Jefferson Huang

Assistant Professor

Operations Research Department

Naval Postgraduate School

Office: 245 Glasgow Hall

Email: firstname dot lastname at nps dot edu

News

• February 2024: Taught short course on machine learning for NPS Hawaii.
• May 2023: Visited DLA Distribution Norfolk, Virginia (DDNV) to brief DLA R&D and Atlantic Fleet Type Commanders on my workload forecasting project for DDNV's Material Processing Center. DLA News

Research Interests:

• Markov Decision Processes
• Decision-Making Under Uncertainty
• Defense Logistics

Publications:

1. J. Huang, D. G. Down, M. E. Lewis and C. Wu. Dynamically scheduling and maintaining a flexible server. Naval Research Logistics, 69(2):223-240, 2022. link
2. E. A. Feinberg and J. Huang. On the reduction of total-cost and average-cost MDPs to discounted MDPs. Naval Research Logistics, 66(1):38-56, 2019. link
3. E. A. Feinberg and J. Huang. Reduction of total-cost and average-cost MDPs with weakly continuous transition probabilities to discounted MDPs. Operations Research Letters, 46(2):179-184, 2018. link
4. E. A. Feinberg and J. Huang. Strongly polynomial algorithms for transient and average-cost MDPs. ACM SIGMETRICS Performance Evaluation Review, 45(2):6-8, 2017. link
5. E. A. Feinberg, J. Huang and B. Scherrer. Modified policy iteration algorithms are not strongly polynomial for discounted dynamic programming. Operations Research Letters, 42(6-7):429-431, 2014. link
6. E. A. Feinberg and J. Huang. The value iteration algorithm is not strongly polynomial for discounted dynamic programming. Operations Research Letters, 42(2):130-131, 2014. link
7. E. A. Feinberg and J. Huang. Strong polynomiality of policy iterations for average-cost MDPs modeling replacement and maintenance problems. Operations Research Letters, 41(3):249-251, 2013. link

Conference Proceedings:

1. J. Huang, D. G. Down, M. E. Lewis and C. Wu. Dynamic scheduling and maintenance for a two-class queue with a deteriorating server. Proceedings of the 2018 American Control Conference, Milwaukee, WI, USA, June 27-29, 2018. link
2. R. Zhang, J. Huang and T. Kumar. Preventive leak detection for high pressure gas transmission networks. AAAI-17 Workshop on AI and OR for Social Good, 2017. pdf

Technical Reports:

1. J. Huang and R. Yoshida. Deception and risk-aware dynamic routing. Technical Report, Operations Research Department, NPS-OR-23-004, 2023. link
2. E. Morman and J. Huang. An approximate dynamic programming approach for weapon system financial execution management. Acquisition Research Program Sponsored Report Series, NPS-FM-20-008, 2019. link

Preprints

1. K. Pasque, C. Teska, R. Yoshida, K. Miura, and J. Huang. Tropical decision boundaries for neural networks are robust against adversarial attacks, 2024. link

Talks:

1. Fleet-Informed Workload Forecasting for the DLA Distribution Norfolk, Virginia Material Processing Center. MORS Emerging Techniques Forum, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, December 6, 2023. slides
2. Online Discrete Convex Optimization. INFORMS Annual Meeting, Phoenix, AZ, 16 October, 2023. slides
3. Optimizing Supply Blocks for Expeditionary Units.
• The XVI International Conference on Stochastic Programming, University of California, Davis, CA, July 28, 2023. slides
• 91st Military Operations Research Society Symposium, United States Military Academy, West Point, NY, June 14, 2023. slides
• 90th Military Operations Research Society Symposium, Marine Corps University, Quantico, VA, June 14, 2022. slides
4. Game-Theoretic Methods for Rapid Operational Airlift Network Design in Contested Environments. 91st Military Operations Research Society Symposium, United States Military Academy, West Point, NY, June 14, 2023. slides
5. Maximally informative underwater sensor placement.
• Distinguished Lecture Series, Naval Surface Warfare Center, Crane Division, Crane, IN, 20 October, 2022. slides
• INFORMS Annual Meeting, Indianapolis, IN, 18 October, 2022. slides
• Machine Learning on Monterey Bay, UC Santa Cruz, Santa Cruz, CA, November 10, 2021. slides
6. Markov decision processes and reinforcement learning: A very short (defense-oriented) introduction. NPS Junior Faculty Research Seminar, Naval Postgraduate School, Monterey, CA, May 25, 2022. slides
7. Bandit algorithms for data-driven resolution/field-of-view tradeoffs in multi-mode sensing and intelligence collection.
• European Conference on Operational Research, University of West Attica, Athens, Greece, July 14, 2021. slides
• Military Operations Research Society Emerging Techniques Forum, December 7, 2020. (MORS Eugene P. Visco Prize for International Collaboration in Operations Research Runner-Up) slides
8. Bandit models of cyber intrusion. INFORMS Annual Meeting, Seattle, WA, October 20, 2019. slides
9. Dynamically scheduling and maintaining a flexible server. INFORMS Annual Meeting, Phoenix, AZ, November 7, 2018. slides
10. Dynamic scheduling and maintenance for a two-class queue with a deteriorating server. American Control Conference, Milwaukee, WI, June 28, 2018. slides
11. Dynamic scheduling and maintenance of a deteriorating server.
• INFORMS International Conference, Taipei, Taiwan, June 19, 2018. slides
• CORS Annual Conference, Halifax, NS, Canada, June 4, 2018. slides
• AMS Spring Eastern Sectional Meeting, Northeastern University, Boston, MA, April 21, 2018. slides
• Seminar on Combinatorics, Games and Optimisation, London School of Economics and Political Science, London, UK, March 28, 2018. slides
12. Near-optimal control of queueing systems via approximate one-step policy improvement. Reinforcement Learning for Processing Networks Seminar, Cornell University, Ithaca, NY, March 21, 2018. slides
13. Solving Markov decision processes. Operations Research Seminar, Naval Postgraduate School, Monterey, CA, February 8, 2018. slides
14. Optimality of a priority policy for a server scheduling problem with a deteriorating server. INFORMS Annual Meeting, Houston, TX, October 22, 2017. slides
15. On the reduction of total cost and average cost MDPs to discounted MDPs. INFORMS Applied Probability Society Conference, Northwestern University, Evanston, IL, July 12, 2017. slides
16. Strongly polynomial algorithms for transient and average-cost MDPs. Workshop on MAthematical performance Modeling and Analysis (MAMA), University of Illinois at Urbana-Champaign, Urbana, IL, June 5, 2017. slides
17. Reducing undiscounted Markov decision processes and stochastic games with unbounded costs to discounted ones. Northeast Regional Conference on Optimization and Optimal Control under Uncertainty, IBM T. J. Watson Research Center, Yorktown Heights, NY, December 8, 2016. slides
18. Reductions of undiscounted Markov decision processes and stochastic games to discounted ones. INFORMS Annual Meeting, Nashville, TN, November 16, 2016. slides
19. Computational complexity estimates for value and policy iteration algorithms for total-cost and average-cost Markov decision processes. AI Seminar, University of Alberta, Edmonton, AB, Canada, May 5, 2016. slides
20. Recovering bandits. SBU Algorithms Reading Group, Stony Brook, NY, February 26, 2016. slides
21. Markov decision processes and complexity theory: some research directions. SBU AMS Graduate Reading Group, Stony Brook, NY, February 10, 2016. slides
22. Computational complexity estimates for policy and value iteration algorithms for total-cost and average-cost Markov decision processes. INFORMS Annual Meeting, Philadelphia, PA, November 2, 2015. slides
23. Nonlinear methods for temporal causal discovery. Industries & Solutions Summer Intern Seminar Series, IBM T. J. Watson Research Center, Yorktown Heights, NY, August 13, 2015.
24. Computational complexity estimates for value and policy iteration algorithms for total-cost and average-cost Markov decision processes. AP for Lunch Seminar, IBM T. J. Watson Research Center, Yorktown Heights, NY, July 29, 2015. slides
25. Computational complexity estimates for policy and value iteration algorithms for total-cost and average-cost Markov decision processes. The Fifth International Workshop in Sequential Methodologies, New York, NY, June 23, 2015. slides
26. Reduction of average-cost Markov Decision Processes to discounting under an accessibility condition. INFORMS Annual Meeting, San Francisco, CA, November 10, 2014. slides
27. Recent progress on the complexity of solving Markov Decision Processes. Prelim talk, January 24, 2014. slides writeup

Current Advisees:

1. C. M. Bromley. Lt Col, USMC. Application of Reinforcement Learning to Air-to-Air Fighter Tactics. M. S. in Operations Research, Anticipated June 2024. (Co-Advising with R. L. Bassett)
2. R. Patel. ENS, USN. Platform Selection and Evaluation: Creating an Effective Littoral Denial System. M. S. in Operations Research, Anticipated June 2024.
3. B. W. Davenport. LCDR, SC, USN. Budget Forecasting Methods for Navy Expeditionary Readiness. M. S. in Operations Research, Anticipated September 2024. (Co-Advising with D. J. MacKinnon)
4. K. R. Pasque. LT, USN. Robust Neural Networks Against Adversarial Attacks using Tropical Geometry. M. S. in Operations Research, Anticipated September 2024. (Co-Advising with R. Yoshida)
5. K. S. Combs. LCDR, SC, USN. Simulation-Based Flow Analysis for the DDNV Material Processing Center. M. S. in Operations Research, Anticipated March 2025.
6. L. Fairley. Approximate Dynamic Programming for the Maintenance of Controlled Network Infrastructure. Ph.D. in Management Science, Lancaster University, Anticipated 2026 (Co-Advising with P. Jacko and R. Shone)

Graduated Advisees:

1. H. Ilyas. MAJ, Pakistan Army. Understanding the Drivers of Extreme Precipitation Events in Pakistan. M. S. in Operations Research, December 2023. link
2. A. P. Davidson. LCDR, SC, USN. An Evaluation of Time Series Methods for Workload Forecasting at the DDNV Material Processing Center. M. S. in Operations Research, September 2023. (MORS Tisdale Graduate Research Prize Finalist) link
3. C. L. Byers, Capt, USMC. Optimizing Service Level Training Exercise (SLTE) Schedules. M. S. in Operations Research, June 2023. link
4. A. J. Cooper. LCDR, SC, USN. Game-Theoretic Models for Rapid Operational Airlift Network Design in Contested Environments. M. S. in Operations Research, March 2023. (MORS Tisdale Graduate Research Prize Finalist) link
5. N. T. Marlow. LCDR, SC, USN. Machine Learning for Aircraft Oil Analysis Using Noisy Field Data. M. S. in Operations Research, September 2022. link
6. C. K. Baldwin. ENS, USN. Identifying Interdependencies Within the Naval Aviation Engine Depot Readiness Assessment Model (EDRAM). M. S. in Operations Research, December 2021. (MORS Tisdale Graduate Research Prize Winner) link
7. N. C. Anthony. Capt, USMC. Increasing Efficiency of Class IX Repair Part Blocks for Deployed Units. M. S. in Operations Research, June 2021. link
8. Z. C. Polson. Capt, USMC. Bayesian Networks for Automated Equipment Diagnostic Support. M. S. in Operations Research, June 2021. link
9. C. T. Norman. LCDR, SC, USN. Optimal Pre-Position of Bulk Fuel Resources: A Stochastic Simulation and Analysis. M. S. in Operations Research, March 2021. (MORS Tisdale Graduate Research Prize Finalist, CNO Award for Excellence in Operations Research Winner) link
10. J. T. S. Tan. CDR, Singapore Navy. Optimal Positioning of Remotely Piloted Fuel Bladders to Support Distributed Maritime Operations. M. S. in Operations Research, December 2020. link
11. S. D. Kasdan. Maj, USMC. Optimal Pre-Positioning of Bulk Fuel Resources. M. S. in Operations Research, June 2020. link

As Co-Advisor:

1. X. K. Chan. DSO National Laboratories. Machine Learning Method to Optimize Targeting of Physical Networks with Stochastic Outcomes. M. S. in Operations Research, March 2024. (Co-Advised with R. Yoshida)
2. E. D. Laforteza. LT, USN. Determining Optimal UAS Platforms to Pair with Surface Vessels. M. S. in Operations Research, March 2024. (MORS Tisdale Graduate Research Prize Finalist, Surface Navy Association Award for Excellence in Surface Warfare Research Winner, Co-Advised with B. P. Wood)
3. M. Luporini. LCDR, SC, USN. Updating the Replenishment at Sea Planner (RASP) for Contested Environments. M. S. in Operations Research, March 2024. (MORS Tisdale Graduate Research Prize Winner, Monterey Peninsula Council of the Navy League LCDR Tom Winant Highest Academic Achievement Award Winner, Co-Advised with P. Nesbitt)
4. Y. Lin. CPT, Taiwan Marine Corps. Online Optimization for Routing in Dynamic Contested Environments. M. S. in Operations Research, December 2023. (Co-Advised with R. Yoshida) link
5. J. Cantwell. LCDR, USN. Quantifying the Value of Environmental Sources and Acoustic Forecasts in Undersea Warfare Decision-Making. M. S. in Operations Research, September 2023. (Co-Advised with E. Regnier) link
6. L. J. Horan. LCDR, SC, USN. Predicting the Authenticity of Code-Switched Text Generated by a Large Language Model. M. S. in Operations Research, September 2023. (Co-Advised with R. Yoshida) link
7. I. A. Henry. LCDR, SC, USN. Metrics to Predict How Future Budget Requirements Affect the Airframe Depot Readiness Assessment Model. M. S. in Operations Research, June 2023. (Co-Advised with D. J. MacKinnon) link
8. R. Kulkarni. LCDR, Indian Navy. Optimizing the Placement of Wave Gliders for Anti-Submarine Warfare. M. S. in Operations Research, March 2023. (Co-Advised with L. L. Chen) link
9. K. E. Plunkett. LT, USN. An Evaluation of Randomized Routing Strategies for Deception in Mobile Networked Control Systems. M. S. in Operations Research, March 2023. (Co-Advised with R. Yoshida) link
10. A. M. Alleman. LCDR, SC, USN. An Analysis of Markov Chain Monte Carlo Methods in Multi-Indenture Inventory Optimization. M. S. in Operations Research, September 2022. (Co-Advised with R. Yoshida) link
11. E. V. Vargas. LT, USN. Optimal Placement of Shallow Water Sensors. M. S. in Operations Research, September 2022. (MORS Tisdale Graduate Research Prize Finalist, Co-Advised with R. L. Bassett) link
12. R. M. Witt. LCDR, SC, USN. Analysis of the Single Fuel Concept within the EUCOM Area of Responsibility. M. S. in Operations Research, September 2022. (Co-Advised with G. Ferrer) link
13. J. B. Frey. Capt, USMC. An Analysis of MARCORLOGCOM SECREP Lead Times. M. S. in Operations Research, June 2021. (Co-Advised with G. Ferrer) link
14. M. W. Luerman. ENS, USN. Naval Aviation Performance/Pricing Model Readiness Analysis. M. S. in Operations Research, December 2020. (Co-Advised with D. J. MacKinnon) link
15. E. T. Rajchel. LCDR, USN. The Conceptual Design Reliability Prediction Method: Establishing Functional-Physical Reliability Relationships for System Reliability Predictions During Conceptual Design. M. S. in Systems Engineering, September 2020. (Co-Advised with B. M. O'Halloran; NAVSEA Award for Excellence in Systems Engineering Winner) link
16. W. Thaw. LCDR, USN. Analysis of How Funding Decisions Affect F/A-18E/F Super Hornet Aircraft Readiness Levels. M. S. in Operations Research, March 2020. (Co-Advised with D. J. MacKinnon) link

As Second Reader:

1. W. Ko. LT, USN. Statistically Predicting Navy Enlisted Sailors' Length of Stay. M. S. in Operations Research, September 2023. (Advisor: R. Yoshida) link
2. M. Seri. LT, Israeli Navy. Large-Scale Hypothesis Testing with Corrupted Data. M. S. in Operations Research, September 2023 (Co-Advisors: L. L. Chen & R. Szechtman) link
3. J. P. Waggener. LCDR, SC, USN. Identifying Out-of-Family Oil Sample Results for H-53E Navy Oil Analaysis Program (NOAP) Enrolled Components. M. S. in Operations Research, September 2023 (Advisor: R. Yoshida; MORS Tisdale Graduate Research Prize Finalist, CNO Award for Excellence in Operations Research Winner)
4. H. K. Kang. CPT, USA. Correlation Analysis of Navy Flight Mishaps. M. S. in Operations Research, June 2023. (Advisor: R. Yoshida) link
5. R. C. Herrmann. CPT, USA. Predicting the Incidence of Military Aviation Safety Mishaps. M. S. in Operations Research, June 2022. (Advisor: R. Yoshida) link
6. S. V. Linton. LT, USN. Recruit processing at the San Jose Military Entrance Processing Station (MEPS). M. S. in Operations Research, September 2021. (Advisor: P. A. Jacobs) link
7. Z. H. Swenson. ENS, USN. Predictive Statistical Modeling of Naval Reserve Officers Training Corps Attrition. M. S. in Operations Research, December 2020. (Advisor: R. Yoshida) link
8. J. E. Good. LCDR, SC, USN. An Operational Model of Critical Supply Chain for the U.S. Virgin Islands. M. S. in Operations Research, September 2019. (Co-Advisors: D. L. Alderson & D. A. Eisenberg; MORS Tisdale Graduate Research Prize Winner) link
9. G. Cavalcanti. LCDR, USN. Exploring On-Board Oxygen Generation System Failures in F/A-18 and EA-18 Aircraft As a Function of Cumulative Flight Hours. M. S. in Operations Research, June 2019. (Advisor: R. A. Koyak) link

Capstone Projects:

1. M. P. Witte (LT, USN), with A. C. Stanislav (LT, USN) et al. Multi-Domain Manned-Unmanned Littoral Denial System. Systems Engineering Analysis Capstone Project, June 2023. (Co-Advised with F. A. Papoulias) link
2. N. D. Walker (CDR, USN), with J. J. Brown (LT, USN) et al. Mission Engineering for Hybrid Force 2025. Systems Engineering Analysis Capstone Project, June 2022. (Co-Advised with F. A. Papoulias) link
3. C. R. Hust (LT, USN) and A. P. Kavall (LT, USN), with M. Bernkopf (Capt, USMC) et al. Analysis of Rare Earth Element Supply Chain Resilience During a Major Conflict. Systems Engineering Analysis Capstone Project, June 2021. (Co-Advised with F. A. Papoulias) link

Teaching:

• Naval Postgraduate School
1. OA2801: Computational Methods for Operations Research (AY22 Q1 & Q3, AY23 Q1 & Q3)
2. OA3101: Probability (AY19 Q1 & Q3, AY20 Q1 & Q3, AY21 Q1 & Q3, AY24 Q1 & Q3)
3. OA4118: Statistical and Machine Learning (AY22 Q4, AY23 Q4)
4. OS3180: Probability and Statistics for Systems Engineering (AY19 Q1 & Q3, AY20 Q3, AY21 Q3)
• National Yang Ming Chiao-Tung University
1. Markov Decision Processes and their Applications to Supply Chain Management (10th ORSCM Workshop, June 24-25, 2018). slides, Jupyter notebook, MDP.py
• Cornell University
1. ORIE 3510: Introduction to Engineering Stochastic Processes I (Spring 2018): Co-taught with Jim Dai
2. ORIE 5122: Inventory Management (Fall 2017)
3. ENGRD 2700: Basic Engineering Probability and Statistics (Spring 2017): Co-taught with Shane Henderson
4. ENGRD 2700: Basic Engineering Probability and Statistics (Fall 2016)
• Stony Brook University
1. AMS 151: Applied Calculus I (Fall 2012, Spring 2013, Winter 2015)

Service:

• Military Operations Research Society
1. Elected Voting Member, Board of Directors, 2022-26.
2. Member, Continuing Education Committee, 2022-24.
3. Member, Prizes & Awards Committee, 2023-24
4. Member, Membership Committee, 2022-23.
5. Deputy Chair, Publications Committee, 2023-24
6. Member, Publications Committee, 2022-23.
• Naval Postgraduate School
1. Faculty Council Representative, Operations Research Department, 2023-26.
2. Chair, Planning, Facilities, and Support Services Committee, Faculty Council, 2022-23.
3. Elected Member, Planning, Facilities, and Support Services Committee, Faculty Council, 2020-23.
• INFORMS Applied Probability Society
1. Webmaster, 2018-2020.

Patents:

1. F. Heng, J. Huang, T. Kumar, and R. Zhang. System and Method for Forecasting Leaks in a Fluid-Delivery Pipeline Network. US 11,620,553 B2. 2023. Google Patents

PhD Dissertation: Complexity estimates and reductions to discounting for total and average-reward Markov decision processes and stochastic games. Department of Applied Mathematics and Statistics, Stony Brook University, 2016. pdf

Master's Thesis: Piecewise truckload network procurement. Department of Civil & Environmental Engineering, Massachusetts Institute of Technology, 2011. link

During the summer of 2015, I interned with the Smarter Energy group at IBM Research in Yorktown Heights, NY.

From 2008 to 2009, I worked at Cambridge Systematics in Oakland, CA.

I earned a B.S. in Civil & Environmental Engineering from UC Berkeley in 2008.