(929) 287-8080

Applications:
Computer Science, Electrical Engineering, Medical Imaging
Methodologies:
Artificial Intelligence, Computational Tools for Data Science, Deep Learning, High-Dimensional Data Analysis, Image Data Processing and Analysis, Information Theory, Machine Learning, Optimization, Pattern Analysis and Classification, Signal Processing, Sparse Data Analysis

Qing Qu

Assistant Professor

EECS

His research interest lies in the intersection of signal processing, data science, machine learning, and numerical optimization. He is particularly interested in computational methods for learning low-complexity models from high-dimensional data, leveraging tools from machine learning, numerical optimization, and high dimensional geometry, with applications in imaging sciences, scientific discovery, and healthcare. Recently, he is also interested in understanding deep networks through the lens of low-dimensional modeling.