Li Hongmin

I am a researcher at the Sato Laboratory for Biomedical Data Science, Institute of Science Tokyo. I am also a guest researcher at the Frith Lab, University of Tokyo. My current research interests center on AI-automated scientific workflows, AI research, bioinformatics, and AI for Science, with a focus on building computational systems that support scientific discovery and biological sequence analysis.

CV Email: lihongmin[at]edu.k.u-tokyo.ac.jp

Recent Activities

2026.06 - Received Google Cloud TPU Builders Awards totaling USD 5,500 in GCP credits to support TPU-based AI and scientific workflow experiments.
2026.05 - Posted “Targeted Tests for LLM Reasoning: An Audit-Constrained Protocol” on arXiv, proposing an audit-constrained protocol for evaluating LLM reasoning under controlled prompt variation.
2026.05 - Posted “Separating Shortcut Transition from Cross-Family OOD Failure in a Minimal Model” on arXiv, analyzing how shortcut learning, shortcut-rule transition, and cross-family OOD failure can separate.
2026.05 - Posted “A Controlled Counterexample to Strong Proxy-Based Explanations of OOD Performance: in a Fixed Pretraining-and-Probing Setup” on arXiv, showing a boundary case where proxy-based structure explanations fail to track task-relevant OOD performance.
2026.05 - Posted “FastUMAP: Scalable Dimensionality Reduction via Bipartite Landmark Sampling” on arXiv, introducing a fast landmark-based dimensionality reduction method for repeated exploratory embedding.
2026.04 - Awarded a KAKENHI Grant-in-Aid for Early-Career Scientists for the ID3 project on input-data differentiable integrated frameworks for biomolecular sequence design.
2026.04 - Joined the Sato Laboratory for Biomedical Data Science at the Institute of Science Tokyo as a researcher; also started as a guest researcher at the Frith Lab, University of Tokyo.
2025.12 - Published preprint “Large generative mRNA language foundation model for efficient coding sequence generation and design with mRNA-GPT” on bioRxiv.
2025.10 - Published preprint “Gradient-based Optimization for mRNA Sequence Design” on bioRxiv.

Professional Experience

2026.4 - Present - Researcher, Sato Laboratory for Biomedical Data Science, Institute of Science Tokyo.
2026.4 - Present - Guest Researcher, Frith Lab, University of Tokyo.
2023.5 - 2026.3 - Postdoctoral Researcher, University of Tokyo.
2022.10 - Joined HAOMO.AI as a machine learning engineer for the “蓝色空间领航者” project.
2022.4 - Appointed as a researcher at the University of Tokyo (Details).
2022.3 - Received Ph.D. from the University of Tsukuba.

Publications

2026.05 - Published “Targeted Tests for LLM Reasoning: An Audit-Constrained Protocol” as an arXiv preprint. arXiv:2605.11599 [cs.LG]
2026.05 - Published “Separating Shortcut Transition from Cross-Family OOD Failure in a Minimal Model” as an arXiv preprint. arXiv:2605.12945 [cs.LG]
2026.05 - Published “A Controlled Counterexample to Strong Proxy-Based Explanations of OOD Performance: in a Fixed Pretraining-and-Probing Setup” as an arXiv preprint. arXiv:2605.11554 [cs.LG]
2026.05 - Published “FastUMAP: Scalable Dimensionality Reduction via Bipartite Landmark Sampling” as an arXiv preprint. arXiv:2605.11428 [cs.LG]
2025.12 - Co-authored “Large generative mRNA language foundation model for efficient coding sequence generation and design with mRNA-GPT” in bioRxiv. Link
2025.10 - Published “Gradient-based Optimization for mRNA Sequence Design” in bioRxiv. Link, Code
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” in Engineering Geology. Link
2023.1 - Published “LSEC: Large-scale spectral ensemble clustering” in Intelligent Data Analysis. arXiv, Code
2022.7 - Published “Missing Value Imputation With Low-Rank Matrix Completion in Single-Cell RNA-Seq Data by Considering Cell Heterogeneity” in Frontiers in Genetics. Code
2022.6 - “Divide-and-conquer based Large-Scale Spectral Clustering” accepted by Neurocomputing. arXiv, Code
2020.11 - “Ensemble Learning for Spectral Clustering” published in ICDM 2020. PDF, Code
2020.11 - “Hubness-based Sampling Method for Nyström Spectral Clustering” published in IJCNN 2020. Link
2020.2 - “An Oversampling Framework for Imbalanced Classification Based on Laplacian Eigenmaps” published in Neurocomputing. Link
2019.8 - “Distributed Collaborative Feature Selection Based on Intermediate Representation” published in IJCAI 2019. Link
2019.8 - “Large Scale Spectral Clustering Using Sparse Representation Based on Hubness” published in CBDCom 2018. Link, Code

Funding

Research Grants

2026.06 - Google Cloud TPU Builders Award - USD 5,500 in GCP credits for TPU-based AI and scientific workflow experiments
2026.04 - KAKENHI Grant-in-Aid for Early-Career Scientists: Development of an Input Data Differentiable Integrated Framework (ID3) for Biomolecular Sequence Design - ¥4,550,000 (Project Info)
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)

Conferences

Conference Activities

2025.11 - Poster - Presented a poster at CREST バイオDX領域 2025年度中間報告シンポジウム (Poster)
2025.8 - Oral - Presented on Input Data Differentiable Designer at RNA Informatics Dojo 2025
2024.10 - Poster - Presented a poster on the FastUMAP model at APBJC24
2024.8 - Oral - Gave a talk on the RNA-language model at RNA Informatics Dojo 2024
2024.8 - Participation - Participated in IJCAI 24 in Jeju
2024.6 - Participation - Participated in WCCI 24 in Yokohama
2024.6 - Participation - Participated in 25th Annual Meeting of the RNA Society of Japan at the University of Tokyo
2023.8 - Oral - Presented on RNA-language pre-training at RNA Informatics Dojo 2023
2022.8 - Oral - Talk on fast dimensionality reduction for RNA at RNA Informatics Dojo 2022
2022.3 - Oral - “LSEC: Large-scale spectral ensemble clustering” at SPRING Fellowship
2021.12 - Oral - “A novel large scale spectral clustering” at CollaboTICS 2021
2019.6 - Poster - “Large Scale Spectral Clustering Using Landmarks Based on Hubness” at the G20 Digital Science Now Symposium (Poster)

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

Awards

2026.06 - Google Cloud TPU Builders Award - USD 5,500 in GCP credits
2020.10 - Second prize, AETA Earthquake Prediction AI Algorithm Competition 2019 (News, Slide)
2019.10 - Special award, 3rd Analysys International Algorithm Competition - PV, UV Prediction Competition
2019.8 - Best Paper Award at CBDCom 2018 for “Large Scale Spectral Clustering Using Sparse Representation Based on Hubness”