: 11746).
8. Courtoy, J. & Bastian, N. (2021). Three Things Leaders Need to Know Before Investing in Artificial Intelligence. Phalanx, 54(1): 32-37.
9. Devine, S. & Bastian, N. (2021). An Adversarial Training Based Machine Learning Approach to Malware Classification under Adversarial Conditions. Proceedings of the 54th Hawaii International Conference on System Sciences, pp. 827-836. ScholarSpace.
10. Kerwin, K. & Bastian, N. (2021). Stacked Generalizations in Imbalanced Fraud Data Sets using Resampling Methods. Journal of Defense Modeling and Simulation, 18(3): 175-192.
11. Shipp, T., Clouse, D., De Lucia, M., Ahiskali, M., Steverson, K., Mullin, J. & Bastian, N. (2020). Advancing the Research and Development of Assured Artificial Intelligence and Machine Learning Capabilities. Proceedings of the AAAI Fall 2020 Symposium on AI in Government and Public Sector. arXiv:2009.13250.
12. Bastian, N. (2020). Building the Army’s Artificial Intelligence Workforce. The Cyber Defense Review, 5(2): 59-63.
13. Chalé, M., Bastian, N. & Weir, J. (2020). Algorithm Selection Framework for Cyber Attack Detection. Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning, pp. 37-42. ACM Digital Library.