Enze Zhang is interested in glacier mass balance, the variation of glacier terminus, and deep learning applications. At UTIG, he is working with Ginny Catania and Daniel Trugman to develop a deep-learning-based method to automate the delineation of glacier termini from remote sensing imagery over the entirety of Greenland. The dataset derived from the method would shed light on the controlling factors of terminus variations and help to better understand ice-ocean interactions. He is also interested in expanding the application of deep learning technology to broader fields such as extracting additional glacial features and mapping earthquake evolution caused by human activities.
Glacier dynamics, ice-ocean interaction, remote sensing, deep learning
Ph.D. Geosciences, The Chinese University of Hong Kong
B.S. Geophysics, University of Science and Technology of China