Yutong Wang’s primary research interest was in developing theory for modern machine learning methods, motivated by providing a rigorous foundation for their use in science and engineering. He was working on applying deep learning to solving inverse problems in reconstructive spectroscopy, motivated by applications in wearable devices. Areas he had worked in included over-parameterized 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 to 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