WenjieDu · WenjieDu · May 5, 2026 · Mar 1, 2026 · Mar 1, 2026 · Mar 6, 2026
diff --git a/README.md b/README.md
@@ -100,8 +100,8 @@ partially-observed time series with missing values. The table below shows the av
 corresponding task (note that models will be continuously updated in the future to handle tasks that are not
 currently supported. Stay tuned❗️).
 
-🌟 Since **v0.2**, all neural-network models in PyPOTS has got hyperparameter-optimization support.
-This functionality is implemented with the [Optuna](https://github.com/optuna/optuna) framework. You may want to
+🌟 Since **v0.2**, all neural-network models in PyPOTS has got hyperparameter-optimization support by [Microsoft NNI](https://github.com/microsoft/nni) until **v2.0**.
+In PyPOTS v2, this functionality is reimplemented with the [Optuna](https://github.com/optuna/optuna) framework. You may want to
 refer to our time-series imputation survey and benchmark
 repo [Awesome_Imputation](https://github.com/WenjieDu/Awesome_Imputation)
 to see how to config and tune the hyperparameters.
@@ -138,7 +138,7 @@ The paper references and links are all listed at the bottom of this file.
 | Neural Net    | ModernTCN[^38]                                                                                                                                  |    ✅     |    ✅     |          |          |          | `2024 - ICLR`                                                                                            |
 | Neural Net    | ImputeFormer🧑‍🔧[^34]                                                                                                                          |    ✅     |          |          |          |    ✅     | `2024 - KDD`                                                                                             |
 | Neural Net    | TOTEM[^50]                                                                                                                                      |    ✅     |          |          |          |          | `2024 - TMLR`                                                                                            |
-| Neural Net    | TKAN[^54]                                                                                                                                       |    ✅     |          |          |          |          | `2024 - arXiv`                                                                                           |
+| Neural Net    | TKAN🧑‍🔧[^54]                                                                                                                                       |    ✅     |          |          |          |          | `2024 - arXiv`                                                                                           |
 | Neural Net    | SAITS[^1]                                                                                                                                       |    ✅     |          |    ✅     |          |    ✅     | `2023 - ESWA`                                                                                            |
 | LLM           | GPT4TS[^46]                                                                                                                                     |    ✅     |    ✅     |          |          |          | `2023 - NeurIPS`                                                                                         |
 | Neural Net    | FreTS🧑‍🔧[^23]                                                                                                                                 |    ✅     |          |          |          |          | `2023 - NeurIPS`                                                                                         |

diff --git a/README_zh.md b/README_zh.md
@@ -95,8 +95,8 @@ PyPOTS当前支持多变量POTS数据的插补, 预测, 分类, 聚类以及异
 符号`✅`表示该算法当前可用于相应的任务(注意, 目前模型尚不支持的任务在未来版本中可能会逐步添加, 敬请关注！).
 算法的参考文献以及论文链接在该文档底部可以找到.
 
-🌟 自**v0.2**版本开始, PyPOTS中所有神经网络模型都支持超参数调优. 该功能基于[Optuna](https://github.com/optuna/optuna)
-框架实现.
+🌟 自**v0.2**版本开始, PyPOTS中所有神经网络模型都由[Microsoft NNI](https://github.com/microsoft/nni)支持超参数调优, 直到**v2.0**. 
+从PyPOTS v2开始, 该功能基于[Optuna](https://github.com/optuna/optuna)框架重新实现.
 你可以通过参考我们的时间序列插补综述和基准评估项目的代码[Awesome_Imputation](https://github.com/WenjieDu/Awesome_Imputation)
 来了解如何使用PyPOTS调优模型的超参.
 
@@ -131,7 +131,7 @@ PyPOTS当前支持多变量POTS数据的插补, 预测, 分类, 聚类以及异
 | Neural Net    | ModernTCN[^38]                                                                                                                                  |    ✅     |    ✅     |          |          |          | `2024 - ICLR`                                                                                            |
 | Neural Net    | ImputeFormer🧑‍🔧[^34]                                                                                                                          |    ✅     |          |          |          |    ✅     | `2024 - KDD`                                                                                             |
 | Neural Net    | TOTEM[^50]                                                                                                                                      |    ✅     |          |          |          |          | `2024 - TMLR`                                                                                            |
-| Neural Net    | TKAN[^54]                                                                                                                                       |    ✅     |          |          |          |          | `2024 - arXiv`                                                                                           |
+| Neural Net    | TKAN🧑‍🔧[^54]                                                                                                                                       |    ✅     |          |          |          |          | `2024 - arXiv`                                                                                           |
 | Neural Net    | SAITS[^1]                                                                                                                                       |    ✅     |          |    ✅     |          |    ✅     | `2023 - ESWA`                                                                                            |
 | LLM           | GPT4TS[^46]                                                                                                                                     |    ✅     |    ✅     |          |          |          | `2023 - NeurIPS`                                                                                         |
 | Neural Net    | FreTS🧑‍🔧[^23]                                                                                                                                 |    ✅     |          |          |          |          | `2023 - NeurIPS`                                                                                         |
@@ -560,4 +560,4 @@ test recently ;-) Follow us, and stay tuned!
 *arXiv 2024*.
 [^55]: Zhang, F., Du, W., Zhang, H., Yu, K., & Qu, S. (2026).
 [HELIX: Hybrid Encoding with Learnable Identity and Cross-dimensional Synthesis for Time Series Imputation](https://openreview.net/forum?id=FN20iuPnEU).
-*ICML 2026*.
+*ICML 2026*.
diff --git a/docs/algo_table.rst b/docs/algo_table.rst
@@ -137,7 +137,7 @@
      -
      - ``2024 - TMLR``
    * - Neural Net
-     - TKAN :cite:`genet2024tkan`
+     - TKAN🧑‍🔧 :cite:`genet2024tkan`
      - ✅
      -
      -

diff --git a/docs/faq.rst b/docs/faq.rst
@@ -9,44 +9,8 @@ published for at least 1 year, have 20+ citations, and the usefulness to our use
 
 **However**, we encourage the authors of proposed new models to share and add your implementations into PyPOTS
 to help boost research accessibility and reproducibility in the field of POTS modeling.
-Note this exception only applies if you commit to the maintenance of your model for at least two years.
-
-
-Join PyPOTS
-^^^^^^^^^^^
-.. _becoming-a-volunteer:
-
-Becoming a Volunteer Developer
-""""""""""""""""""""""""""""""
-To become a member of PyPOTS volunteer development team, you should
-
-1. love open-source science and be active on GitHub;
-2. be familiar with the PyPOTS codebase and have made at least one pull request merged into branch ``main`` of PyPOTS,
-   which is not for fixing typos or improving the docs;
-3. watch PyPOTS repository to receive the latest news from it;
-4. join the `PyPOTS community on Slack <https://join.slack.com/t/pypots-org/shared_invite/zt-1gq6ufwsi-p0OZdW~e9UW_IA4_f1OfxA>`_
-   and become a member of the channel ``#dev-team``. ``#dev-team`` currently is a public channel, and you don't need an invitation to join it;
-5. commit to constantly maintain PyPOTS project and obey our development principles;
-
-Once you obtain the role, you'll be listed as a member on the ``About Us`` pages of
-`PyPOTS main site <https://pypots.com/about/>`_
-and
-`PyPOTS docs site <https://docs.pypots.com/en/latest/about_us.html>`_.
-
-Becoming a Lead
-"""""""""""""""
-To become a lead at PyPOTS, surely you have to already be a volunteer developer first, i.e. you've met all requirements in the section :ref:`becoming-a-volunteer`.
-Your research should be highly related to data mining/machine learning on POTS data, and
-you need to prove that you're capable of proposing a research plan solely and conducting it.
-You're willing to take developing PyPOTS as your responsibility and commit to constantly and regularly
-contribute you time and ideas to PyPOTS things (including community culture construction,
-code maintenance, current research implementation, new research planning).
-The lead is a permanent role unless your research is no longer related to the field of modeling POTS or
-you no longer want to get involved with affairs at PyPOTS.
-
-If you believe you want to do this, you can drop an email with anything you want to tell and your CV attachment to
-`team@pypots.com <mailto:team@pypots.com>`_. We will schedule a meeting for you and all other members at PyPOTS for further discussion.
-This is absolutely not a so-called interview, please don't take it formal and we just would like to listen to your thoughts about the field of POTS ;-)
+Note this exception only applies if you commit to the maintenance of your model for at least two years,
+and you should contact `Wenjie Du <mailto:wdu@time-series.ai>`_ to discuss the details of your implementation and maintenance plan before coding start.
 
 
 Our Development Principles

diff --git a/docs/index.rst b/docs/index.rst
@@ -123,8 +123,8 @@ time series with missing values. The table below shows the availability of each
 The symbol ✅ indicates the algorithm is available for the corresponding task (note that models will be continuously updated
 in the future to handle tasks that are not currently supported. Stay tuned❗️).
 
-🌟 Since **v0.2**, all neural-network models in PyPOTS has got hyperparameter-optimization support.
-This functionality is implemented with the `Optuna <https://github.com/optuna/optuna>`_ framework. You may want to refer to our time-series
+🌟 Since **v0.2**, all neural-network models in PyPOTS has got hyperparameter-optimization support by `Microsoft NNI <https://github.com/microsoft/nni>`_ until **v2.0**.
+In PyPOTS v2, this functionality is reimplemented with the `Optuna <https://github.com/optuna/optuna>`_ framework. You may want to refer to our time-series
 imputation survey repo `Awesome_Imputation <https://github.com/WenjieDu/Awesome_Imputation>`_ to see how to config and
 tune the hyperparameters.
 

diff --git a/docs/milestones.rst b/docs/milestones.rst
@@ -38,16 +38,8 @@ Here is `an incomplete list of them <https://scholar.google.com/scholar?as_ylo=2
 Project Milestones
 ^^^^^^^^^^^^^^^^^^
 - 2022-03: `PyPOTS project <https://github.com/WenjieDu/PyPOTS>`_ is initiated;
-- 2022-04: PyPOTS v0.0.1 is released;
-- 2022-09: PyPOTS achieves its first 100 stars ⭐️ on GitHub;
-- 2023-03: PyPOTS is `published on Conda-Forge <https://anaconda.org/conda-forge/pypots>`_, and users can install it via Anaconda;
-- 2023-04: `PyPOTS website <https://pypots.com>`_ is launched, and PyPOTS achieves its first 10K downloads on PyPI;
-- 2023-05: PyPOTS v0.1 is released, and `the preprint paper <https://arxiv.org/abs/2305.18811>`_ is published on arXiv;
-- 2023-06: A short version of PyPOTS paper is accepted by the 9th SIGKDD International
-  Workshop on Mining and Learning from Time Series (`MiLeTS'23 <https://kdd-milets.github.io/milets2023/>`_);
-- 2023-07: PyPOTS has been accepted as a `PyTorch Ecosystem <https://landscape.pytorch.org/?item=modeling--specialized--pypots>`_ project;
-- 2023-12: PyPOTS achieves its first 500 stars 🌟;
-- 2024-02: PyPOTS Research releases its imputation survey paper `Deep Learning for Multivariate Time Series Imputation: A Survey <https://arxiv.org/abs/2402.04059>`_;
-- 2024-06: PyPOTS Research releases the 1st comprehensive time-series imputation benchmark paper `TSI-Bench: Benchmarking Time Series Imputation <https://arxiv.org/abs/2406.12747>`_;
-- 2024-07: PyPOTS achieves its first 300,000 downloads in total;
-- 2024-08: We present the keynote "Learning from Partially Observed Time Series: Towards Reality-Centric AI4TS" `IJCAI'24 AI4TS workshop <https://ai4ts.github.io/ijcai2024>`_;
+- 2023-06: `A short version of PyPOTS paper <https://arxiv.org/abs/2305.18811>`_ is accepted by `KDD'23 MiLeTS workshop <https://kdd-milets.github.io/milets2023/>`_);
+- 2023-07: PyPOTS has been selected as a `PyTorch Ecosystem <https://landscape.pytorch.org/?item=modeling--specialized--pypots>`_ project;
+- 2024-08: We present the keynote "Learning from Partially Observed Time Series: Towards Reality-Centric AI4TS" in `IJCAI'24 AI4TS workshop <https://ai4ts.github.io/ijcai2024>`_;
+- 2024-10: PyPOTS achieves its first 1K stars 🌟on GitHub;
+- 2025-06: PyPOTS hits its first 1M downloads in total;