CV

Louis L. Chen
Assistant Professor, Department of Operations Research
Naval Postgraduate School

Updated October 2026.

Appointments · Education · Publications · Research funding · Teaching · Advising and mentoring · Talks · Service

Appointments

• Assistant Professor, Department of Operations Research, Naval Postgraduate School, Monterey, CA. December 2019–present.
• Academic Visitor, National University of Singapore, Industrial Systems and Engineering; Institute of Operations Research and Analytics. September–December 2019. Host: Wang Chi Cheung.
• Summer Research Intern, Alibaba, Machine Intelligence Technology Group. June–September 2018. Research on cloud scheduling and robust omnichannel inventory replenishment. Supervisor: Sen Yang.
• Visiting Research Scholar, Singapore University of Technology and Design. June–August 2017. Research on distributionally robust optimization with marginal constraints. Host: Karthik Natarajan.
• Research Assistant, Massachusetts Institute of Technology. Fall and Spring terms, 2014–2018. Dissertation research on robust optimization in operations and revenue management. Supervisor: David Simchi-Levi.

Education

• Ph.D. in Operations Research, Massachusetts Institute of Technology, September 2019. Advisor: David Simchi-Levi. Dissertation: Distributionally Robust Optimization with Marginals: Theory and Applications.
• B.A. in Computational and Applied Mathematics; B.S. in Mechanical Engineering, Rice University, June 2012. Magna cum laude. Undergraduate advisor: Ilya Hicks.

Publications

Additional paper descriptions are available on my publications page.

^* First and corresponding author; ^○ corresponding author.

Refereed journal articles

1. Johannes O. Royset, Louis L. Chen^○, and Eric Eckstrand (2024). “Rockafellian Relaxation and Stochastic Optimization Under Perturbations.” Mathematics of Operations Research, 50(3):1585–1610. DOI.
2. Louis L. Chen ^*, Chee Chin Lim, Divya Padmanabhan, and Karthik Natarajan (2024). “Robustness to Dependency in Influence Maximization.” Management Science, 71(3):2696–2713. DOI.
3. Louis L. Chen ^*, Will Ma, Karthik Natarajan, David Simchi-Levi, and Zhenzhen Yan (2022). “Distributionally Robust Linear and Discrete Optimization with Marginals.” Operations Research, 70(3):1822–1834. DOI.

The 2024 dates above are online-publication dates; the corresponding journal issues appeared in 2025.

Refereed conference papers

1. Louis L. Chen ^*, Bobbie Chern, Eric Eckstrand, Amogh Mahapatra, and Johannes Royset (2026). “Enhancing Learning with Noisy Labels via Rockafellian Relaxation.” International Conference on Learning Representations (ICLR 2026).
2. Louis L. Chen ^*, Matan Seri, and Roberto Szechtman (2024). “On the Adversarial Robustness of Benjamini Hochberg.” Advances in Neural Information Processing Systems (NeurIPS 2024), Article 2887, pp. 90965–90988.
3. Louis L. Chen ^*, Divya Padmanabhan, Chee Chin Lim, and Karthik Natarajan (2020). “Correlation Robust Influence Maximization.” Advances in Neural Information Processing Systems (NeurIPS 2020), 33:7078–7089, Article 594.
4. Louis L. Chen ^*, Will Ma, James B. Orlin, and David Simchi-Levi (2020). “Distributionally Robust Max Flows.” Symposium on Simplicity in Algorithms (SOSA 2020), pp. 81–90. SIAM.

Manuscripts under review

1. Louis L. Chen ^*, Ang Xu +, Roberto Szechtman, Chiwei Yan, and Vincent Vanterpool (2026). “Meeting Uncertain Threats with Feedback.” Major Revision at Operations Research.

2. Louis L. Chen ^* and Jake Roth (2026). “Repair-Based Duality for Distributionally Robust Optimization via Conditional-Moment Optimal Transport.” Submitted to Mathematical Programming.

Preprints and working papers

1. Louis L. Chen ^*, Jake Roth, and Johannes Royset (2026). “Perturbation Duality for Robust and Distributionally Robust Optimization: Short and General Proofs.” arXiv preprint.
2. Louis L. Chen ^*, Jake Roth, and Johannes Royset (2026). “Simplifying Primal-Worst Equals Dual-Best via Perturbations.” In preparation.
3. Louis L. Chen ^*, Hanzhang Qin, David Simchi-Levi, and Zirun Zhang (2019). “Distributionally Robust Omnichannel Stocking Decisions in Quick Fulfillment Systems.” SSRN preprint.
4. Louis L. Chen ^* and David Simchi-Levi (2017). “On the Structure of Cardinality Constrained Assortment Optimization.” In preparation.

Patent applications

• “Packing Management Techniques with Packing Movement Series, Variable Packing and Accountability.” U.S. patent application No. 19/448,087, filed January 13, 2026.
• “Packing Management Techniques with Accountability.” U.S. provisional patent application No. 63/744,795, filed January 13, 2025.

Research funding

• Reducing Training Delays in Naval Aviation Through Simulation — Naval Research Program. $125,000, October 1, 2026–December 31, 2027. Principal investigator. Research with Roberto Szechtman on discrete-event simulation of naval aviation training, scheduling, and resource allocation.
• Aligning Large Language Models for Naval Report Automation — Office of Naval Intelligence. $200,000, October 1, 2026–September 30, 2027. Principal investigator. Research with Eric Eckstrand.
• Optimal Decision Making under Tight Performance Requirements in Adversarial and Uncertain Environments: Insights from Rockafellian Functions — Air Force Office of Scientific Research. $408,336, October 2021–October 2024. Co-principal investigator in year 1; principal investigator in years 2–3. With Johannes O. Royset. Research on perturbation methods, robust optimization, and learning with noisy data.
• Operational Decision-Making and Learning Under Uncertainty: A (Distributionally) Robust Optimization Approach — RIP. $59,434.07 labor and $28,000 non-labor, AY2020–AY2021. Principal investigator. Research on optimization under uncertain dependence and applications to inventory, scheduling, and network design.

Teaching

Supporting course pages are available on my teaching page.

Naval Postgraduate School — Instructor

Academic-year designations follow the NPS teaching record.

• OS3680 — Naval Tactical Analysis. Spring AY2026. Resident and distance-learning sections, with 11 and 29 students, respectively.
• OA4201 — Nonlinear Programming. Summer AY2023; Winter and Summer AY2024; Winter and Summer AY2025; Winter AY2026. Nine sections, with enrollments of 10–23 students.
• OA3602 — Search Theory and Detection. Fall and Spring AY2021; Spring AY2022; Fall and Spring AY2023; Spring AY2024; Winter AY2025; Winter AY2026. Eight offerings, with enrollments of 8–18 students.
• OA3101 — Probability. Fall and Spring AY2022; Fall and Spring AY2023. Five sections, with enrollments of 10–20 students.
• OS3111 — Probability and Statistics for HSI and MOVES. Winter and Summer AY2020; Fall and Summer AY2021. Five sections, with enrollments of 12–23 students; resident and distance-learning instruction, including distance learning during the COVID-19 pandemic.
• OA3201 — Linear Programming. Winter AY2020. Two sections, with 25 and 14 students.

Massachusetts Institute of Technology — Teaching Assistant

• 15.763 — Manufacturing Systems and Supply Chain Design, Spring 2016.
• 15.075 — Statistical Thinking and Data Analysis, Fall 2014.
• 15.761 — Introduction to Operations Management, Summer 2014.

Professional and fleet instruction

Operations Analysis in Naval Operational Planning, NPS Extended Campus. Selected to help modernize the five-day short course and associated three-day practica for NAWDC MISR Weapons and Tactics Instructors. Contributed syllabus redesign, scenario development, and instruction in stochastic, decision-analytic, and simulation methods. Direct instruction: 6–10 hours, with 4–8 additional hours of practical problem facilitation. Collaborator: CDR Paolo J. Singh; instruction includes Naval Air Station Fallon.

Advising and mentoring

Master’s thesis advisor

Naval Postgraduate School. Degrees are in Operations Research unless otherwise indicated.

• Ryan Gallagher, expected March 2027. Optimizing Navy Recruitment Productivity: A Data-Driven Approach.
• Vince Vanterpool, September 2024. Dynamic Weapon Target Assignment in Support of Force Level Effects Coordination. MORS Tisdale Award.
• Junnel Q. Layug, June 2024. Statistical Analysis of Manpower Attrition in the Naval Reserve Officer Training Corps.
• Matan Seri, September 2023. Large-Scale Hypothesis Testing with Corrupted Data. Donald P. Gaver Thesis Research Award.
• Jeremy Berg, March 2023. Budget-Constrained Robust Influence Maximization.
• Rohan Kulkarni, March 2023. Optimal Placement of Wave Gliders for Anti-Submarine Warfare.
• Spencer Kitten, September 2022. Revisiting Coordinated Submarine Tactics Using Modern Computational Methods. MORS Tisdale Award.
• Young Hong, September 2022. Optimizing First-Term Retention of Sailors.

Master’s thesis co-advisor

• Park Seok, September 2024. Enhancing Naval Retention: A Strategic Approach to Allocating Selective Reenlistment Bonuses.
• Alexander G. Moore, June 2024. The Multi-Secretary Problem with Random Lifetime: Applications in UAV Target Selection.
• Victor Benicio Ardilha da Silva Alves, June 2024. Online Large-Scale Hypothesis Testing with Corrupted Data.
• Marcus Garcia, September 2022. An Analysis of Covid-19 Misinformation on the Telegram Social Network.
• Joseph Novak, September 2021. Optimization for Human Systems Integration. Master’s degree in Human Systems Integration. MORS Tisdale Finalist.

Doctoral dissertation committees

Committee member, Naval Postgraduate School, Operations Research.

• John Joseph Sabol, September 2026. Tropical Fermat–Weber Problems: Geometry, Combinatorics, and Optimization.
• Peter David Barkley, September 2025. Optimization via Adaptive Resolvent Splitting.

Master’s thesis second reader

Naval Postgraduate School, Operations Research.

• Roi Huminer Goldenberg, September 2026. Optimal Allocation Policies Under Friend-or-Foe Uncertainty.
• Noah A. Richwine, December 2025. Leveraging Oceanographic Forecasting for Optimized Submarine Search Path Planning Under Dynamic Conditions.
• Zachary S. Chyra, March 2025. Evaluation of the Logarithmic Spiral Search Pattern for Antisubmarine Warfare.
• Yonatan Sigal, September 2024. A Neural Network Perspective on the Multi-Secretary Problem.
• Stephen J. Martir, September 2024. Improved Endurance Supply Solutions for Naval Aviation.
• Sam T. Fritz-Schreck, June 2024. Strategic Workforce Management Through Simulation-Online Optimization.
• Wei Tien Chea, September 2023. Optimizing Ammunition Management in Singapore. MORS Tisdale Finalist.
• Christopher Newcomb, June 2023. Uncertainty in Risk Assessment Derived from Subject Matter Expert Elicitation.

Talks

Invited talks and conference presentations, listed from newest to oldest. Linked paper titles identify the associated research.

Additional presentation details are available on my talks page.

• April 2026 — International Conference on Learning Representations (ICLR). Enhancing Learning with Noisy Labels via Rockafellian Relaxation.
• April 2026 — Computer Science Theory Seminar, University of California, Irvine. On the Adversarial Robustness of Benjamini Hochberg.
• October 2025 — INFORMS Annual Meeting, Atlanta, GA. “Meeting Uncertain Demand with Deadlines.”
• July 2025 — International Conference on Continuous Optimization, University of Southern California, Los Angeles, CA. On the Adversarial Robustness of Benjamini Hochberg.
• December 2024 — NeurIPS, Vancouver, Canada. On the Adversarial Robustness of Benjamini Hochberg.
• October 2024 — INFORMS Annual Meeting, Seattle, WA. On the Adversarial Robustness of Benjamini Hochberg.
• August 2024 — AFOSR Mathematical Optimization Program Review, Arlington, VA. “Optimal Decision Making under Tight Performance Requirements in Adversarial and Uncertain Environments: Insight from Rockafellian Functions.”
• July 2024 — International Symposium on Mathematical Programming, Montreal, Canada. On the Adversarial Robustness of Benjamini Hochberg.
• April 2024 — INFORMS Conference on Analytics, Orlando, FL. On the Adversarial Robustness of Benjamini Hochberg.
• March 2024 — INFORMS Conference on Optimization, Houston, TX. On the Adversarial Robustness of Benjamini Hochberg.
• December 2023 — Department of Industrial and Information Management Seminar, National Cheng Kung University. Robustness to Dependency in Influence Maximization.
• October 2023 — INFORMS Annual Meeting, Phoenix, AZ. Robustness to Dependency in Influence Maximization.
• August 2023 — AFOSR Mathematical Optimization Program Review, Arlington, VA. “Optimal Decision Making under Tight Performance Requirements in Adversarial and Uncertain Environments: Insight from Rockafellian Functions.”
• July 2023 — International Conference on Stochastic Programming, University of California, Davis. “Optimistic-Pessimistic Duality.”
• October 2022 — INFORMS Annual Meeting, Indianapolis, IN. “Rockafellian Relaxation in Optimization under Uncertainty: Asymptotically Exact Formulations.”
• September 2022 — Lee Kong Chian School of Business Seminar, Singapore Management University. “Product Recommendation and Robust Influence Maximization.”
• July 2022 — International Conference on Continuous Optimization, Lehigh University, Bethlehem, PA. “Rockafellian Relaxation in Optimization under Uncertainty: Asymptotically Exact Formulations.”
• March 2022 — INFORMS Conference on Optimization, Greenville, SC. “Rockafellian Relaxation in Optimization under Uncertainty: Asymptotically Exact Formulations.”
• October 2021 — INFORMS Annual Meeting, Houston, TX. Correlation Robust Influence Maximization.
• December 2020 — NeurIPS, online. Correlation Robust Influence Maximization.
• October 2020 — INFORMS Annual Meeting. Robustness to Dependency in Influence Maximization.
• January 2020 — Symposium on Simplicity in Algorithms, Salt Lake City, UT. Distributionally Robust Max Flows.
• July 2019 — Manufacturing & Service Operations Management Conference. Distributionally Robust Omnichannel Stocking Decisions in Quick Fulfillment Systems.
• July 2018 — International Symposium on Mathematical Programming. Distributionally Robust Linear and Discrete Optimization with Marginals.

Consulting

Algorithms Consultant, 14Bis, 2021–present. Optimization of logistics operations for Small Business Innovation Research projects sponsored by the Marine Corps, Air Force, and Space Force.