Research
Papers
Notes: * stands for equal contributions; $^\dagger$ stands for students that I mentored.
Manuscript
A Community Machine Learning Challenge to Predict the Effects of Gene Perturbations on T Cell Differentiation for Cancer Immunotherapy
Jiaqi Zhang, Marc Schwartz, Orr Ashenberg, Mohammed Mutaher, Oluwatomisin Olajide, …, Yuri Pritykin, Nir Hacohen*, Caroline Uhler*.
[available upon request]
MORPH Predicts the Single-cell Outcome of Genetic Perturbations across Various Data Modalities
Chujun He*, Jiaqi Zhang*, Munther Dahleh, Caroline Uhler.
[bioRxiv]
[code]
[bibtex]
Faithfulness and Intervention-Only Causal Discovery
Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler.
[workshop]
Meta-Dependence in Conditional Independence Testing
Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler.
[arXiv]
[code]
[bibtex]
Publications
On the Number of Conditional Indepdence Tests in Constraint-based Causal Discovery
Marc Franquesa Monés$^\dagger$*, Jiaqi Zhang*, Caroline Uhler. AISTATS (Spotlight Presentation, <3%), 2026.
[conference to appear]
[available upon request]
Learning Genetic Perturbation Effects with Variational Causal Inference
Emily Liu$^\dagger$*, Jiaqi Zhang*, Caroline Uhler. PLOS Computational Biology, 2026.
[journal to appear]
[bioRxiv]
[code]
[bibtex]
Causal Structure and Representation Learning with Biomedical Applications
Caroline Uhler*, Jiaqi Zhang*. Proceedings of the International Congress of Mathematicians, 2026.
[conference to appear]
[arXiv]
[bibtex]
Can Diffusion Models Disentangle? A Theoretical Perspective
Liming Wang, Muhammad Jehanzeb Mirza, Yishu Gong, Yuan Gong, Jiaqi Zhang, Brian H. Tracey, Katerina Placek, Marco Vilela, James R. Glass. NeurIPS, 2025.
[arXiv]
[conference]
[bibtex]
Probabilistic Factorial Experimental Design for Combinatorial Interventions
Divyal Shyamal$^\dagger$*, Jiaqi Zhang*, Caroline Uhler. ICML (Spotlight, < 2.6%), 2025.
[arXiv]
[conference]
[bibtex]
Identifiabiltiy Guarantees of Causal Disentanglement from Purely Observational Data
Ryan Welch$^\dagger$*, Jiaqi Zhang*, Caroline Uhler. NeurIPS, 2024.
[arXiv]
[code]
[conference]
[bibtex]
Causal Discovery with Fewer Conditional Independence Tests
Kirankumar Shiragur*, Jiaqi Zhang*, Caroline Uhler. ICML, 2024.
[arXiv]
[code]
[bibtex]
Towards Causal Foundation Model: on Duality between Causal Inference and Attention
Jiaqi Zhang*, Joel Jennings, Agrin Hilmkil, Nick Pawlowski, Cheng Zhang, Chao Ma*. ICML, 2024.
[arXiv]
[code]
[bibtex]
Membership Testing in Markov Equivalence Classes via Independence Query Oracles
Jiaqi Zhang*, Kirankumar Shiragur*, Caroline Uhler. AISTATS (Oral Presentation, <3%), 2024.
[arXiv]
[conference]
[bibtex]
Meek Separators and Their Applications in Targeted Causal Discovery
Kirankumar Shiragur*, Jiaqi Zhang*, Caroline Uhler. NeurIPS, 2023.
[arXiv]
[code]
[conference]
[bibtex]
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
Jiaqi Zhang, Kristjan Greenewald, Chandler Squires, Akash Srivastava, Karthikeyan Shanmugam, Caroline Uhler. NeurIPS, 2023.
[arXiv]
[code]
[conference]
[bibtex]
Active Learning for Optimal Intervention Design in Causal Models
Jiaqi Zhang, Louis Cammarata, Chandler Squires, Themistoklis P Sapsis, Caroline Uhler. Nature Machine Intelligence, 2023.
[arXiv]
[code]
[journal]
[bibtex]
Machine-learning-optimized Cas12a Barcoding Enables the Recovery of Single-cell Lineages and Transcriptional Profiles
Nicholas W Hughes, Yuanhao Qu*, Jiaqi Zhang*, Weijing Tang*, Justin Pierce*, Chengkun Wang, Aditi Agrawal, Maurizio Morri, Norma Neff, Monte M Winslow, Mengdi Wang, Le Cong. Molecular Cell, 2022.
[code]
[journal]
[bibtex]
Stochastic Augmented Projected Gradient Methods for the Large-Scale Precoding Matrix Indicator Selection Problem
Jiaqi Zhang, Zeyu Jin, Bo Jiang, Zaiwen Wen. IEEE Transactions on Wireless Communications, 2022.
[journal]
[bibtex]
Matching a Desired Causal State via Shift Interventions
Jiaqi Zhang, Chandler Squires, Caroline Uhler. NeurIPS, 2021.
[arXiv]
[code]
[conference]
[bibtex]