题　目：AI and Machine Learning Innovations for Next-Generation Healthcare

嘉　宾：Dr. Joyce Yan-Ran Wang  Assistant Professor, University of Michigan-Ann Arbor

主持人：盛斌 教授 上海交通大学计算机学院教授、主动健康战略与发展研究院兼职研究员

时　间：2025年11月7日（周五）10:00-11:30

地　点：上海交通大学徐汇校区安泰浩然204室

内容简介:

Artificial intelligence is redefining how we understand, diagnose, and treat human diseases. In this talk, Dr. Yan-Ran Joyce Wang will share how her group, the PixAIL Lab (Precision Imaging × Artifical Intelligence Lab) at the University of Michigan, Ann Arbor, develops next-generation AI systems that bridge computer vision, multimodal learning, and medical data.

By integrating deep learning with diverse biomedical data — from imaging and genomics to clinical text — her lab aims to build foundation models for precision medicine and real-world deployable AI systems that improve diagnosis, prognosis, and treatment planning. Dr. Wang will also discuss open problems at the intersection of machine learning and medical data constraints, inviting collaboration from computer science students eager to work on impactful, high-dimensional, and safety-critical data challenges.

演讲人简介：

Dr. Joyce Yan-Ran Wang is a tenure-track Assistant Professor at the University of Michigan, Ann Arbor, jointly appointed in Biomedical Engineering (BME) and Electrical & Computer Engineering (ECE). She earned her Ph.D. in Computer Science from Northwestern University in 2019 and completed postdoctoral training at Stanford University’s Departments of Biomedical Data Science and Radiology, as part of the Stanford Center for Artificial Intelligence in Medicine & Imaging (AIMI).

Her work pioneers the intersection of computer vision and biomedical data analysis, tackling core ML problems such as domain adaptation, multi-task learning, data efficiency, and interpretability in healthcare. As first and corresponding author, Dr. Wang has published in Nature Medicine (2024), Cancer Cell (2024), European Journal of Nuclear Medicine and Molecular Imaging (2023), and Radiology: Artificial Intelligence (2023). She has also contributed to algorithmic advances presented at CVPR, ICLR, AAAI, ACM Multimedia.

At Michigan, her PixAIL Lab brings together computer scientists, engineers, and clinicians to develop AI foundation models and vision-language systems that learn from large-scale multimodal medical data. The lab welcomes students passionate about machine learning, computer vision, and AI for science to join a mission-driven environment that combines algorithmic innovation with real-world impact in medicine.