Jun Xia

Ph.D. Student
School of Engineering
Westlake University & Zhejiang University
Email: xiajun@westlake.edu.cn
Advisor: Stan Z. Li (IEEE Fellow)

Profile

Hi there! I am Jun Xia, a Ph.D. student at Westlake University and Zhejiang University, advised by Chair Prof. Stan Z. Li. Before Joining Westlake, I received my B.E. degree with honors from Central South University. My primary research interests lie in the intersection of Machine Learning and Computational Biochemistry, supported by the Fundamental Research Project for Young Ph.D. students from NSFC (首批国家自然科学基金青年学生基础研究项目(博士生)) and CIE-Tencent Doctoral Research Incentive Project (首届中国电子学会—腾讯博士生科研激励计划(混元大模型专项)).

🌟 Hiring: We are actively seeking funded visiting students, research assistant and self-motivated Ph.D. students. Since July 2022, almost every visiting student who worked with me has published papers at top conferences such as ICML, NeurIPS, and ICLR during their visit. If you are interested, please don't hesitate to contact me via Email.

News

  • (2024.10) A paper on computational proteomics is accepted in AIDrugX@NeurIPS 2024 as Spotlight.
  • (2024.10) Seven papers on computational biology and GNNs are accepted in NeurIPS 2024.
  • (2024.07) Invited to visit Prof. Fabian Theis's group at TUM & Helmholtz and Prof. Matthias Mann's group at Max Planck Institute of Biochemistry, Germany.
  • (2024.06) Invited review for Nature Communications.
  • (2024.05) Invited to deliver a talk on "Foundation Models for Biochemistry" at VALSE student seminar.
  • (2024.05) Two papers on drug discovery are accepted in ICML 2024.
  • (2024.04) Two papers on drug discovery are accepted in IJCAI 2024.
  • (2024.01) One paper on protein design is accepted in ICLR 2024.
  • (2023.12) Selected as the Rising Stars in AI 2024 organized by KAUST AI Initiative.
  • (2023.12) One paper on antibody design is accepted in AAAI 2024
  • (2023.12) One paper on graph pre-training is accepted in ICDE 2024
  • (2023.10) Invited to diliver a talk on AIDD @ Fudan University.
  • (2023.10) I am awarded NeurIPS Scholar Award & Suwu Scholarship.
  • (2023.09) One paper on molecular property prediction is accepted in NeurIPS 2023.
  • (2023.09) Invited to review for ICLR 2024, ACM TKDD, SDM 2024 and IEEE TIP.
  • (2023.08) Two papers on graph clustering are accepted at ACM MM 2023.
  • (2023.06) One paper on label-noise learning on graph data is accepted in TKDE 2023.
  • (2023.06) One paper on Chemical Pre-trained Models is accepted in ECML 2023.
  • (2023.04) Invited to diliver a talk @ Canada MILA Lab (Led by Turing Awardee Yoshua Bengio).
  • (2023.04) One paper on graph clustering is accepted in ICML 2023.
  • (2023.04) Our survey on Chemical Language Models has been accepted in IJCAI 2023 survey track.
  • (2023.02) Our SimGRACE (WWW 2022) paper is featured as Most Influential WWW Papers by Paper Digest.
  • (2023.02) Two papers (One oral presentation) are accepted in CVPR 2023.
  • (2023.01) I am a winner of Westlake Presidential Awards, the highest honor for Westlake students.
  • (2023.01) One paper on molecular pre-trained models is accepted in ICLR 2023.
  • (2022.10) I am awarded National Scholarship.
  • (2022.05) One paper on graph contrastive learning is accepted in ICML 2022.
  • (2022.01) One paper on word embeddings is accepted in ACL 2022.
  • (2022.01) One paper on graph pre-training is accepted in WWW 2022.

Invited Talks

  • 2024/07: Talk on "UltraProt: Diciphering Proteomic Mass Spectra by Scaling Transfer Learning to 137 Million Peptide Spectrum Matches" @ Max Planck Institute of Biochemistry, Germany.
  • 2024/02: Talk on "Discipher Biochemical Codes with Pre-learned Potentials" @ KAUST AI Rising Stars Symposium, Saudi Arabia.
  • 2023/10: Talk on "Accelerating Drug Discovery with Pre-learned Potential" @ Fudan University. [slides]
  • 2023/05: Talk on "Chemical Pre-trained Models: Retrospect & Prospect" @ Chinese Genomics Meet-up online [Vedio].
  • 2023/03: Talk on "Accelerating Drug Discovery with Pre-learned Potential" @ Alibaba Group.
  • 2022/11: Talk on graph contrastive learning @ Aminer seminer (Online) [Vedio].
  • 2022/03: Talk on hard negative mining in GCL @ LoGs seminer (Online)[Vedio].
  • 2022/08: Talk on pre-training GNNs @ Chungbuk National University, South Korea (Online)[Slides (Stay tuned)].
  • 2022/06: Talk on hard negative mining @ Prof. Yue Zhang's group in Westlake University.
  • 2022/05: Talk on graph contrastive learning @ Hangzhou Normal University (Online).

Selected Publications

Discover the full list | google scholar | semantic scholar | dblp.
[AI for Biochemistry]
  • Bridging the Gap between Database Search and De Novo Peptide Sequencing with SearchNovo
    Jun Xia, Sizhe Liu, Jingbo Zhou, Shaorong Chen, Stan Z.Li.
    AIDrugX@NeurIPS 2024 (Spotlight)
    [PDF] [Bib] [Code]
  • AdaNovo: Towards Robust De Novo Peptide Sequencing in Proteomics against Data Biases
    Jun Xia, Shaorong Chen, Jingbo Zhou, Tianze Ling, Stan Z.Li.
    NeurIPS 2024
    [PDF] [Bib] [Code]
  • Understanding the Limitations of Deep Models for Molecular property prediction: Insights and Solutions
    Jun Xia, Lecheng Zhang, Xiao Zhu, Stan Z.Li.
    NeurIPS 2023; ICML 2023 Computational Biology Workshop (Spotlight Talk)
    [PDF] [Bib] [Code]
  • Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules
    Jun Xia, Chengshuai Zhao, Bozhen Hu, Zhangyang Gao, Cheng Tan, Yue Liu, Siyuan Li, Stan Z.Li.
    ICLR 2023
    [PDF] [Bib] [Code]
  • A Systematic Survey of Chemical Pre-trained Models
    Jun Xia, Yanqiao Zhu, Yuanqi Du, Stan Z.Li.
    IJCAI 2023 Survey Track
    [PDF] [Bib] [Code]
  • FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning
    Sizhe Liu, Jun Xia, Lecheng Zhang, Yuchen Liu, Stan Z.Li.
    NeurIPS 2024 Datasets and Benchmarks Track
    [PDF] [Bib] [Code]
  • NovoBench: Benchmarking Deep Learning-based \emph{De Novo} Sequencing Methods in Proteomics
    Jingbo Zhou, Shaorong Chen, Jun Xia, Sizhe Liu, Tianze Ling, Stan Z.Li.
    NeurIPS 2024 Datasets and Benchmarks Track
    [PDF] [Bib] [Code]
  • KW-Design: Pushing the Limit of Protein Deign via Knowledge Refinement
    Zhangyang Gao, Cheng Tan, Xingran Chen, Yijie Zhang, Jun Xia, Siyuan Li, Stan Z.Li.
    ICLR 2024
    [PDF] [Bib] [Code]
  • CoSP: Co-supervised pretraining of pocket and ligand
    Zhangyang Gao, Cheng Tan, Jun Xia, Stan Z.Li.
    ECML 2023
    [PDF] [Bib] [Code]
[Graph Machine Learning]
  • SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation
    Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, Stan Z.Li.
    WWW 2022, Most Influential WWW Papers according to Paper Digest
    [
    PDF] [Bib] [Code]
  • ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning
    Jun Xia, Lirong Wu, Ge Wang, Jintao Chen, Stan Z.Li.
    ICML 2022, Spotlight
    [PDF] [Bib] [Code]
  • GNN Cleaner: Label Cleaner for Graph-structured Data
    Jun Xia, Haitao Lin, Yongjie Xu, Cheng Tan, Lirong Wu, Bozhen Hu, Siyuan Li, Stan Z.Li.
    TKDE 2023
    [PDF] [Bib] [Code]
  • DiscoGNN: A Sample-Efficient Framework for Graph Self-supervised Representation Learning
    Jun Xia, Shaorong Chen, Yue Liu, Zhangyang Gao, Jiangbing Zheng, Xihong Yang, Stan Z.Li.
    ICDE 2024
    [PDF] [Bib] [Code]
  • Dink-Net: Neural Clustering on Large Graphs
    Yue Liu, Ke Liang, Jun Xia, Sihang Zhou, Xihong Yang, Xingwang Liu, Stan Z.Li.
    ICML 2023
    [PDF] [Bib] [Code]
  • GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction
    Lirong Wu, Jun Xia, Haitao Lin, Zhangyang Gao, Cheng Tan, Stan Z.Li.
    ECML 2023
    [PDF] [Bib] [Code]
[Others]
  • Co-learning: Learning from Noisy Labels with Self-supervision
    Cheng Tan, Jun Xia, Lirong Wu, Stan Z.Li.
    ACM MM 2021, Oral Presentation
    [PDF] [Bib] [Code]
  • CVT-SLR: Contrastive Visual-Textual Transformation for Sign Language Recognition with Variational Alignment
    Jiangbin Zheng, Yile Wang, Cheng Tan, Siyuan Li, Ge Wang, Jun Xia, Siyuan Li, Stan Z.Li.
    CVPR 2023, Highlight (Oral) Presentation
    [PDF] [Bib] [Code]

Awards & Honors

  • 2024: CIE-Tencent Doctoral Research Incentive Project((首届)中国电子学会—腾讯博士生科研激励计划).

  • 2024: Fundamental Research Project for Young Ph.D. students from NSFC(首批国家自然科学基金青年学生基础研究项目(博士生)).

  • 2024: DAAD AINet Fellowship.

  • 2023: Rising Star in AI (awarded by KAUST AI Initiative headed by Prof. Jürgen Schmidhuber)

  • 2023: Apple AI/ML Scholar Finalist

  • 2023: NeurIPS Scholar Award.

  • 2023: Suwu Scholarship.

  • 2023: Westlake Presidential Awards (The highest honor at Westlake Univ.).

  • 2022: National Scholarship.

  • 2022: ICML 2022 Participation Grant.

  • 2021: Outstanding Student Cadre, Zhejiang University.

  • 2021: Outstanding Student, Zhejiang University.

  • 2020: Outstanding Graduate of Central South University.

  • 2019: The Interdisciplinary Contest in Modeling (ICM) of America, Meritorious Winner (The First Prize).

  • 2018: World Robot Competition in World Robot Conference (WRC) 2018, The Third Prize.

  • 2017: National Scholarship.

  • 2017, 2018 & 2019: The First Class Scholarship, Central South University.

  • 2017, 2018 & 2019: Outstanding Student, Central South University.

Academic Service

  • Program Committee Member:
    • Conferences: ICLR, ICML, NeurIPS, CVPR, KDD, ACL, SDM, ECML, ICASSP, etc.

  • Journal Reviewer: Nature Communications, IEEE TIP, ACM TKDD, IEEE TNNLS, Neural Networks, etc.

Co-working Students

(* I am working with some visiting/undergraduate students at Westlake with Chair Prof. Stan Z. Li)