Computational Imaging Group (CIG) is looking for motivated WashU students for the following research projects for Spring 2020. If you are interested in a given project, please apply by emailing your resume to the project supervisor.

Implementing CNNs with Tensorflow 2.0

Status: reserved (Ziwen Wang) Description: Tensorflow is one of the most popular platforms for deep learning. In this project, the goal is to implement several popular CNN computer vision architectures (including U-Net, DnCNN, and etc.) on Tensorflow 2.0. Moreover, the student will need to conduct some experiments to demonstrate the correctness of their implementation. This project is well-suited for those students who wants to sharpen their coding skills for deep learning.. Supervision: Yu Sun (sun.yu@wustl.edu), Ulugbek Kamilov Areas: Deep Learning, Large-Scale Optimization

Training Robust Convolutional Neural Networks

Status: reserved (Renhao Liu) Description: Imposing mathematical properties to convolutional neural networks is an active area of research. In this project, we will specifically focus on training CNNs that are Lipschitz continuous. The application of such neural nets include computational imaging, generative models, and robustness in machine learning. Supervision: Yu Sun (sun.yu@wustl.edu), Ulugbek Kamilov Areas: Deep Learning, Large-Scale Optimization

Accelerated MRI using Generative Models

Status: reserved (Guangyu Meng) Description: Generative models have recently gained significant attention in computer vision and computational imaging. This project seeks to build on their ability to concisely represent high-dimensional imaging data to design algorithms for accelerated magnetic resonance imaging. The student will work on training and deploying generative models for relaxing data acquisition constraints. Supervision: Jiaming Liu (jiaming.liu@wustl.edu), Ulugbek Kamilov Areas: Deep Learning, Large-Scale Optimization

Deep Learning for Quantitative Brain Imaging

Status: reserved (Eddie Chandler) Description: Quantitative brain imaging is extensively used for early detection of neurodegenerative diseases such as Alzheimer disease and Dementia. This project seeks to build on the latest progress in self-supervised deep learning to enable the next generation algorithms that are robust to noise and other artifacts. This project is well-suited for those students interested in learning advanced concepts in deep learning and imaging. Supervision: Yu Sun (sun.yu@wustl.edu), Ulugbek Kamilov Areas: Medical Imaging, Deep Learning

Learning for Video Reconstruction

Status: available Description: Video reconstruction is aimed to restore clear videos from corrupted observations, where state-of-the-art methods have achieved promising performance via supervised deep learning technologies. However, those methods require ground-truth videos, which may be inaccessible in some cases. Our goal is to develop a novel video reconstruction algorithm without the need of ground-truth. Supervision: Weijie Gan (weijie.gan@wustl.edu), Yu Sun (sun.yu@wustl.edu), Ulugbek Kamilov Areas: Deep Learning, Low-Level and Physics-Based Vision

Learning for Practical Video Compression

Status: reserved (Alan Zhu) Description: Video compression plays a critical role in internet traffic given limited bandwidth budget and increasing online content nowadays. Our goal is to develop novel and practical video compression algorithms especially when the required videos are in low-quality Supervision: Weijie Gan (weijie.gan@wustl.edu), Yu Sun (sun.yu@wustl.edu), Ulugbek Kamilov Areas: Deep Learning, Low-Level and Physics-Based Vision

• Machine learning for 4D MRI (W. Gan, H. An, U. S. Kamilov)
• Proximal Optimization for Computerized Tomography (R. Schurz, J. Liu, U. S. Kamilov)
• End-to-End Training of VAE-GAN Priors (G. Meng, J. Liu, U. S. Kamilov)
• Design of GAN Prios for Iterative Algorithms (A. Bani, J. Liu, U. S. Kamilov)
• Recovery Experiments for Deep Learning (J. Liu, Y. Sun, U. S. Kamilov)
• Model-based and Learning-based Computerized Tomography (M. Xie, Y. Sun, U. S. Kamilov)
• Stochastic Algorithms using Deep Priors (Z. Wu, Y. Sun, U. S. Kamilov)
• Fast Automatic Focal Plane Selection for Brightfield Microscopy using Machine Learning (Y. Song, X. Xu, N. Laird, Z. Pincus, U. S. Kamilov)
• Nonlocal Learning for Image Restoration (Y. Li, X. Xu, U. S. Kamilov)
• Motion-artifact Correction in Quantitative MRI (H. Tang, X. Xu, U. S. Kamilov)

• Motion artifact reduction in quantitative brain MRI (N. Woythaler, X. Xu, U. S. Kamilov)
• Large-Scale Optimization for Computational Imaging (Y. Jia, U. S. Kamilov)
• Generative Adversarial Networks for Image Restoration (R. Suzuki, X. Xu, U. S. Kamilov)
• Adversarially robust classifiers for image reconstruction (E. Tang, X. Xu, U. S. Kamilov)
• Learning for material imaging with light (Z. Wu, Y. Sun, U. S. Kamilov)
• Asynchronous large-scale optimization for imaging (Y. Sun, Y. Sun, U. S. Kamilov)
• Next generation Cryo-EM imaging (M. Xie, Y. Sun, U. S. Kamilov)
• Computational methods for quantitative brain MR imaging (M. Torop, Y. Sun, U. S. Kamilov)

• Deep Single-Molecule Super-Resolution Imaging (S. Xu, Y. Sun, H. Mazidi, M. Lew, U. S. Kamilov)
• Learning for material imaging with light (Z. Wu, Y. Sun, U. S. Kamilov)
• Regularization with ConvNets (J. Liu, Y. Sun, U. S. Kamilov)

• Regularization with ConvNets (J. Liu, Y. Sun, U. S. Kamilov)
• Learning for 3D computational imaging (W. Gan, X. Xu, U. S. Kamilov)
• Convolutional dictionary learning for image reconstruction (F. Long, X. Xu, U. S. Kamilov)
• Unsupervised deep image learning (H. Zou, Y. Sun, U. S. Kamilov)
• Distributed algorithms for nonconvex stochastic optimization (M. Abijaoude, U. S. Kamilov)
• Learning for material imaging with light (R. Wu, Y. Sun, U. S. Kamilov)
• Image restoration with GANs (F. Zhai, Y. Sun, U. S. Kamilov)
• Learning for MRI (J. Xing, X. Xu, U. S. Kamilov)
• Learning for material imaging with light (R. Wu, Y. Sun, U. S. Kamilov)
• Dictionary Learning for Fourier Ptychographic Microscopy (S. Xu, X. Xu, U. S. Kamilov)