Wei Fan is currently working as a Postdoctoral Researcher in the Medical Sciences Division at the University of Oxford, UK. His research focuses on data-centric AI, time series modeling, and spatial-temporal data mining. He is also dedicated to applying these methods to solve real-world data science applications, such as healthcare, transportation, and energy. Wei earned his Ph.D. degree in Computer Science from the University of Central Florida. He has interned at industry organizations such as Microsoft Research, Baidu Research, and Bytedance AI Lab.

Wei has published over 20 papers in leading machine learning and data mining journals (e.g., TKDE, TKDD) and conferences (e.g., ICLR, NeurIPS, AAAI, IJCAI). Two of his papers were selected as spotlight papers of ICLR. He has also co-organized Workshops at conferences such as ICDM and CIKM. Wei also serves as a PC member/reviewer for conferences and journals such as ICLR, NeurIPS, KDD, WWW, IJCAI, AAAI, MM, SDM, LOG, AISTATS, PRICAI, IEEE Bigdata, IEEE TKDE, IEEE TCSS, IEEE IoT, IEEE TBD, ACM TKDD, ACM TOIS, ACM TOMM, Scientific Reports.

Education

• Ph.D. in Computer Science, University of Central Florida, 2023
• B.E. in Computer Science, Beijing University of Posts and Telecommunications, 2020

Professional Experience

• Postdoctor Researcher, Medical Sciences Division, University of Oxford, 2024
• Visiting Scholar, University of Macau, 2023
• Research Intern, Vision Lab, Baidu Research, 2022
• Research Intern, Machine Learning Group, Microsoft Research Asia, 2021
• Research Intern, Business Intelligence Lab, Baidu Research, 2020
• Algorithm Intern, Speech Team, Bytedance AI Lab, 2019

Publication

I have published 20+ papers in prestigious journals and conferences, including data mining and machine learning venues (e.g., TKDE, TKDD, ICLR, NeurIPS, ICML, AAAI, IJCAI, ICDM, SDM, EMNLP, etc). Among them, I got two spotlight papers in ICLR 2022 and ICLR 2024 respectively. More details can be found at my Google Scholar. The representative papers can be categorized as follows:

• Deep Time Series Modeling
  • The Data Learning Perspective: ICLR 22, NeurIPS 23, NeurIPS 23, NeurIPS 24
  • The Data Manipulating Perspective: AAAI 23, IJCAI 24, IJCAI 24, Preprint.
• Automated Feature Engineering
  • Automated Feature Selection: ICDM 20, TKDE 21, SDM 21
  • Automated Instance and Feature Selection: IJCAI 22
• Deep Representation Learning
  • Tabular Representation Learning: ICLR 24
  • Graph Representation Learning: ICDM 21, EMNLP 22
• Spatial-temporal Data Mining and Interdisciplinary Applications
  • Urban Computing and Smart City Applications: ICDM 23, SDM 24, IJCAI 24
  • Energy Forecasting: TKDD 24
  • Medical Signal Classification: Preprint

Services

As a Program Committee member or Reviewer, I provide review comments to papers:

• International Conference on Learning Representations (ICLR), 2023, 2024, 2025
• The Conference on Neural Information Processing Systems (NeurIPS), 2023, 2024
• ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023, 2024, 2025
• ACM International World Wide Web Conference (WWW), 2024, 2025
• The AAAI Conference on Artificial Intelligence (AAAI), 2023, 2024, 2025
• The International Joint Conference on Artificial Intelligence (IJCAI), 2023, 2024
• ACM Annual Conference on Multimedia (MM), 2024
• ACM International Conference on Information and Knowledge Management (CIKM), 2023
• SIAM International Conference on Data Mining (SDM), 2024.
• IEEE International Conference on Big Data (IEEE BigData), 2024.
• International Joint Conference on Natural Language Processing (IJCNLP), 2022, 2023
• The Learning on Graphs Conference (LOG), 2022, 2023, 2024
• The Pacific Rim International Conference on Artificial Intelligence (PRICAI), 2024
• IEEE Transactions on Knowledge and Data Engineering (TKDE)
• IEEE Transactions on Computational Social Systems (TCSS)
• IEEE Internet of Things Journal (IoT)
• IEEE Transactions on Big Data (TBD)
• ACM Transactions on Knowledge Discovery from Data (TKDD)
• ACM Transactions on Information Systems (TOIS)
• ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)
• Nature Scientific Reports