Li Hongmin

I am a postdoctoral researcher at the Asai Laboratory at the University of Tokyo. My research focuses on spectral clustering, machine learning, and bioinformatics. Currently, I am developing machine learning algorithms for bioinformatics applications.

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

Recent Activities

• 2024.11 - Presenting a poster on the our project at 2024年度 CREST「データ駆動・AI駆動を中心としたデジタルトランスフォーメーションによる生命科学研究の革新」領域 中間報告シンポジウム.
• 2024.10 - Presenting a poster on the FastUMAP model at APBJC24.
• 2024.8 - Gave a talk on the RNA-language model at RNA Informatics Dojo 2023.
• 2024.8 - Participated in IJCAI 24 in Jeju 03.08.24 - 09.08.24.
• 2024.6 - Participated in WCCI 24 in Yokohama.
• 2024.6 - Participated in 25th Annual Meeting of the RNA Society of Japan at the Yasuda Auditorium, University of Tokyo.
• 2024.4 - Awarded a young researcher grant for developing a large-scale language model integrating RNA sequences and text (Project Info).

Professional Experience

• 2023.5 - Resumed postdoctoral research at the 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

• 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

Peer Review Activities

• 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

Presentations

• 2023.8 - Presented on RNA-language pre-training at RNA Informatics Dojo 2023.
• 2022.8 - Talk on fast dimensionality reduction for RNA at RNA Informatics Dojo 2022.
• 2022.03 - “LSEC: Large-scale spectral ensemble clustering,” presented at SPRING Fellowship.
• 2021.12 - Presented “A novel large scale spectral clustering” at CollaboTICS 2021.
• 2019.6 - Poster presentation on “Large Scale Spectral Clustering Using Landmarks Based on Hubness” at the G20 Digital Science Now Symposium. Poster

Awards

• 2021.10 - JST award for Pioneering Research Initiated by the Next Generation.
• 2020.10.18 - 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”