Lu Mi   米璐

I am joining the College of AI at Tsinghua University as an Assistant Professor, starting from May 2025. I am looking for highly-motivated PhD students and interns to join Tsinghua University and Shanghai Qizhi Institute. Drop me an email if you are interested.

Before joining Tsinghua, I was an Assistant Professor at Gatech CSE from 2024 to 2025 and a Shanahan Foundation Fellow at the Allen Institute for Brain Science and University of Washington from 2022 to 2024. Prior to that, I received my PhD degree in Computer Science from MIT CSAIL in 2022, advised by Prof. Nir Shavit. Concurrently, I was a fellow in the Harvard Center for Brain Science from 2020 to 2022. I received a bachelor’s degree from Tsinghua University.

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My research lies in the intersection of natural intelligence and artificial intelligence. I am working on accelerating scientific discovery in the brain in the Age of AI, and bridging the gap between artificial and biological neural networks. You could check my research summary here.

My research is recognized with several honors, including the MathWorks Fellowship, Shanahan Foundation Fellowship, and an NIH award. I was selected as 2022 Rising Stars in EECS and our work has been featured in MIT News and Forbes.

I am broadly interested in
(1) AI for Neuroscience: Designing interpretable and generalized methods (including foundation models BrainGPT) for data-driven discovery in neuroscience, understanding the mechanisms of coding, computation, and learning within biological neural networks.
(2) Neuroscience for AI: Using mechanisms such as sparsity, plasiticity, and modularity discovered in the brain to develop brain-inspired AI frameworks.
(3) Brain Computer Interface: Developing computational algorithms that decode brain signals into behaviors, images and languages.

• [05/25] I started as an Assistant Professor at College of AI, Tsinghua University.
  
• [04/25] I gave a talk on Accelerating Scientific Discovery in the Brain in the Age of AI in the A3D3 Seminar at Caltech.
  
• [04/25] I gave a tutorial on NetFormer in the Workshop on Data-Driven and Large-Scale Modeling in Neuroscience at Gatech.
  
• [02/25] Our work on Interpretable NetFormer to Recover Dynamical Connectivity was accepted at ICLR 2025 as a Spotlight paper (top 5.1%).
  
• [02/25] I gave a talk on Accelerating Scientific Discovery in the Brain with Machine Learning at the TReNDS Center.
  
• [12/24] I gave a talk on Bridging the Gap Between the Structure and Function in the Age of AI on at Allen Institute.
  
• [12/24] Our work on Presynaptic Input Synchrony at Scale and Interpretable NetFormer to Recover Dynamical Connectivity was accepted at Cosyne 2025.
  
• [09/24] Our work on Active Input Design for Identifying Neural Dynamics was accepted at NeurIPS 2024.
  
• [01/24] I gave a talk on Identification of Cell Types and Connectivity from In Vivo Neural Activity Recordings in Brain Science Seminars at Allen Institute.
  
• [01/24] Our work on Energy-based Concept Bottleneck was accepted at ICLR 2024.
  
• [10/23] I gave a spotlight talk on Bridge the Gap between Biological and Artificial Neural Networks and received "Most Creative Application of AI" award at Michigan AI Symposium 2023 (Youtube Video).
  
• [10/23] Our work on NeuPRINT: Neuronal Time-Invariant Representations was selected as a Spotlight Talk at NeuroAI in Montreal 2023.
  
• [09/23] Our work on NeuPRINT: Neuronal Time-Invariant Representations was accepted at NeurIPS 2023.
  
• [09/23] Our work on Smart Microscope was reported on Forbes (Youtube Video).
  
• [03/23] The Shanahan Undergraduate and Postbac Fellowships are open to apply.
  
• [02/23] I gave a guest lecture on Biophysics VAEs in Deep Learning for Neuroscience course (CSE 599N) at University of Washington.
  
• [01/23] Our work on Xray2EM: Cross-Modality Image Reconstruction was accepted at ISBI 2023.
  
• [11/22] Our organized challenge on Xray Projectomic Reconstruction was accepted at ISBI 2023.
  
• [09/22] I gave a talk on How to Link Multi-Modal Neural Data with Deep Learning at NeuroAI in Seattle 2022.
  
• [09/22] I joined Allen Institute as Shanahan Foundation Fellow, and University of Washington as postdoctoral researcher.
• [08/22] Ph.D. Thesis defended!
• [08/22] I received the 2022 Shanahan Foundation Fellowship.
  
• [08/22] I was selected into 2022 Rising Stars in EECS.
  
• [04/22] I gave a talk on Deep Learning Tools for Next-Generation Connectomics at Allen Institute.
  
• [01/22] Our work on Connectome-Constrained Modeling was accepted at ICLR 2022.
  
• [01/22] I gave a talk on Connectome-Constrained Modeling in Computational Neuroscience Seminar at Flatiron Institute.
  
• [12/21] Our work on Training-Free Uncertainty Estimation was accepted at AAAI 2022.
  
• [06/21] I joined Google Research as a research intern.
  
• [06/21] I received the 2021 MathWorks Fellowship.
  
• [02/21] Our work on HD Maps Generation was accepted at CVPR 2021.
  
• [01/21] Our work on Connectome-Constrained Modeling was accepted at Cosyne 2021.
  
• [09/20] I received the NIH Awards at MICCAI 2020.
  
• [09/20] Our work on Learning-Guided Electron Microscope was accepted at MICCAI 2020.
  
• [02/19] Our work on Cross-Classification Clustering for Connectomics was accepted at CVPR 2019.
  

• Most Creative Application of AI, Michigan AI Symposium, 2023
  
• Shanahan Foundation Fellowship, Allen Institute & University of Washington, Seattle, 2022
  
• Rising Stars in EECS, 2022
  
• MathWorks Fellowship, MIT EECS, 2021
  
• NIH Awards, MICCAI, 2020
  
• Grass Instruments Co. Fellowship, MIT EECS, 2017
  
• Tang Lixin Fellowship, Tsinghua (30 in 3000), 2017
  
• Best Paper Awards, Optofluidics, 2016
  

• Talk on Accelerating Scientific Discovery in the Brain in the Age of AI, 2025, A3D3 Seminar, Caltech, 2025.
  
• Tutorial on Interpretable NetFormer to Recover Dynamical Connectivity, Workshop on Data-Driven and Large-Scale Modeling in Neuroscience, Gatech, 2025.
  
• Talk on Accelerating Scientific Discovery in the Brain with Machine Learning, TReNDS Center, 2025.
  
• Talk on Bridging the Gap Between the Structure and Function in the Age of AI, Allen Institute, 2024.
  
• Talk on Accelerating Scientific Discovery in the Brain with Machine Learning, Gatech CSE, 2024.
  
• Talk on Accelerating Scientific Discovery in the Brain with Machine Learning, UNC CS/DS, 2024.
  
• Guest Lecture on Transformers as Identity and Structure Learners for Neural Dynamics, Transformers in Neuroscience, CSE 5900, University of Washington, 2024.
  
• Talk on Identification of Cell Types and Connectivity from In Vivo Neural Activity Recordings, Brain Science Seminars, Allen Institute, 2024.
  
• Spotlight Talk on Bridge the Gap between Biological and Artificial Neural Networks, Michigan AI Symposium, 2023.
  
• Spotlight Talk on Neuronal Time-Invariant Representations, NeuroAI in Montreal, 2023.
  
• Guest lecture on Biophysics VAEs, Deep Learning for Neuroscience, CSE 599N, University of Washington, 2023.
  
• Talk on How to Link Multi-Modal Neural Data with Deep Learning, NeuroAI in Seattle, 2022.
  
• Talk on Deep Learning Tools for Next-Generation Connectomics, Allen Institute, 2022.
  
• Talk on Connectome-Constrained Modeling, Computational Neuroscience Seminar, Flatiron Institute, 2022.
  
• Talk on Connectome-Constrained Modeling, CVML meeting, HHMI Janelia Research Campus, 2021.
  
• Talk on Cross-Classification Clustering Segmentation, Machine Learning & Biology NSF Workshop, 2019.
  
• Reviewer for Nature, Cell Reports, NeurIPS, ICLR, ICML, CVPR, ECCV, ICCV, AAAI.
  

• Wuwei Zhang, now PhD student at Princeton University
  
• Yicong Li, now PhD student at Harvard University
  
• Xinyue (Thea) Xu, now PhD student at Hong Kong University of Science and Technology
  
• Zhongren Chen, now PhD student at Yale University
  

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