Author

Hanrui Lyu

Northwestern UniversityORCID 0000-0002-4349-8975Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:electronic health records· 2 Research Focus:cell type annotation· 1 Approach:contrastive learning· 1 Research Focus:deconvolution· 1 Organism:mouse· 1 Approach:statistical model· 1

Frequent Co-Authors

Cole HurwitziD· 2 shared Liam PaninskiiD· 2 shared Alexandre PougetiD· 1 shared Andrew M. SheltoniD· 1 shared Chandramouli ChandrasekaraniD· 1 shared Charles FindlingiD· 1 shared Charlie WindolfiD· 1 shared Erdem VaroliD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

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10 top collaborators · 30 mutual co-author links

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2 shared papersNeuroscience

Cole HurwitzORCID

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2 papers

Recent·Most cited

Exploiting correlations across trials and behavioral sessions to improve neural decoding

Yizi Zhang, Hanrui Lyu, Cole Hurwitz, Shuqi Wang, Charles Findling, Yanchen Wang, Félix Hubert, Alexandre Pouget, Erdem Varol, Liam Paninski

This paper improves neural decoding by exploiting correlations across trials and sessions, showing better accuracy for four behaviors in mice using interpretable models.

Neuron · Nov 26, 2025Peer-reviewedNeuroscience

In vivo cell-type and brain region classification via multimodal contrastive learning

Han Young Yu, Hanrui Lyu, Ethan YiXun Xu, Charlie Windolf, Eric Lee, Fan Yang, Andrew M. Shelton, Shawn R. Olsen, Sahar Minavi, Olivier Winter, Eva L. Dyer, Chandramouli Chandrasekaran, Nicholas A. Steinmetz, Liam Paninski, Cole Hurwitz

This paper introduces NEMO, a multimodal contrastive learning method for classifying cell types and brain regions from electrophysiological data, achieving state-of-the-art results on two opto-tagged datasets and the International Brain Laboratory Brain-wide Map dataset.

bioRxiv · Nov 5, 2024 · PreprintNeuroscience