People / Nhan Ly-Trong

Research Associate, Australian National University

Biography

Nhan Ly-Trong received his Ph.D. from the Australian National University (ANU) in 2024. He holds a Bachelor of Engineering in Computer Networks and Communications (2016) and a Master’s degree in Computer Science (2019), both from the University of Information Technology – Vietnam National University-HoChiMinh City. After completing his Ph.D., he continued his work at the Computational Phylogenomics Lab at ANU as a Research Associate in August 2024. For more information, please have a look at his ORCID profile.

Research Interests

His research interests span both Computer Science and Bioinformatics, consisting of Phylogenetics, Machine Learning, and High Performance Computing. He is now focusing on developing new effective methods for phylogenetic inference to deal with challenges from big biological data sets.

Awards and Honours

• 2022: One of the two nominees from ANU for the Google PhD Fellowship.
• 2021: Honourable mention for the best talk at the combined ABACBS and Phylomania conference.
• 2020: ANU University Research Scholarship (covers living stipends) & ANU HDR Fee Remission Merit Scholarship (provides tuition fees).
• 2020: The Science and Technology Scholarship, Vingroup, Vietnam.

Reviewer

Reviewer for various scientific journals, such as Bioinformatics; Systematic Biology; and Molecular Phylogenetics and Evolution.

Publications

1. IQ-TREE 3: Phylogenomic Inference Software using Complex Evolutionary Models highly cited
   T.K.F. Wong, N. Ly-Trong, H. Ren, P. Demotte, H. Baños, A. Roger, E. Susko, C. Bielow, N. De Maio, N. Goldman, M.W. Hahn, M.d. Reis, L.S. Vinh, G. Huttley, R. Lanfear, B.Q. Minh
   Molecular Biology and Evolution 43:msag117 (2026)

2. AliSim: A fast and versatile phylogenetic sequence simulator for the genomic era highly cited
   N. Ly-Trong, S. Naser-Khdour, R. Lanfear, B.Q. Minh
   Molecular Biology and Evolution 39:msac092 (2022)

3. Phylogenomics in the pandemic era
   N. Ly-Trong
   PhD Theses (2024)

4. AliSim-HPC: Parallel sequence simulator for phylogenetics
   N. Ly-Trong, G.M.J. Barca, B.Q. Minh
   Bioinformatics 39:btad540 (2023)

5. CMAPLE: Efficient phylogenetic inference in the pandemic era
   N. Ly-Trong, C. Bielow, N. De Maio, B.Q. Minh
   Molecular Biology and Evolution 41:msae134 (2024)

6. Assessing phylogenetic confidence at pandemic scales
   N. De Maio, N. Ly-Trong, S. Martin, B.Q. Minh, N. Goldman
   Nature 647:472–478 (2025)

7. Rate variation and recurrent sequence errors in pandemic-scale phylogenetics
   N. De Maio, M. Willemsen, S. Martin, Z. Guo, A. Saha, M. Hunt, N. Ly-Trong, B.Q. Minh, Z. Iqbal, N. Goldman
   Nature Methods 23:565–573 (2026)

8. IQ-NET: Fast and Accurate Quartet Phylogenetic Inference Using Deep Learning Trained on Empirical DNA Alignments
   C. Yang, Z. Zhuang, P. Demotte, C.C. Dang, L.S. Vinh, B.Q. Minh, N. Ly-Trong
   Molecular Phylogenetics and Evolution (2026)

9. TreeFormer: A transformer-based tree rearrangement operation for phylogenetic reconstruction
   N. Ly-Trong, F. Albert Matsen IV, B.Q. Minh
   bioRxiv (2024)

10. IQ2MC: A New Framework to Infer Phylogenetic Time Trees Using IQ-TREE 3 and MCMCTree with Mixture Models
    P. Demotte, M. Panchaksaram, H. Kumarasinghe, N. Ly-Trong, M.d. Reis, B.Q. Minh
    EcoEvoRxiv (2025)

11. Modeling Site-Specific Mutation Patterns in Pandemic-Scale Phylogenetics
    S. Martin, N. Ly-Trong, B.Q. Minh, N. Goldman, N. De Maio
    bioRxiv (2026)

12. Tryptophan became part of the universal genetic code post-LUCA
    S. Wehbi, N. Ly-Trong, A. Wheeler, B.Q. Minh, D. Lauretta, J. Masel
    bioRxiv (2026)

13. CMAPLE 2: Fast and Accurate Phylogenetic Inference for Millions of Pathogen Genomes
    N. Ly-Trong, S. Martin, N. Goldman, N. De Maio, B.Q. Minh
    bioRxiv (2026)

14. GTRspmix: Capturing heterogeneity of exchangeabilities across sites to improve protein phylogenetics
    R. Harada, E. Susko, T.K.F. Wong, H. Banos, N. Ly-Trong, R. Lanfear, D.L. Theobald, B.Q. Minh, A.J. Roger
    bioRxiv (2026)

15. ProtFinder: An efficient machine learning framework for protein model selection on real data
    N.H. Tinh, Y. Dong, N. Ly-Trong, L.S. Vinh, B.Q. Minh
    bioRxiv (2026)

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