Liang HONG Pronunciation: /liɑŋ hɔŋ/ (similar to "ryo"; the kanji means "brightness") PhD in Computer Science and Engineering, CUHK (2026) Hong Kong SAR, China Email: brighthong233 [AT] gmail.com GitHub: heathcliff233 Google Scholar: profile

About Me

I recently completed my PhD in the Department of Computer Science and Engineering at The Chinese University of Hong Kong, advised by Prof. Yu Li. My research focuses on biomolecule modeling, including structure prediction / design, bio-FM pretraining, and transcriptomics studies.

From April 2025 to June 2026, I was a research intern at ByteDance Seed, where I worked on evolution-inspired diffusion language models for protein sequence understanding (DPLM-evo), as well as structure prediction and design tooling.

I previously worked as a research assistant at RIKEN Center for Integrative Medical Sciences (IMS) on genomic models for mutagenesis studies.

News

• Jun. 2026: Completed PhD at CUHK.
• May 2026: "Towards A Generative Protein Evolution Machine with DPLM-evo" accepted to ICML 2026.
• Apr. 2025 - Jun. 2026: Research Intern at ByteDance Seed.
• Nov. 2024 - Feb. 2025: Research Assistant at RIKEN IMS.
• Sep. 2022: Started PhD study at CUHK.
• Feb. 2022: Research Intern at BioMap.

Research Interests

• RNA structure prediction and design
• Diffusion (language) models for biomolecule pretraining
• Biological sequence analysis
• Transcriptomics study

Selected Publications and Preprints

[Google Scholar]

• [DHR] Fast, sensitive detection of protein homologs using deep dense retrieval
  L. Hong, Z. Hu, S. Sun, X. Tang, J. Wang, Q. Tan, L. Zheng, S. Wang, S. Xu, I. King, M. Gerstein, Y. Li
  Nature Biotechnology, 2024.
  doi:10.1038/s41587-024-02353-6
• [DPLM-evo] Towards A Generative Protein Evolution Machine with DPLM-evo
  X. Wang*, L. Hong*, J. Ye, Z. Zheng, Y. Li, S. Huang, Q. Gu ^*equal contribution
  ICML, 2026 (Accepted).
• [RhoDesign] Deep generative design of RNA aptamers using structural predictions
  F. Wong, D. He, A. Krishnan, L. Hong, A. Z. Wang, J. Wang, Z. Hu, S. Omori, A. Li, J. Rao, Q. Yu, W. Jin, T. Zhang, K. Ilia, J. X. Chen, S. Zheng, I. King, Y. Li, J. J. Collins
  Nature Computational Science, 2024.
  doi:10.1038/s43588-024-00720-6
• [RhoFold+] Accurate RNA 3D structure prediction using a language model-based deep learning approach
  T. Shen, Z. Hu, S. Sun, D. Liu, F. Wong, J. Wang, J. Chen, Y. Wang, L. Hong, J. Xiao, L. Zheng, T. Krishnamoorthi, I. King, S. Wang, P. Yin, J. J. Collins, Y. Li
  Nature Methods, 2024.
  doi:10.1038/s41592-024-02487-0
• [DEER] Exploring functional insights into the human gut microbiome via the structural proteome
  H. Liu, J. Shen, Z. Zhang, J. Wang, C. Zhang, L. Zheng, H. Ni, L. Hong, J. Zhang, D. Xue, J. Chen, Q. Yu, Z. Qu, Y. Ma, T. Si, L. Zheng, S. Wang, C. Jiang, Y. Li, K. Wang, L. Dai
  Cell Host and Microbe, 2025 (Accepted).
  doi:10.1016/j.chom.2025.11.001
• [RiboDiffusion] RiboDiffusion: Tertiary structure-based RNA inverse folding with generative diffusion models
  H. Huang, Z. Lin, D. He, L. Hong, Y. Li
  ISMB, 2024.
  doi:10.1101/2024.04.18.590187
• [RNA-FM] Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions
  J. Chen, Z. Hu, S. Sun, Q. Tan, Y. Wang, Q. Yu, L. Zong, L. Hong, J. Xiao, T. Shen, I. King, Y. Li
  arXiv, 2022.
  arXiv:2204.00300

Other Publications

• Learning meaningful representation of single-neuron morphology via large-scale pre-training
  Y. Fan, Y. Li, Y. Zhong, L. Hong, L. Li, Y. Li
  ECCB 2024.
  doi:10.1093/bioinformatics/btae395
• Deep learning for RNA structure prediction
  J. Wang, Y. Fan, L. Hong, Z. Hu, Y. Li
  Current Opinion in Structural Biology, 2025.
  doi:10.1016/j.sbi.2025.102991
• Self-supervised contrastive learning for integrative single cell RNA-seq data analysis
  W. Han, Y. Cheng, J. Chen, H. Zhong, Z. Hu, S. Chen, L. Zong, L. Hong, T.-F. Chan, I. King, X. Gao, Y. Li
  Briefings in Bioinformatics, 2022.
  doi:10.1093/bib/bbac377
• AcrNET: predicting anti-CRISPR with deep learning
  Y. Li, Y. Wei, S. Xu, Q. Tan, L. Zong, J. Wang, Y. Wang, J. Chen, L. Hong, Y. Li
  Bioinformatics, 2023.
  doi:10.1093/bioinformatics/btad259

Research Experience

• Research Intern, ByteDance Seed, Shanghai (Apr. 2025 - Jun. 2026)
  Advisors: Prof. Quanquan Gu, Dr. Zaixiang Zheng
  • Worked on an evolution-inspired diffusion language model for protein sequence understanding (DPLM-evo).
  • Contributed to structure prediction and design tooling (SeedFold, SeedProteo).
• Research Assistant, RIKEN Center for Integrative Medical Sciences (IMS), Japan (Nov. 2024 - Feb. 2025)
  Advisor: Prof. Chung Chau Hon, Dr. Ramzan Umarov
  • Worked on AlphaGenome-like sequence-to-expression prediction models.
  • Participated in mutagenesis analysis; manuscript in preparation.
• PhD Candidate, CUHK, Hong Kong SAR (Sep. 2022 - Jun. 2026)
  Advisor: Prof. Yu Li
  • Published DHR and continued sequence analysis research.
  • Participated in CASP15 and won first place in RNA track (AIchemy team).
  • Co-led RNA structure prediction (RhoFold+) and sequence design (RiboDiffusion, RhoDesign).
  • Leading diffusion RNA design and assisted structure modeling tool development (submitted).

Education

• PhD in Computer Science and Engineering, The Chinese University of Hong Kong (Sep. 2022 - Jun. 2026)
• BSc in Computer Science, Nanjing University (Sep. 2018 - Jun. 2022)

Academic Service

• Reviewer for journals: Science, Cell, Nature Methods, Nature Communications, Cell Systems, Cell Reports, Advanced Science, Briefings in Bioinformatics, PLoS Computational Biology
• Reviewer for conferences: ISMB, ICLR, NeurIPS