Yu Xie on Muck Rack

Yu Xie

Princeton
As seen in: AIChE Journal, Color Research & Application, Nature, Reuters, Reuters Japan, Wiley Online Library, ScienceDirect, BMJ Open, Frontiers, Frontiers in Immunology and
Bert G. Kerstetter '66 University Professor of Sociology and the Princeton Institute for International and Regional Studies.
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Yu

Contact Yu, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Switchable Altermagnetism Induced by Polyhedral Rotation Distortion Click to copy article link Article link copied!

|
By Yu Xie, Yifei Hao, Dinghui Wang
| ACS Publications
Abstract Click to copy section linkSection link copied! Altermagnetism has emerged as a distinct class of unconventional magnetism, uniquely combining the nonrelativistic spin splitting of ferromagnets with the stray-field immunity of antiferromagnets. However, realizing altermagnetism driven by universal and strain-tunable structural geometry effects remains a critical challenge. Here, we introduce a mechanism for inducing altermagnetism via a polyhedral rotation distortion.
Open in Who Shared Issue with article?

A deep learning system for non-invasive breast cancer diagnosis with multimodal data

|
By Yonghao Li, Jiadong Zhang, Haoyuan Chen, Long Yang, Yu Xie, Qi Xu, Jun Li, Ling Dai, Zhenlu Yang, Yingnan Luo, Jue Zhao, Yu Yin, Shijie Wang, YUAN ZHANG, Zhuolin Li, Zhen Li, Rui Zheng, Jianping Wu, Zhihui Shi, Jun Liu, Jiayin Zhang, Rongpin Wang, Zhongxiang Ding, Jing Ke, Zhenhui Li, Dinggang Shen, Pinxiong Li, Zheren Li, Ruoxia Shen
| Nature
Abstract Early and accurate diagnosis of breast cancer is critical for minimizing needle biopsies and enhancing patient outcomes and requires effective integration of multimodal information. In this article, we introduce a breast cancer intelligent non-invasive diagnosis system (BINDS) to integrate multimodal medical imaging data for breast cancer risk assessment and subtype classification.
Open in Who Shared Issue with article?

Evaluating the statistical realism of LLM-generated social science data

|
By Yueqi Xie, Mengdi Wang, Yu Xie
| PNAS
Evaluating the statistical realism of LLM-generated social science data Contributed by Yu Xie; received December 31, 2025; accepted April 8, 2026; reviewed by David B. Grusky and Michael Hout Significance Large language models (LLMs) enable the generation of data that could potentially be analyzed for social research. While the need for assessing the validity of such AI-generated data is widely recognized, we do not yet have a coherent framework for assessment.
Open in Who Shared Issue with article?
See all 262 licensed articles (274 Total)

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Get in touch with Yu

Contact Yu, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack
 •  Privacy  •  Terms  •  Security and Legal  •   •   •   • 

© 2026 Muck Rack

SOC 2® Certified