Stable Differentiable Causal Discovery
Achille Nazaret*, Justin Hong*, Elham Azizi, and David Blei.
Proceedings of the 41st International Conference on Machine Learning (ICML) (2024). [URL].

A heterogeneous pharmaco-transcriptomic landscape induced by targeting a single oncogenic kinase
Ross M Giglio, ..., Justin Hong, ..., and Jose L McFaline-Figueroa.
bioRxiv (2024). [URL].

CRISPRmap: Sequencing-free optical pooled screens mapping multi-omic phenotypes in cells and tissue
Jiacheng Gu, ..., Justin Hong, ..., and Jellert T Gaublomme.
bioRxiv (2024). [URL].

BetterBoost-Inference of Gene Regulatory Networks with Perturbation Data
Achille Nazaret and Justin Hong.
GSK.ai CausalBench Challenge, Machine Learning for Drug Discovery (MLDD) Workshop at ICLR, 1st Place Prize, Oral Presentation (2023). [URL].

Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells
Adam Gayoso, ..., Justin Hong, ..., and Nir Yosef.
Nature Methods (2023). [URL].

Deep generative modeling for quantifying sample-level heterogeneity in single-cell omics
Pierre Boyeau*, Justin Hong*, Adam Gayoso, Michael Jordan, Elham Azizi, and Nir Yosef.
Machine Learning in Computational Biology (MLCB), Oral Presentation (2022). [URL].

A Python library for probabilistic analysis of single-cell omics
Adam Gayoso*, Romain Lopez*, Galen Xing*, ..., Justin Hong, ..., and Nir Yosef.
Nature Biotechnology (2022). [URL].

Likelihood-based deconvolution of bulk gene expression data using single-cell references
Dan D. Erdmann-Pham*, Jonathan Fischer*, Justin Hong, and Yun S. Song.
Genome Research (2021). [URL].

A Likelihood-based Deconvolution of Bulk Gene Expression Data Using Single-cell References
Justin Hong, Dan D. Erdmann-Pham, Jonathan Fischer, and Yun S. Song.
Master’s Thesis. University of California, Berkeley, 2021. [URL].

Robust Federated Learning in a Heterogeneous Environment
Avishek Ghosh*, Justin Hong*, Dong Yin, and Kannan Ramchandran.
ICML Workshop on Privacy and Security in ML (2019)[URL].