Yutong Wang’s primary research interest is in developing theory for modern machine learning methods with motivation for providing a rigorous foundation for its use in science and engineering. He is currently working on applying deep learning to solving inverse problems in reconstructive spectroscopy motivated by applications in wearable devices. Areas he has worked included over-parametrized learning, ensemble methods, quantized neural networks, kernel methods, and optimization. During his PhD, Yutong was a trainee in the Michigan Center for Single-Cell Genomic Data Analytics research team and was a co-first author on a publication for his contribution in the application of machine learning for genomic data analysis.
- AI Mentor: Qing Qu; Electrical Engineering and Computer Science
- Science Mentor: Pei-Cheng Ku; Electrical Engineering and Computer Science
- Additional Mentor: Jeffrey Fessler; Electrical Engineering and Computer Science, Biomedical Engineering, Radiology
- Research Theme: Developing Machine Learning Theory to Support Scientific and Engineering Applications