Curriculum Vitae - Li Hongmin

Email: lihongmin[at]edu.k.u-tokyo.ac.jp Affiliation: Asai Laboratory, The University of Tokyo

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Research Interests

• Machine Learning for Bioinformatics
• Spectral Clustering
• RNA Sequence Design and Optimization
• Large Language Models for Biological Sequences

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Education

• Ph.D. in Computer Science, University of Tsukuba (March 2022)
• Master’s in Computer Science, University of Tsukuba
• Bachelor’s in Computer Science

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Professional Experience

• 2023.5 - Present

  Postdoctoral Researcher, The University of Tokyo

• 2022.10 - 2023.5

  Machine Learning Engineer, HAOMO.AI (蓝色空间领航者 project)

• 2022.4 - 2022.10

  Researcher, The University of Tokyo (Details)

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Funding

Research Grants

• 2025.10

  Google Grant: Input Data Differentiable Designer: A Novel ML Algorithm for Biological Sequence Optimization - $30,000

• 2024.4

  KAKENHI Young Researcher Grant for developing a large-scale language model integrating RNA sequences and text (Project Info)

Scholarships

• 2021.10

  JST SPRING Fellowship (Pioneering Research Initiated by the Next Generation)

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Publications

Preprints

• 2025.12

  Bian Bian, Yiming Zhang, Hongmin Li, Jiuzhou Zhong, Yutaka Saito. “Large generative mRNA language foundation model for efficient coding sequence generation and design with mRNA-GPT”. bioRxiv. Link

• 2025.10

  Hongmin Li, Goro Terai, Takumi Otagaki, Kiyoshi Asai. “Gradient-based Optimization for mRNA Sequence Design”. bioRxiv. Link, Code

Journal Articles

• 2023.9

  Co-authored “An Integrated Physical Approach to Earthquake-Induced Landslide Susceptibility Incorporating Geological Structure: A Case Study of the Diexi Catchment, Sichuan, China”. Engineering Geology. Link

• 2023.1

  Hongmin Li, Kiyoshi Asai. “LSEC: Large-scale spectral ensemble clustering”. Intelligent Data Analysis. arXiv, Code

• 2022.7

  Xinyi Lin, Hongmin Li, Mingfeng Li, Yuhang Liu, Jie Gao, Qi Tian. “Missing Value Imputation With Low-Rank Matrix Completion in Single-Cell RNA-Seq Data by Considering Cell Heterogeneity”. Frontiers in Genetics. Code

• 2022.6

  Hongmin Li, Kiyoshi Asai. “Divide-and-conquer based Large-Scale Spectral Clustering”. Neurocomputing. arXiv, Code

• 2020.2

  Hongmin Li, Kiyoshi Asai. “An Oversampling Framework for Imbalanced Classification Based on Laplacian Eigenmaps”. Neurocomputing. Link

Conference Papers

• 2020.11

  Hongmin Li, Kiyoshi Asai. “Ensemble Learning for Spectral Clustering”. ICDM 2020. PDF, Code

• 2020.11

  Hongmin Li, Kiyoshi Asai. “Hubness-based Sampling Method for Nyström Spectral Clustering”. IJCNN 2020. Link

• 2019.8

  Hongmin Li, Tianyu Mu, Songcan Chen, Kiyoshi Asai. “Distributed Collaborative Feature Selection Based on Intermediate Representation”. IJCAI 2019. Link

• 2019.8

  Hongmin Li, Kiyoshi Asai. “Large Scale Spectral Clustering Using Sparse Representation Based on Hubness”. CBDCom 2018. Link, Code (Best Paper Award)

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Conference Activities

• 2025.11

  Poster - CREST バイオDX領域 2025年度中間報告シンポジウム (Poster)

• 2025.8

  Oral - RNA Informatics Dojo 2025

• 2024.10

  Poster - APBJC24

• 2024.8

  Oral - RNA Informatics Dojo 2024

• 2024.8

  Participation - IJCAI 24 (Jeju)

• 2024.6

  Participation - WCCI 24 (Yokohama)

• 2024.6

  Participation - 25th Annual Meeting of the RNA Society of Japan

• 2023.8

  Oral - RNA Informatics Dojo 2023

• 2022.8

  Oral - RNA Informatics Dojo 2022

• 2022.3

  Oral - SPRING Fellowship

• 2021.12

  Oral - CollaboTICS 2021

• 2019.6

  Poster - G20 Digital Science Now Symposium

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Peer Review Activities

• 2026.01

  The Journal of Supercomputing

• 2024.11

  Pattern Analysis and Applications

• 2024.11

  Knowledge and Information Systems

• 2024.10

  Neurocomputing

• 2024.09

  The Visual Computer

• 2024.03

  International Journal of Machine Learning and Cybernetics

• 2022.11

  Transactions on Pattern Analysis and Machine Intelligence

• 2022.10

  Briefings in Functional Genomics

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Awards

• 2021.10

  JST SPRING Fellowship (Pioneering Research Initiated by the Next Generation)

• 2020.10

  Second Prize, AETA Earthquake Prediction AI Algorithm Competition 2019

• 2019.10

  Special Award, 3rd Analysys International Algorithm Competition

• 2019.8

  Best Paper Award, CBDCom 2018

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Skills

Programming Languages

• Python, R, MATLAB, C/C++, Java

Machine Learning & Deep Learning

• PyTorch, TensorFlow, scikit-learn
• Spectral Clustering, Large Language Models
• Bioinformatics Applications

Computational Biology

• RNA Sequence Analysis
• mRNA Design and Optimization
• Single-cell RNA-seq Data Analysis

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Last Updated: January 2026