PhD | University of Michigan
About David Hong
David Hong is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Delaware. Previously, he was an NSF Postdoctoral Research Fellow in the Department of Statistics and Data Science at the University of Pennsylvania. He completed his PhD in the Department of Electrical Engineering and Computer Science at the University of Michigan, where he was an NSF Graduate Research Fellow. He also spent a summer as a Data Science Graduate Intern at Sandia National Labs.
His research aims to develop principled approaches to answering the question: how can we make sense of big heterogeneous data? What underlying patterns (i.e., signals) are in the data and how can we find them? Moreover, how can we use the learned structure to better solve inverse problems, e.g., in medical imaging? Most recently, his works has centered on theory and algorithms for low-rank matrix and tensor methods, which are unsupervised learning methods (in the language of machine learning and AI) and have numerous applications (e.g., in RADAR, data science, genomics, astronomy, etc.). He is particularly interested in understanding how to make these methods robust to various forms of heterogeneity in the signals and in the noise.