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More awesome private repos will open source πŸ™ƒ
More awesome private repos will open source πŸ™ƒ


@conda-forge @PyPOTS
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πŸ€™ Contact info:

πŸ‘‹ Hi, I'm Wenjie Du (ζœζ–‡ζ° in Chinese). My research majors in modeling time series with machine learning, especially partially-observed time series (POTS), namely, incomplete time series with missing values, A.K.A. irregularly-sampled time series. I strongly advocate open-source and reproducible research, and I always devote myself to building my work into valuable real-world applications. Unix philosophy "Do one thing and do it well" is also my life philosophy, and I always strive to walk my talk. My research goal is to model this non-trivial and kaleidoscopic world with machine learning to make it a better place for everyone. It's my honor if my work could help you in any way.

πŸ€” POTS is ubiquitous in the real world and is vital to AI landing in the industry. However, it still lacks attention from academia and is also in short of a dedicated toolkit even in a community as vast as Python. Therefore, to facilitate our researchers and engineers' work related to POTS, I'm leading PyPOTS Research Team ( to build a comprehensive Python toolkit ecosystem for POTS modeling, including data preprocessing, neural net training, and benchmarking. Stars🌟 on our repos are also very welcome of course if you like what we're trying to achieve with PyPOTS.

πŸ’¬ I'm open to questions related to my research and always try my best to help others. I love questioning myself and I never stop. If you have questions for discussion or have interests in collaboration, please feel free to drop me an email or ping me on LinkedIn/WeChat/Slack (contact info is at the top) πŸ˜ƒ You can follow me on Google Scholar and GitHub to get notified of our latest publications and open-source projects. Note that I'm very glad to help review papers related to my research, but ONLY for open-source ones with readable code.

❀️ If you enjoy what I do, you can fund me and become a sponsor. And I assure you that every penny from sponsorships will be used to support impactful open-science research.

😊 Thank you for reading my profile. Feel free to contact me if you'd like to trigger discussions.

🏠 Visits number of profile visits


  1. PyPOTS PyPOTS Public

    A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputatio…

    Python 788 78

  2. SAITS SAITS Public

    The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-s…

    Python 283 48

  3. Awesome_Imputation Awesome_Imputation Public

    Awesome Deep Learning for Time-Series Imputation, including a must-read paper list about applying neural networks to impute incomplete time series containing NaN missing values/data

    Python 114 16

  4. TSDB TSDB Public

    Time Series Data Beans: a Python toolbox loads 170 public time-series datasets for machine learning/deep learning with a single line of code.

    Python 126 13

  5. PyGrinder PyGrinder Public

    PyGrinder grinds data beans into the incomplete by introducing missing values with different missing patterns.

    Python 24 4

  6. BrewPOTS BrewPOTS Public

    The tutorials for PyPOTS.

    Jupyter Notebook 44 4