Wenlu Zhang, Ph.D.
Deep learning is recently considered one of the most breakthrough technologies in computer science. Deep learning models have been successfully applied to a variety of real-world applications such as natural image classification, detection, and generation. However, in the biomedical field, deep learning is still very challenging due to the incomplete data and the multi-modality of patterns. The research projects will introduce students to implementing and designing deep learning models in 3-dimensional (3D) microscopy images. A central challenge in neuroscience is to identify the 3D morphology of neurons from microscopy images. Students will first understand the basic concepts and techniques in deep learning such as the convolutional neural network (CNN), recurrent neural network (RNN) and generative adversarial network (GAN), etc. They will also learn how to develop 3D encoder-decoder neural network architectures to train and predict using large microscopy images in an end-to-end manner.
Potential mentees should have basic coding skill, such as Python programming, and know basic machine learning algorithms.
Dr. Wenlu Zhang joined the CSULB Department of Computer Engineering & Computer Science as an Assistant Professor in Fall 2017. She worked as a postdoctoral associate in the Washington State University School of Electrical Engineering and Computer Science for one year after earning her Ph.D. in Computer Science from Old Dominion University in 2016.
Dr. Zhang received an Outstanding Research Assistant Award while a graduate student at Old Dominion. She and her team participated in the MICCAI Challenge on Circuit Reconstruction from Electron Microscopy Images (CREMI). Her team ranked first on synaptic cleft detection and second on neuron segmentation tasks. Zhang also serves as a program committee member for the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) and the ACM International Conference on Information and Knowledge Management (CIKM).
Or by appointment