Zhang's research focuses on developing data-driven identification and prediction systems for real-world medical applications. I am particularly interested in enhancing the generalization and interpretability of machine learning and deep learning models in medicine, as well as exploring innovative methods to improve model accuracy. To address challenges such as the lack of annotated data, limited generalization capabilities, and the need for interpretable models, I have applied several strategies, including privileged information learning, self-supervised learning, and approximate reasoning.

In this episode, Dr. Elizabeth Harry is joined by Michigan Medicine’s Chief Information Officer Dr. Andrew Rosenberg. Harry and Rosenburg discuss how technology has aided and created hurdles to positive well-being in the medical setting. The two talk about the human focus, and ways data and innovation can be helpful in creating better relationships to reduce burnout.

J. Michelle Kahlenberg, M.D., Ph.D., led a team of researchers looking at an alternative treatment for lupus rashes with a topical treatment called mupirocin. 

The Carol Hollenshead Inspire Award for Excellence in Promoting Equity and Social Change recognizes faculty, staff and students — either an individual or a group — who have proven that social change is possible through persistent hard work and who demonstrate that one person can make a lasting difference in their communities. Girls Who Code was founded at U-M in 2017 by doctoral students in the Medical School’s Gilbert S. Omenn Department of Computational Medicine and Bioinformatics.

University of Michigan researchers found that emergency departments vary widely in how they balance the need to diagnose appendicitis with the potential harms of overtesting.

Researchers at have validated a previously developed urine test, which can potentially bypass invasive procedures for prostate cancer detection among men who are unlikely to benefit.