This is the personal website of Zijian Wan.
Zijian Wan is a Ph.D. student in Geographic Information Science at the University of California, Santa Barbara (UCSB) and is further augmenting his background by pursuing a M.A. in Mathematical Statistics. As a member of the MOVE Lab, he focuses on understanding movement behaviors of animals and humans, and their interactions with the environment, using machine/deep learning, spatiotemporal data mining, and statistical modeling. Zijian specializes in unveiling implicit patterns from massive datasets using data-driven models. He is a self-motivator and accomplished modeler and programmer with more than six years of experience working with machine/deep learning models using Keras/TensorFlow, and PyTorch in more recent projects. He obtained his B.S. degree in Geographic Information Science from Wuhan University, China, and his M.A. in Geography from UCSB. His previous work includes detecting turns and extracting road intersection information from vehicle trajectories using the decision tree and long short-term memory (LSTM), a recurrent neural network (RNN), and interpolating the missing points in trajectories using an uncertainty-aware generative adversarial network (GAN) supported by LSTM.