Latest News

2025

2024

  • 202411 I am serving as a program committee member for RECOMB 2025.
  • 202407 LLMs in scientific papers is accepted at COLM 2024. See you in Philadelphia!
  • 202406 Huang et al. Pathologist-AI collaboration study is published in Nature Biomedical Engineering.
  • 202406 We introduce TextGrad: Automatic "Differentiation" via Text! Start optimizing prompts in your LLM system. GitHub stars for zou-group/textgrad
  • 202405 Impact of ChatGPT in AI review is accepted at ICML 2025 (oral).
  • 202403 New York Times opinion on AI-generated articles (co-authored manuscript).
  • 202401 New study on off-label and off-guideline cancer therapy usage is accepted in Cell Reports Medicine.

2023

Active Research Areas

1. Foundation Model for Pathology

Pathology is medicine's ground truth. We train AI models with vision, language, and knowledge to improve machine understanding of pathology.

Related Publication:

A visual–language foundation model for pathology image analysis using medical Twitter

Zhi Huang†, Federico Bianchi†, Mert Yuksekgonul, Thomas J. Montine, James Zou (†: Equal contribution)

Nature Medicine (2023)

Nature Medicine September 2023 cover story.

✨ Checkout our OpenPath, a visual-language dataset for pathology at here.

Nature Medicine Cover

2. AI Systems and Human-AI Collaboration

We develop AI software platforms to assist human experts in clinical practice, promote human-AI collaboration. We also optimize LLMs.

Related Publication:

A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies

Zhi Huang, Eric Yang, Jeanne Shen, Dita Gratzinger, ..., Thomas J. Montine & James Zou

Nature Biomedical Engineering (2024)

nuclei.io: AI platform for digital pathology [website] [GitHub stars for huangzhii/nuclei.io]

Optimizing generative AI by backpropagating language model feedback

Mert Yuksekgonul*, Federico Bianchi*, Joseph Boen*, Sheng Liu*, Pan Lu*, Zhi Huang*, Carlos Guestrin, James Zou

Nature (2025)

✨ We introduce TextGrad, automatic "differentiation" via text.

Brain Proteomic Analysis

3. Machine Learning and Statistical Analysis for Human Diseases

We leverage machine learning and statistical analysis tools to study human diseases (e.g., Alzheimer's Disease, breast cancer) with imaging, omics, and clinical data.

Related Publication:

Brain Proteomic Analysis Implicates Actin Filament Processes and Injury Response in Resilience to Alzheimer's Disease

Zhi Huang, Gennifer E. Merrihew, Eric B. Larson, Jea Park,..., James Y. Zou, Michael J. MacCoss & Thomas J. Montine

Nature Communications (2023)

✨ First study to identify molecular signatures of resilience to Alzheimer's Disease.

Brain Proteomic Analysis

Artificial Intelligence Reveals Features Associated with Breast Cancer Neoadjuvant Chemotherapy Responses from Multi-stain Histopathologic Images

Zhi Huang, et al.

NPJ Precision Oncology (2023)

✨ AI-powered prediction of breast cancer treatment outcomes from H&E and IHC.

Brain Proteomic Analysis

SALMON: Survival Analysis Learning with Multi-Omics Neural Networks on Breast Cancer

Zhi Huang, et al.

Frontiers in genetics (2019)

✨ Novel deep learning framework for multi-omics data integration and survival prediction.

Brain Proteomic Analysis