Dr Nguyen Dang

Dr Nguyen Dang

Lecturer

Researcher profile

Phone
+44 (0)1334 46 3690
Email
nttd@st-andrews.ac.uk

 

Research areas

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

Selected publications

  • 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

  • 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., 19 Mar 2025, (Accepted/In press) Proceedings of the Genetic and Evolutionary Computation Conference 2025. ACM

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • 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

  • Open access

    Cost-Efficient Training for Automated Algorithm Selection

    Kus, E., Miguel, I. J., Akgun, O. & Dang, N., 12 Jul 2024, (Accepted/In press).

    Research output: Contribution to conference › Paper › peer-review

  • 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

  • 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

  • Using automated algorithm configuration for parameter control

    Chen, D., Buzdalov, M., Doerr, C. & Dang, N., 30 Aug 2023, FOGA'23: proceedings of the 17th ACM/SIGEVO conference on Foundations of Genetic Algorithms. Chicano, F., Friedrich, T., Kötzing, T. & Rothlauf, F. (eds.). New York, NY: ACM, p. 38-49 12 p.

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Open access

    A framework for generating informative benchmark instances

    Dang, N., Akgun, O., Espasa Arxer, J., Miguel, I. J. & Nightingale, P., 23 Jul 2022, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Solon, C. (ed.). Dagstuhl: Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 18 p. 18. (Leibniz International Proceedings in Informatics (LIPIcs); vol. 235).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Open access

    A portfolio-based analysis method for competition results

    Dang, N., 31 Jul 2022, ModRef 2022: 21st workshop on constraint modelling and reformulation. Online, 11 p.

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Open access

    Theory-inspired parameter control benchmarks for dynamic algorithm configuration

    Biedenkapp, A., Dang, N., Krejca, M., Hutter, F. & Doerr, C., 8 Jul 2022, GECCO '22: Proceedings of the genetic and evolutionary computation conference. Fieldsend, J. E. (ed.). New York, NY: ACM, p. 766–775 10 p.

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

 

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