Nam Ho-Nguyen

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I am a Senior Lecturer in the Discipline of Business Analytics at The University of Sydney Business School.

My research focuses on data-driven optimization models and scalable algorithms for decision-making problems under uncertainty.

Previously, I was a Postdoctoral Research Associate (from September 2019 to May 2020) at the University of Wisconsin-Madison. I completed my PhD in 2019 at the Tepper School of Business, Carnegie Mellon University.

Research

I employ techniques from large-scale optimization, machine learning and statistics to better understand the challenges and capabilities of decision making models under uncertainty.

My research is currently funded by a Discovery Project grant ($625K AUD) and a Discovery Early Career Researcher Award ($470K AUD).

Preprints

Giang-Tran, K.-H., Shafiee, S., & Ho-Nguyen, N. (2025). Efficient conditional gradient methods for solving stochastic convex bilevel optimization problems. https://arxiv.org/abs/2505.18037 (accepted to NeurIPS 2025)
Anderson, E., Ho-Nguyen, N., & Radchenko, P. (2025). Poisson inventory models with many items: An empirical bayes approach. https://arxiv.org/abs/2508.03074
Shen, L., Ho-Nguyen, N., Giang-Tran, K.-H., & Kılınç-Karzan, F. (2024). Mistake, manipulation and margin guarantees in online strategic classification. https://arxiv.org/abs/2403.18176
Lee, D., Ho-Nguyen, N., & Lee, D. (2023a). Non-smooth, Hölder-smooth, and robust submodular maximization. https://arxiv.org/abs/2210.06061
Lee, D., Ho-Nguyen, N., & Lee, D. (2023b). Projection-free online convex optimization with stochastic constraints. https://arxiv.org/abs/2305.01333

Publications

Giang-Tran, K.-H., Ho-Nguyen, N., & Lee, D. (2024). A projection-free method for solving convex bilevel optimization problems. Mathematical Programming. https://doi.org/10.1007/s10107-024-02157-1
Gutman, D. H., & Ho-Nguyen, N. (2023). Coordinate descent without coordinates: Tangent subspace descent on Riemannian manifolds. Mathematics of Operations Research, 48(1), 127–159. https://doi.org/10.1287/moor.2022.1253
Ho-Nguyen, N., Kılınç-Karzan, F., Küçükyavuz, S., & Lee, D. (2023). Strong formulations for distributionally robust chance-constrained programs with left-hand side uncertainty under Wasserstein ambiguity. INFORMS Journal on Optimization, 5(2), 211–232. https://doi.org/10.1287/ijoo.2022.0083
Ho-Nguyen, N., & Wright, S. J. (2023). Adversarial classification via distributional robustness with Wasserstein ambiguity. Mathematical Programming, 198(2), 1411–1447. https://doi.org/10.1007/s10107-022-01796-6
Shen, L., Ho-Nguyen, N., & Kılınç-Karzan, F. (2023). An online convex optimization-based framework for convex bilevel optimization. Mathematical Programming, 198(2), 1519–1582. https://doi.org/10.1007/s10107-022-01894-5
Benadè, G., Ho-Nguyen, N., & Hooker, J. N. (2022). Political districting without geography. Operations Research Perspectives, 9. https://doi.org/10.1016/j.orp.2022.100227
Gutman, D. H., & Ho-Nguyen, N. (2022). Cyclic coordinate descent in the Hölder smooth setting. Operations Research Letters, 50(5), 458–462. https://doi.org/10.1016/j.orl.2022.06.002
Ho-Nguyen, N., & Kılınç-Karzan, F. (2022). Risk guarantees for end-to-end prediction and optimization processes. Management Science, 68(12), 8680–8698. https://doi.org/10.1287/mnsc.2022.4321
Ho-Nguyen, N., Kılınç-Karzan, F., Küçükyavuz, S., & Lee, D. (2022). Distributionally robust chance-constrained programs with right-hand side uncertainty under Wasserstein ambiguity. Mathematical Programming, 196(1-2), 641–672. https://doi.org/10.1007/s10107-020-01605-y
Ho-Nguyen, N., & Kılınç-Karzan, F. (2021). Technical note—dynamic data-driven estimation of nonparametric choice models. Operations Research, 69(4), 1228–1239. https://doi.org/10.1287/opre.2020.2077
Ho-Nguyen, N., & Kılınç-Karzan, F. (2019). Exploiting problem structure in optimization under uncertainty via online convex optimization. Mathematical Programming, 177(1-2), 113–147. https://doi.org/10.1007/s10107-018-1262-8
Ho-Nguyen, N., & Kılınç-Karzan, F. (2018a). Online first-order framework for robust convex optimization. Operations Research, 66(6), 1670–1692. https://doi.org/10.1287/opre.2018.1764
Ho-Nguyen, N., & Kılınç-Karzan, F. (2018b). Primal–dual algorithms for convex optimization via regret minimization. IEEE Control Systems Letters, 2(2), 284–289. https://doi.org/10.1109/LCSYS.2018.2831721
Ho-Nguyen, N., & Kılınç-Karzan, F. (2017). A second-order cone based approach for solving the trust-region subproblem and its variants. SIAM Journal on Optimization, 27(3), 1485–1512. https://doi.org/10.1137/16M1065197

PhD Dissertation

Ho-Nguyen, N. (2019). Models and Efficient Algorithms for Convex Optimization under Uncertainty. https://doi.org/10.1184/R1/9544625.v1

Awards and Honours

  • The University of Sydney Business School Excellence in Research Award for Early Career Researchers 2022.

  • INFORMS Optimization Society Young Researchers Prize 2022 (joint with Huck Gutman; citation).

  • The University of Sydney Business School Freda and Len Lansbury Early-Career Researcher Award 2021.

  • Gerald L. Thompson Doctoral Dissertation Award in Management Science 2019 (citation).

  • Honourable Mention in the INFORMS Optimization Society Best Student Paper Prize 2018 (citation).

Teaching

  • QBUS2310 Management Science, S1 2024, S1 2023, S1 2022

  • BUSS4932 Advanced Optimization for Business, S1 2024, S1 2022, S1 2021

  • QBUS6820 Prescriptive Analytics: From Data to Decisions, S1 2023

  • QBUS6820 Business Risk Management, S2 2021, S2 2020

  • QBUS1040 Foundations of Business Analytics, S2 2022, S1 2021, S1 2020

Contact me

  • Office: Room 4142, H70 Abercrombie Building

  • Email

  • CV

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