About Me
I’m an incoming Assistant Professor at Columbia University in Computer Science, with an affiliation in Systems Biology. I recently earned my PhD at MIT EECS, advised by Caroline Uhler. My research focuses on establishing theoretical and algorithmic foundations for discovery and decision-making within systems created by underlying causal rules. In particular, I develop tools to understand causal relationships from data, model and extrapolate to predict the effects of interventions, and select informative interventions for experimental design. Motivated by problems in cell biology, these tools help accelerate and translate to biomedical discovery.
I was a research intern at Bytedance, Microsoft Research, and Apple. I obtained my Bachelor’s degree in Mathematics from Peking University, where I worked with Zaiwen Wen, Mengdi Wang, and Le Cong.
News
- NOW. We are hosting the Obesity Machine Learning Competition to tackle metabolic diseases, as the latest series within the Cell Perturbation Prediction Challenge (CPPC).
- Apr., 2026. We organized the Causal Learning and Reasoning (CLeaR) 2026 conference at the Broad Institute of MIT and Harvard between April 6th to 8th. Check out the full agenda and contributions here!
- Dec., 2025. We organized a NeurIPS2025 workshop on Uncovering Causality in Science (CauScien). Check out the talks and accepted papers. Follow our official X account @CauScien for more updates from the growing community.
- Jul., 2025. We organized an ICML2025 workshop on Scaling up Intervention Models (SIM). Check out the talks and accepted papers.
- Jul., 2025. How to more efficiently study complex treatment interaction: MIT news covered our work on experimental design.
- Dec., 2024. Honored to receive the Stuart L. Schreiber Awards in Scientific Excellence.
- Nov., 2024. A causal theory for studying the cause-and-effect relationships of genes: MIT news covered our work on causal theory.
- Oct., 2024. Happy to be selected for the Rising Star in EECS 2024 cohort. Learn more about the workshop here.
- Mar., 2024. Happy to present at Women in Data Science (WiDS) conference. Learn more about the conference here.
- Oct., 2023. A more effective experimental design for engineering a cell into a new state: MIT news covered our work on active learning in causal models. Read also on EWSC news.
- Mar., 2023. We hosted the Cancer Immunotherapy Data Science Grand Challenge, as first within the Cell Perturbation Prediction Challenge (CPPC). Read about the challenge here.
Awards
- Stuart L. Schreiber Award in Scientific Excellence, Broad Institute (2024)
- Rising Stars in EECS (2024)
- Apple AI/ML PhD Fellowship (2023-2025)
- Eric and Wendy Schmidt Center PhD Fellowship (2022-2023)
- Member of National Mathematics Training Team of China (2016)
- Gold medal in CMO (2016)
- Rank 1st in Chinese Girls’ Mathematics Olympics (2015)
Papers
Manuscript
A Community Machine Learning Challenge to Predict the Effects of Gene Perturbations on T Cell Differentiation for Cancer Immunotherapy
Jiaqi Zhang, Marc Schwartz, Mohammed Mutaher, Oluwatomisin Olajide, Yuri Pritykin, Orr Ashenberg*, Nir Hacohen*, Caroline Uhler*.
[bioRxiv]
[bibtex]
Latent Causal Diffusions for Single-Cell Perturbation Modeling
Lars Lorch, Jiaqi Zhang, Charlotte Bunne, Andreas Krause, Bernhard Schölkopf, Caroline Uhler.
[arXiv]
[bibtex]
MORPH Predicts the Single-cell Outcome of Genetic Perturbations across Various Data Modalities
Chujun He*, Jiaqi Zhang*, Munther Dahleh, Caroline Uhler.
[bioRxiv]
[code]
[bibtex]
Publications
Relaxing Faithfulness with Intervention-Only Causal Discovery
Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler. UAI, 2026.
[conference to appear]
[arXiv]
[shorter version at workshop]
[code]
[bibtex]
Meta-Dependence in Conditional Independence Testing
Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler. UAI, 2026.
[conference to appear]
[arXiv]
[code]
[bibtex]
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]
[arXiv]
[bibtex]
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]