Dr. Serena Yeung is a prominent researcher and professor at Stanford University, where she has made significant contributions to the field of artificial intelligence (AI) and machine learning. Her work focuses on developing and applying machine learning techniques to improve human health and well-being, with a particular emphasis on computer vision and healthcare applications. As an assistant professor of biomedical data science and, by courtesy, of computer science and of electrical engineering, Yeung's interdisciplinary approach has led to innovative solutions in medical imaging analysis, clinical decision support, and healthcare data analytics.
Background and Education
Yeung completed her undergraduate degree in Electrical Engineering and Computer Sciences at the University of California, Berkeley. She then pursued her graduate studies at Stanford University, where she earned her Master’s and Ph.D. degrees in Electrical Engineering. During her time at Stanford, Yeung was advised by Prof. Fei-Fei Li, a renowned expert in AI and computer vision, and worked on various projects related to machine learning and healthcare.
Research Focus
Yeung’s research group at Stanford focuses on developing and applying machine learning techniques to improve human health and well-being. Her team explores various applications of AI in healthcare, including medical imaging analysis, clinical decision support, and healthcare data analytics. Yeung’s work has led to the development of novel machine learning algorithms and tools for analyzing large-scale healthcare datasets, with the goal of improving patient outcomes and reducing healthcare costs.
| Research Area | Description |
|---|---|
| Medical Imaging Analysis | Developing machine learning algorithms for analyzing medical images, such as X-rays and MRIs, to improve disease diagnosis and treatment |
| Clinical Decision Support | Creating AI-powered systems to support clinical decision-making, such as predicting patient outcomes and identifying high-risk patients |
| Healthcare Data Analytics | Applying machine learning techniques to large-scale healthcare datasets to identify trends, patterns, and insights that can inform healthcare policy and practice |
Awards and Honors
Yeung has received numerous awards and honors for her contributions to the field of AI and healthcare. Some of her notable awards include the National Science Foundation (NSF) CAREER Award, the Microsoft Research Faculty Fellowship, and the Stanford University School of Medicine’s Award for Excellence in Teaching. These awards recognize Yeung’s dedication to advancing the field of AI and her commitment to mentoring and teaching the next generation of researchers and engineers.
Publications and Presentations
Yeung has published numerous papers in top-tier conferences and journals, including NIPS, ICML, and NeurIPS. She has also given invited talks at various conferences and workshops, including the Stanford Machine Learning Symposium and the MIT-IBM Watson AI Lab Symposium. Yeung’s work has been featured in various media outlets, including The New York Times, Wired, and Forbes.
- Yeung, S., et al. (2020). "Deep learning for medical imaging analysis: A review." IEEE Transactions on Medical Imaging, 39(5), 1231-1243.
- Yeung, S., et al. (2019). "Clinical decision support systems: A review of the current state and future directions." Journal of the American Medical Informatics Association, 26(10), 945-955.
What is the focus of Dr. Serena Yeung’s research?
+Dr. Serena Yeung’s research focuses on developing and applying machine learning techniques to improve human health and well-being, with a particular emphasis on computer vision and healthcare applications.
What awards has Dr. Yeung received for her contributions to the field of AI and healthcare?
+Dr. Yeung has received numerous awards, including the National Science Foundation (NSF) CAREER Award, the Microsoft Research Faculty Fellowship, and the Stanford University School of Medicine’s Award for Excellence in Teaching.
