Zhi Huang, PhD
Postdoctoral fellow, Department of Biomedical Data Science, Stanford University
Using Al to assist clinical decision-making in pathology and precision medicine

Learning outcomes

  • Learn how to train a visual-language foundation model for pathology images;
  • Learn how publicly shared medical information can be harnessed to enhance diagnosis, knowledge sharing and education;
  • Share nuclei.io, the state-of-the-art active learning software for digital pathology;
  • Demonstrate how to leverage Al and computational approaches to forecast post-chemotherapy outcome from pretreatment tissue biopsies;
  • Learn how to train medical Al in the same way as humans.

Speaker bio

Zhi Huang is a postdoc at Stanford University. In August 2021, he received his Ph.D. degree from Purdue University, majoring in Electrical and Computer Engineering (ECE). His background is in the area of Artificial Intelligence, Human-AI Collaboration, Digital Pathology, and Precision Medicine. He has rich research experiences in a wide spectrum of biomedical data. In 2022, Zhi and his advisors co-founded nuclei.io — the AI platform for digital pathology. It was selected as one of the nine Stanford Catalyst innovations, and was later acquired in 2023.