Research areas
I am a machine learning researcher with extensive experience in both academia and industry, specializing in natural language processing and machine learning. I am passionate about translating cutting-edge research into real-world applications, and I maintain a deep curiosity about fundamental scientific mysteries—particularly the emergence of human language.
See more https://lephong.github.io/
PhD supervision
- Aarushi Sharma
- Victor Yuan
Selected publications
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Open access
An emergent communication framework for honeybee waggle dance
Siregar, N., Le, P. & G. Alhama, R., 21 Apr 2026, The evolution of language: proceedings of the 16th international conference on the evolution of language (EVOLANG XVI). Hartmann, S., Sibierska, M., Fröhlich, M., Jadoul, Y., Josserand, M., Matzinger, T., Mudd, K., Nölle, J., Pleyer, M., Wacewicz, S. & Żywiczyński, P. (eds.). Nijmegen: The Evolution of Language Conferences, p. 435-442 8 p. (International conference on the evolution of language).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Open access
Encoding without influence: dissociating demographic representation from causal effect in large language models
Sharma, A. & Le, P., 10 Jun 2026, In: Transactions on Machine Learning Research. 34 p.Research output: Contribution to journal › Article › peer-review
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Open access
Hierarchical text classification with LLM-refined taxonomies
Golde, J., Jedema, N., Krishnan, R. & Le, P., 24 Mar 2026, Proceedings of the 19th conference of the European Chapter of the Association for Computational Linguistics. Demberg, V., Inui, K. & Marquez, L. (eds.). Kerrville, TX: Association for Computational Linguistics, Vol. 1 (Long Papers). p. 214–228 15 p. (Proceedings of the Association for Computational Linguistics. European Chapter).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
Prediction of propene hydroformylation using machine learning
Tripathi, A., Lozano-Perez, A. S., Fuentes, J. A., Clarke, M. L., von Wolff, N., Dang, N., Le, P. & Kumar, A., 17 Oct 2025, ChemRxiv, 7 p.Research output: Working paper › Preprint