About-CN

中文Bio:

张文涛，北京大学国际机器学习研究中心助理教授、研究员、博士生导师，研究兴趣为数据驱动的机器学习、图机器学习、机器学习系统和AI4Science。 他近5年在机器学习（ICML, NeurIPS, ICLR）、数据挖掘（KDD, WWW）和数据库（SIGMOD, VLDB, ICDE）等领域发表论文40余篇，并获得多个最佳论文奖（如第一作者获WWW’22 Best Student Paper Award 和 通讯作者获APWeb-WAIM’23 Best Paper Runner Up Award）。他领导或参与开源了多个机器学习系统，如大规模图学习系统SGL、分布式机器学习系统Angel、和黑盒优化系统OpenBox。他曾获2021年度亚太地区唯一的Apple Scholar、2022世界人工智能大会云帆奖等多项荣誉。

邮箱: wentao.zhang@pku.edu.cn

Wechat (微信): z1299799152

办公地点: 北京大学静园6号院217室

2023年秋季入学国际机器学习研究中心的博士轮转名额还有2个空缺！

2024年秋季入学的申请考核制博士、硕士（北大数院大数据硕士项目）名额还有空缺，长期招收实习生（可远程校外实习），如感兴趣请直接联系我！

Research Interests

• Data-centric ML: annotation, augmentation, imbalance, noise, distillation, out-of-distribution, heterogeneity, and privacy.

• Graph ML: graph neural network, graph representation learning.

• ML Systems: large-scale distributed training, AutoML, and data-centric ML platform.

• Interdisciplinary Application: AI4Science (e.g., drug and protein), and AI4Industry (e.g.,recommender system and anomaly detection).

A summary of my recent works:

• Data-centric ML on graph: how to prepare high quality and quantity data for graph ML?
  
  • Data annotation
    • Better efficiency [ALG, SIGMOD 21]
    • Model free [Grain, VLDB 21]
    • Noise handling[RIM, NeurIPS 21, Spotlight]
    • Simplifying the labeling task [IGP, ICLR 22]
  • Feature engineering (Complex model –> better features + simple model)
    • Feature/label smoothing + simple model [NDLS, NeurIPS 21, Spotlight]
    • Unsupervised and non-parametric feature smoothing [NAFS, ICML 22]
    • Graph-based MLP deployed at Tencent [GAMLP, KDD 22]
    • Inference at large scale [NAI, ICDE 24]
    • Experimental evaluation [AIR, KDD 22]
  • Data distillation
    • Offline distillation [RDD, SIGMOD 20]
    • Online distillation [ROD, KDD 21]
• ML Systems: how to make machine learning faster and easier?
  
  • Distributed ML & AutoML
    • Distributed NAS on graph [PasCa, WWW 22, Best Student Paper Award]
    • Deep and flexible NAS on graph [DF-GNAS, ICML 22]
    • Scalable graph learning [SGL]
    • Distributed graph learning [Angel Graph]
    • End-to-End AutoML [MindWare, VLDB 21]
    • Black box optimization [OpenBox, KDD 21]
    • Large-scale hyper-parameter tuning [Hyper-Tune, VLDB 22]
    • Distributed GNN training[The First Survey of Distributed GNN Training, Arxiv 22]
• Interdisciplinary Application: how to use machine learning in real applications?
  • AI4Industry
    • GNN-based recommendation [The First Survey of GNN-based RS, CSUR 22]
    • GNN-based recommendation system deployed at Taobao [Zoomer, ICDE 22]
  • AI4Science
    • Diffusion models [The First Survey of Diffusion Models, CSUR 23]
    • AutoML for biology [AutoDC, Bioinformatics 22]

What's New

• 2023-10: One paper is accepted by ICDE 2024.
• 2023-10: 🏆 We win the Best Paper Runner Up Award in APWeb-WAIM 2023.
• 2023-09: One paper is accepted by ACM Computing Survey 2023.
• 2023-09: One paper is accepted by NeurIPS 2023.
• 2023-08: One paper is accepted by VLDB 2024.
• 2023-08: One paper is accepted by APWEB-WAIM 2023.
• 2023-08: One paper is accepted by CIKM 2023.
• 2023-08: Our book about Diffusion Model is now avaliable.
• 2023-05: One paper is accepted by TKDE 2023.
• 2023-05: One paper is accepted by SIGKDD 2023.
• 2023-05: One paper is accepted by VLDB 2023.
• 2023-03: One paper is accepted by SIGMOD 2023.
• 2022-11: One paper is accepted by AAAI 2023.
• 2022-11: One paper is accepted by ICDE 2023.
• 2022-10: One paper is accepted by VLDBJ 2022.
• 2022-09: One paper is accepted by NeurIPS 2022.
• 2022-09: I am awared Rising Star (云帆奖-明日之星) in World AI Conference, 2022.
• 2022-06: I am honor to present the valedictorian for the class of 2022 in CS of PKU.
• 2022-06: I receive my Ph.D. degree in computer science from Peking University with Outstanding Doctoral Dissertation Award.
• 2022-05: One paper is accepted by the journal VLDBJ 2022.
• 2022-05: Four papers are accepted by the conference SIGKDD 2022.
• 2022-05: Two papers as first author, have been accepted by ICML 2022.
• 2022-05: One paper related to AutoML, has been accepted by Bioinformatics 2022.
• 2022-04: 🏆 We win the Best Student Paper Award in WWW 2022 !
• 2022-04: We release our first version of the scalable graph learning toolkit–SGL.
• 2022-03: One paper is selected as the Best Paper Award Nominees in WWW 2022. The corresponding PasCa system (integrated into SGL) will be open source next month!
• 2022-03: One paper as corresponding author, related to GNN-based Recommendation, has been accepted by the journal ACM Computing Survey 2022 .
• 2022-01: One paper related to graph-based recommendation, has been accepted by the conference ICDE 2022 .
• 2022-01: One paper as first author, related to graph data annotation, has been accepted by the conference ICLR 2022 .
• 2022-01: One paper related to our large scale Hyper-paramater Tuning system, has been accepted by the conference VLDB 2022 .
• 2022-01: I accepted the invitation to serve as Program Committee member of the Research Track of ACM SIGKDD 2022.
• 2022-01: One paper as first author, related to our scalable graph NAS system, has been accepted by the conference WWW 2022 .
• 2021-12: Our OpenBox team won the “Outstanding Winner” at the openGCC contest in CCF ChinaSoft 2021. Congratulations!
• 2021-09: Two papers as first author, related to scalable graph learning and graph data annotation, have been accepted by the conference NeurIPS 2021 with Spotlight (< 3%).
• 2021-08: We propose GAMLP, a scalable and efficient graph model, which achieves the top #1 performance in three public and largest ogbn graphs (i.e., ogbn-papers100M, ogbn-products, and ogbn-mag)! See the leaderboards here.
• 2021-07: One paper as first author, related to large-scale graph data selection, has been accepted by the conference VLDB 2021.
• 2021-07: One paper as co-first author, related to deep GNN, has been accepted by the journal TKDE 2021.
• 2021-06: One paper as third author, related to our AutoML system – VocalnoML, has been accepted by the conference VLDB 2021.
• 2021-05: Three papers, related to sparse graph, graph decomposition and our blackbox optimization (BBO) system – OpenBox, are accepted by the conference SIGKDD 2021.
• 2021-03: As the only person in China, I was supported by the Apple Scholars in AI/ML PhD fellowship. Many thanks to Apple!
• 2021-03: One paper as first author has been accepted by the conference SIGMOD 2021. Looking forward to the meeting in Xi’an this summer!

Contributed Open-source Projects

• Angel: a high-performance distributed machine learning and graph computing platform, jointly designed by Tencent and PKU.

• SGL: a scalable graph learning toolkit for extremely large graph datasets.

• MindWare: a powerful AutoML system, which automates feature engineering, algorithm selection and hyperparameter tuning.

• OpenBox: an efficient open-source system designed for solving generalized black-box optimization (BBO) problems.

Selected Awards

1. 🏆 Best Paper Runner Up Award, APWeb-WAIM 2023.
2. Rising Star (云帆奖-明日之星), World AI Conference, 2022.
3. 🏆 Best Student Paper Award of WWW 2022 (1/1822, the second WWW Best Student Paper from China), 2022
4. IVADO Postdoctoral Fellowship, Canada
5. Outstanding Doctoral Dissertation Award, Peking University (Sole winner in Computer Software and Theory), 2022
6. Outstanding Graduate of Beijing, China, 2022
7. Candidate of May 4th Medal (Each School recommends 1 candidate, highest honor in PKU), 2022
8. The Big Data Expo Leading Technology Achievement Award, China International Big Data Industry Expo (Angel Graph project), 2022
9. Candidate of People of the Year (1 people in EECS, and 42 people in PKU), 2021
10. Merit Student of Beijing (2 people in EECS, and 58 people in PKU), 2021
11. Apple PhD Fellowship (1 people in China, and 15 people in the world), 2021
12. National Scholarship (Top 1% in PKU), 2019, 2021
13. Baidu Scholarship Nominee (20 people in the world), 2021

Selected Competitions

1. Outstanding Winner of the openGCC contest in CCF ChinaSoft (1/3814), 2021
2. Rank #1 in Open Graph Benchmark, 2021
3. Outstanding Winner of the BDIC Big Data Competition (1/575), 2018

Selected Program Committee Member and Reviewer

• Database and Data Management:
  • ICDE 2023
  • DASFFA 2022
  • VLDBJ 2022, 2023
• Machine Learning:
  • ICML 2021, 2022, 2023
  • NeurIPS 2022, 2023
  • ICLR 2024
  • JMLR 2023
  • Machine Learning 2023
  • LoG 2024
• Data Mining:
  • SIGKDD 2021, 2022, 2023
  • SDM 2024
  • WWW 2022
  • DASFFA 2022, 2023, 2024
  • IEEE TKDE 2022
  • IEEE TNNLS 2022
  • PAKDD 2023, 2024
• Others:
  • ICCV 2023
  • CVPR 2023, 2024
  • SCIS 2023

Invited Talks

I am happy to give a talk if you are interested in my work. 😊

1. Model Degradation Hinders Deep Graph Neural Networks.
   KDD’22, 2022. 08
   
2. Graph Attention Multi-Layer Perceptron.
   KDD’22, 2022. 08
   
3. NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning.
   AI Time [News]
   ICML’22, Virtual, 2022. 07
   Jiqizhixin, Virtual, 2022. 07 [News][Slides]
   
4. Deep and Flexible Graph Neural Architecture Search.
   ICML’22, Virtual, 2022. 07
   Jiqizhixin, Virtual, 2022. 07
   
5. Towards Large Scale Graph Learning: Data, Model and System.《大规模图学习：数据、模型与系统》
   THU, Virtual, 2023.02
   PKU, Virtual, 2023.02
   SUSTech, 2023.01
   HKUST (Guang Zhou), Virtual, 2022.04 [News]
   Stanford, Virtual, 2021.11
   Mila, Virtual, 2021.9
   
6. Towards Automated Graph Learning. 《自动化图机器学习》 [Doc]
   HKUST, Virtual, 2022.11[News]
   NUDT, Virtual, 2022. 07
   HUST, 2022. 08
   Zhejiang University, 2022. 08
   
7. Information gain propagation a new way to graph active learning with soft labels. 《软标签场景下的图主动学习》
   AI Time, Virtual, 2022. 06 [News]
   ICLR’22, Virtual, 2022. 04
   
8. Data-centric ML on Graph.
   UvA, 2022. 04
   PKU, 2023.05
   HKUST, 2023.04
   
9. Towards Data-Centric ML.《数据驱动的机器学习》
   Apple research, 2022. 06
   RUC, 2023.06
   SEU, 2023.07
   PKU, 2023.08
   
10. valedictorian Speech.《北京大学计算机系2022级毕业生代表致辞》
    CS of PKU, 2022. 06 [News]
    
11. PaSca: a graph neural architecture search system under the scalable paradigm. 《可扩展性的图神经结构搜索系统》
    DGL Team, Amazon, Virtual, 2022.07
    CSU, Virtual, 2022. 07
    CCF, Virtual, 2022.06 [News] [Slides]
    DataFun, Virtual, 2022.06 [Slides]
    MLNLP, Virtual, 2022.06 [News][Slides][Video]
    InfoQ, Tencent Cloud, Virtual, 2022.06 [News]
    WWW’22, Virtual, 2022.04 [Slides]
    Data Platform, Tencent, Virtual, 2022.05
    
12. Towards Large-scale Graph Machine Learning. 《大规模图机器学习》 [Doc]
    HKUST, Virtual, 2022. 08 (In Preparing)
    LOGs, Virtual, 2022. 07 [Video]
    

13. How to Do Research? 《浅谈科研》
    Apple Research, Virtual, 2021.12
    PKU, Virtual, 2021.12 [News-1, News-2] [Slides]
    

14. The Scalability of Large-scale Graph Machine Learning.《大规模图机器学习的可扩展性》
    Tencent Big Data, Virtual, 2022.04
    NeurIPS, Virtual, 2021.12
    4Paradigm, Virtual, 2021.12
    AI Drive, 2021.12 [Video] [News] [Slides]
    
15. RIM: Reliable Influence-based Active Learning on Graphs.
    NeurIPS, Virtual, 2021.12
    NeurIPS MeetUp China, 2021.12 [News] [Slides]
    

16. A survey of GNN system.《GNN系统调研》
    Tencent, Virtual, 2021.12 [Slides]
    

17. Graph Attention Multi-Layer Perceptron.《图注意力多层感知器》
    DataFun, Virtual, 2021.10 [News] [Slides]