Research areas
Personal homepage: https://ndangtt.github.io/
Broadly speaking, my research interests lie in the intersection between machine learning and optimisation. I am particularly interested in automated algorithm configuration/design, where the aim is to leverage machine learning to automate the development of optimisation algorithms. One of my current research focuses is deep reinforcement learning for Dynamic Algorithm Configuration.
I am also interested and have been working intensively on constraint modelling and solving, where I focus on integrating machine learning and automated algorithm configuration techniques into constraint programming.
PhD supervision
- Duong Phuc Tai Nguyen
- Tianchen Wu
Selected publications
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Open access
Towards generating discriminating instances for multi-agent pathfinding: a case study with shelf-based warehouse scenarios
Wu, T., Barták, R., Dang, N., Miguel, I. & Švancara, J., 10 Feb 2026, p. 1-5. 5 p.Research output: Contribution to conference › Paper › peer-review
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Open access
Athanor: Local search over abstract constraint specifications
Attieh, S., Dang, N., Jefferson, C., Miguel, I. J. & Nightingale, P., Mar 2025, In: Artificial Intelligence. 340, 39 p., 104277.Research output: Contribution to journal › Article › peer-review
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Open access
Constraint models for Klondike
Dang, N., Gent, I. P., Nightingale, P., Ulrich-Oltean, F. & Waller, J., 8 Aug 2025, 31st international conference on principles and practice of constraint programming, CP 2025. de la Banda, M. G. (ed.). Saarbrücken/Wadern: Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, p. 1-20 20 p. 9. (Leibniz international proceedings in informatics (LIPIcs); vol. 340).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Open access
Multi-parameter control for the (1+(λ,λ))-GA on OneMax via deep reinforcement learning
Nguyen, T., Le, P., Doerr, C. & Dang, N., Aug 2025, Proceedings of the 18th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. ACMResearch output: Chapter in Book/Report/Conference proceeding › Conference contribution
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On the importance of reward design in reinforcement learning-based dynamic algorithm configuration: a case study on OneMax with (1+(λ,λ))-GA
Nguyen, T., Le, P., Biedenkapp, A., Doerr, C. & Dang, N., Jul 2025, Proceedings of the genetic and evolutionary computation conference 2025 (GECCO '25). New York: ACM, p. 1162 - 1171Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Open access
Transformer-based feature learning for algorithm selection in combinatorial optimisation
Pellegrino, A., Akgün, Ö., Dang, N., Kiziltan, Z. & Miguel, I., 8 Aug 2025, 31st international conference on principles and practice of constraint programming, CP 2025. de la Banda, M. G. (ed.). Saarbrücken/Wadern: Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, p. 1-22 22 p. 31. (Leibniz international proceedings in informatics, LIPIcs; vol. 340).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Open access
Automatic feature learning for Essence: a case study on car sequencing
Pellegrino, A., Akgün, Ö., Dang, N., Kiziltan, Z. & Miguel, I., 23 Sept 2024, ModRef 2024 - The 23rd workshop on Constraint Modelling and Reformulation (ModRef). 17 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Open access
Cost-efficient training for automated algorithm selection
Kus, E., Akgun, O., Dang, N. & Miguel, I. J., 9 Sept 2024, p. 1-17. 17 p.Research output: Contribution to conference › Paper › peer-review
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Open access
Frugal algorithm selection
Kus, E., Akgun, O., Dang, N. & Miguel, I., 29 Aug 2024, 30th International Conference on Principles and Practice of Constraint Programming. Shaw, P. (ed.). Saarbrücken, Germany: Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 16 p. 38. (Leibniz International Proceedings in Informatics (LIPIcs); vol. 307).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Open access
Automated streamliner portfolios for constraint satisfaction problems
Spracklen, J. L. P. J., Dang, N., Akgun, O. & Miguel, I. J., 1 Jun 2023, In: Artificial Intelligence. 319, 24 p., 103915.Research output: Contribution to journal › Article › peer-review