促进生物神经网络和人工神经网络的交叉融合，通过人工智能加速脑研究的科学发现。利用人工智能算法采集，处理，分析，理解大规模高维多模态的脑数据，在神经元以及其连接突触的单细胞尺度级别上理解大脑编码，计算和学习的新机制。

• 在NeurIPS、ICLR、ICML、CVPR、AAAI、MICCAI、Cosyne等人工智能和计算神经的国际顶级会议上共发表15篇著作。其中包括第一作者/共同一作/通讯作者的著作共11篇，并获得美国技术并获得一项专利和多项荣誉, 包括EECS Rising Star, Shanahan Fellowship, Mathwork Fellowship, NlH award, Most Creative Applications of Al，并受邀在多个美国相关领域的学术研讨会进行口头报告。

• 科研成果被麻省理工学院新闻（MIT News）和福布斯采访报道。在神经科学研究中得到广泛关注和应用，被近年来多篇发表在Nature及其子刊的多篇神经科学重大科学成果的文章引用，还得到了哈佛大学、麻省理工学院、华盛顿大学、艾伦脑科学研究所等顶尖高校和科研机构的研究学者的积极合作。

1. Concept-Based Unsupervised Domain Adaptation.

Xinyue Xu, Yueying Hu, Hui Tang, Yi Qin, Lu Mi, Hao Wang, Xiaomeng Li.

Forty-second International Conference on Machine Learning (ICML 2025).

2. NetFormer: An Interpretable Model for Recovering Dynamical Connectivity in Neuronal Population Dynamics.

Ziyu Lu*, Wuwei Zhang*, Trung Le, Hao Wang, Uygar Sümbül, Eric Shea-Brown, Lu Mi.

The 13th International Conference on Learning Representations (ICLR 2025), Spotlight (Top 5.1%).

3. Active Learning of Two-Photon Holographic Stimulation for Identifying Neural Population Dynamics.

Andrew Wagenmaker*, Lu Mi*, Marton Rozsa, Matthew Storm Bull, Karel Svoboda, Kayvon Daie, Matthew D. Golub, Kevin Jamieson.

The 38th Conference on Neural Information Processing Systems (NeurIPS 2024).

4. Energy-Based Concept Bottleneck Models.

Xinyue Xu, Yi Qin, Lu Mi, Hao Wang, Xiaomeng Li.

The 12th International Conference on Learning Representations (ICLR 2024).

5. Learning Time-Invariant Representations for Individual Neurons from Population Dynamics.

Lu Mi*, Trung Le*, Tianxing He, Eli Shlizerman, Uygar Sümbül.

The 37th Conference on Neural Information Processing Systems (NeurIPS 2023).

6. Connectome-Constrained Latent Variable Model of Whole-Brain Neural Activity.

Lu Mi, Richard Xu, Sridhama Prakhya, Albert Lin, Nir Shavit, Aravinthan D.T. Samuel, Srinivas C. Turaga.

The 10th International Conference on Learning Representations (ICLR 2022).

7. Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate.

Lu Mi, Hao Wang, Yonglong Tian, Hao He, Nir Shavit.

The 36th Association for the Advancement of Artificial Intelligence (AAAI 2022).

8. HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps.

Lu Mi, Hang Zhao, Charlie Nash, Xiaohan Jin, Jiyang Gao, Chen Sun, Cordelia Schmid, Nir Shavit, Yuning Chai, Dragomir Anguelov.

The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2021 (CVPR 2021).

9. Learning Guided Electron Microscopy with Active Acquisition.

Lu Mi, Hao Wang, Yaron Meirovitch, Richard Schalek, Srinivas C. Turaga, Jeff W. Lichtman, Aravinthan D. T. Samuel, Nir Shavit.

The 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2020).

10 .Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics.

Yaron Meirovitch*, Lu Mi*, Hayk Saribekyan, Alexander Matveev, David Rolnick, Nir Shavit.

The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2019 (CVPR 2019).