Publications

2024

Toward the Identifiability of Comparative Deep Generative Models. Causal Learning and Reasoning, 2024.

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Multi-ContrastiveVAE disentangles perturbation effects in single cell images from optical pooled screens. bioRxiv, 2024.

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Sequential optimal experimental design of perturbation screens guided by multi-modal priors. Research in Computational Molecular Biology (RECOMB), 2024.

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2023

A Supervised Contrastive Framework for Learning Disentangled Representations of Cell Perturbation Data. Machine Learning in Computational Biology (MLCB), 2023.

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Generative Flow Networks Assisted Biological Sequence Editing. NeurIPS Workshop on Generative AI and Biology, 2023.

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Learning causal representations of single cells via sparse mechanism shift modeling. Conference on Causal Learning and Reasoning, 2023.

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NODAGS-Flow: Nonlinear cyclic causal structure learning. International Conference on Artificial Intelligence and Statistics, 2023.

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The scverse project provides a computational ecosystem for single-cell omics data analysis. Nature Biotechnology, 2023.

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An empirical Bayes method for differential expression analysis of single cells with deep generative models. Proceedings of the National Academy of Sciences, 2023.

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2022

Disentangling shared and group-specific variations in single-cell transcriptomics data with multiGroupVI. Machine Learning in Computational Biology (MLCB), 2022.

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Large-scale differentiable causal discovery of factor graphs. Advances in Neural Information Processing Systems, 2022.

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DestVI identifies continuums of cell types in spatial transcriptomics data. Nature Biotechnology, 2022.

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A Python library for probabilistic analysis of single-cell omics data. Nature Biotechnology, 2022.

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2021

Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data. ICML Workshop in Computational Biology, 2021.

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Joint probabilistic modeling of single-cell multi-omic data with totalVI. Nature Methods, 2021.

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Learning from eXtreme bandit feedback. AAAI Conference on Artificial Intelligence, 2021.

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Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Molecular Systems Biology, 2021.

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2020

Decision-making with auto-encoding variational Bayes. Advances in Neural Information Processing Systems, 2020.

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Enhancing scientific discoveries in molecular biology with deep generative models. Molecular Systems Biology, 2020.

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Cost-effective incentive allocation via structured counterfactual inference. AAAI Conference on Artificial Intelligence, 2020.

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2019

A joint model of RNA expression and surface protein abundance in single cells. Machine Learning in Computational Biology (MLCB), 2019.

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A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. ICML Workshop in Computational Biology, 2019.

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Deep generative models for detecting differential expression in single cells. Machine Learning in Computational Biology (MLCB), 2019.

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Detecting zero-inflated genes in single-cell transcriptomics data. Machine Learning in Computational Biology (MLCB), 2019.

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Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Systems, 2019.

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2018

Deep generative modeling for single-cell transcriptomics. Nature Methods, 2018.

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Information constraints on auto-encoding variational Bayes. Advances in Neural Information Processing Systems, 2018.

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A deep generative model for semi-supervised classification with noisy labels. Bay Area Machine Learning Symposium, 2018.

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2017

A deep generative model for gene expression profiles from single-cell RNA sequencing with application to differential expression. NeurIPS Machine Learning workshop in Computational Biology, 2017.

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A deep generative model for gene expression profiles from single-cell RNA sequencing. Bay Area Machine Learning Symposium, 2017.

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