Student projects

CIG is looking for motivated WashU students for the following projects. We have a history of mentoring undergraduate and graduate students interested in research, with many of our alumni becoming PhD students in top programs, including WashU, and many projects leading to publications in top venues (see below). If you are interested in a given project, please apply by emailing your resume and a short description of your interests to the project supervisor.

Robust NMR spectroscopy using deep learning

Status: available Description: Nuclear Magnetic Resonance (NMR) spectroscopy is an important tool for understanding the molecular structure, dynamics, and interactions of compounds. The quality of collected NMR spectra is often degraded by noise and various artifacts. This project seeks to develop state-of-the-art deep learning framework for enhancing NMR spectra. This project is in collaboration with BioProducts Engineering Lab in the EECE department. Supervision: Edward Chandler (e.p.chandler@wustl.edu), Ulugbek Kamilov Areas: Scientific Imaging, Deep Learning

• Deep Learning for Quantitative MRI (K. Duan, Y. Wang, and U. S. Kamilov)
• Deep Learning for MRI Denoising (K. Duan, Y. Wang, and U. S. Kamilov)
• Accelerated Data Acquisition for Quantitative MRI (J. Wu, R. Tang, Y. Wang, and U. S. Kamilov)
• Generative Models for Imaging Inverse Problems (H. Gao, W. Gan, and U. S. Kamilov)
• Deformation Compensated Learning (C. Park, W. Gan, and U. S. Kamilov)
• Deep Learning for Blind Inverse Problems (H. Zhang, W. Gan, and U. S. Kamilov)
• Video restoration for two-photon microscopy (D. Wang, W. Gan, and U. S. Kamilov)
• Deep learning for diagnosis of post-traumatic elbow disease (V. Siu, W. Gan, and U. S. Kamilov)
• Learning for Small Angle Neutron Scattering (SANS) (N. Asokan, S. Shoushtari, and U. S. Kamilov)
• Learning the Development Patterns of Cancer (H. Mellow, Z. Sun, and U. S. Kamilov)

• Noise Adaptive Deep Denoisers for Imaging (E. McMillian, Y. Hu, and U. S. Kamilov)
• Interactive Digit Recognition for ML Education (J. Ti, F. Sun, and U. S. Kamilov)
• Detecting Multiple Myeloma using Deep Image Analysis (X. Yang, F. Sun, and U. S. Kamilov)
• Deep Learning for 3D Quantitative Brain MRI (K. Prinos, Y. Hu, and U. S. Kamilov)
• Machine Learning Biomarkers for Fetal Growth Restrictions (Z. Xiang, Y. Hu, and U. S. Kamilov)
• Deep Learning for Digital Mammography (E. Sul, S. Shoushtari, and U. S. Kamilov)
• Deep Learning for the Next-Gen Scientific Imaging (J. Yao, W. Zhang, S. Shoushtari, and U. S. Kamilov)
• Deep learning for diagnosis of post-traumatic elbow disease (V. Siu, W. Gan, and U. S. Kamilov)
• Deformation-Compensated Learning (C. Park, W. Gan, and U. S. Kamilov)
• Continuous Regualarier for Inverse Problems (N. Huang, W. Gan, and U. S. Kamilov)
• Breast Cancer Risk Assessment using Machine Learning (C. Jin, S. Shoushtari, and U. S. Kamilov)
• Robust deep learning for computational imaging (E. Chandler, J. Liu, and U. S. Kamilov)
• Efficient and effective data-driven image representations (H. Gao, W. Gan, and U. S. Kamilov)
• Deep learning for the next-generation microscopy (Y. Wang, J. Liu, and U. S. Kamilov)

• Deep learning for the next-generation microscopy (Y. Wang, J. Liu, and U. S. Kamilov)
• Deep learning for diagnosis of post-traumatic elbow disease (H. T. James, W. Gan, and U. S. Kamilov)
• Memory and computationally efficient deep learning (Z. Zou, J. Liu, and U. S. Kamilov)
• Efficient and effective data-driven image representations (H. Gao, W. Gan, and U. S. Kamilov)
• Robust deep learning for computational imaging (E. Chandler, J. Liu, and U. S. Kamilov)
• Breast Cancer Risk Assessment using Machine Learning (C. Jin, S. Shoushtari, and U. S. Kamilov)
• Continuous Regualarier for Inverse Problems (N. Huang, W. Gan, and U. S. Kamilov)
• Deformation-Compensated Learning (C. Park, W. Gan, and U. S. Kamilov)
• Deep Learning for Digital Mammography (E. Choi, S. Shoushtari, and U. S. Kamilov)
• Quantitative Brain MRI using Deep Learning (H. Li, Y. Hu, and U. S. Kamilov)
• Image Restoration using Generative Models (Y. Ma, S. Shoushtari, and U. S. Kamilov)
• Deep Complex Neural Networks for Imaging (M. Yu, W. Gan, and U. S. Kamilov)

• Detecting multiple myeloma using deep image analysis (J. Hu, F. Sun, and U. S. Kamilov)
• Deep learning for diagnosis of post-traumatic elbow disease (H. T. James, W. Gan, and U. S. Kamilov)
• Deep learning for the next-generation microscopy (X. Chen, J. Liu, and U. S. Kamilov)
• Learning strategies for MRI reconstruction (H. Gao, W. Gan, and U. S. Kamilov)
• Memory and computationally efficient deep learning (Z. Zou, J. Liu, and U. S. Kamilov)
• Memory-efficient deep image restoration (J. Zeng, X. Xu, and U. S. Kamilov)
• Inhomogeneity correction in quantitative brain MRI (E. Chandler, X. Xu, and U. S. Kamilov)
• Deep learning for calf muscle and fat measurement (Y. Luo, X. Xu, and U. S. Kamilov)

• Regularization by artifact removal for PET (J. Choi, W. Gan, and U. S. Kamilov)
• Deep learning for diagnosis of post-traumatic elbow disease (Y. Yuan, W. Gan, M. David, S. Lake, and U. S. Kamilov)
• Implementing optimization algorithms in Jupyter Notebook (X. Huang and U. S. Kamilov)
• Deep learning for abdominal visceral and subcutaneous fat measurement (T. Lee, X. Xu, P. Commean, and U. S. Kamilov)
• Deep learning for calf muscle and fat measurement (Y. Luo, X. Xu, P. Commean, and U. S. Kamilov)
• Synthesizing high-resolution videos using deep learning (W. Shangguan, Y. Sun, and U. S. Kamilov)
• Asynchronous plug-and-play priors with theoretical guarantees (H. Qin, Y. Sun, and U. S. Kamilov)
• Learning Optimal Sampling Strategies for MRI (D. Jin, W. Gan, and U. S. Kamilov)
• Image Transformers for Computational Imaging (Z. Zhang, J. Liu, and U. S. Kamilov)
• Detecting multiple myeloma using deep image analysis (F. Sun, W. Gan, M. Shokeen, and U. S. Kamilov)
• Inhomogeneity correction in quantitative brain MRI (E. Chandler, X. Xu, and U. S. Kamilov)

• Next generation robust learning algorithms (R. Liu, Y. Sun, and U. S. Kamilov)
• Accurate calibration of coil sensitivities in MRI (Y. Hu, W. Gan, and U. S. Kamilov)
• Synthesizing high-resolution videos using deep learning (W. Shangguan, Y. Sun, and U. S. Kamilov)
• Tunable deep learning for computational imaging (A. Zhou, X. Xu, and U. S. Kamilov)
• Low-complexity learning for large-scale imaging (Z. Wang, J. Liu, and U. S. Kamilov)
• Deep learning for diagnosis of post-traumatic elbow disease (Y. Yuan, W. Gan, M. David, S. Lake, and U. S. Kamilov)
• Deep learning for quantitative brain imaging (E. Chandler, X. Xu, and U. S. Kamilov)

• Deep Learning for quantitative brain imaging (E. Chandler, X. Xu, Y. Sun, and U. S. Kamilov)
• Cycle GAN for quantitative MRI (Y. Li, X. Xu, and U. S. Kamilov)
• Training robust convolutional neural networks (R. Liu, Y. Sun, and U. S. Kamilov)
• Implementing CNNs with Tensorflow 2.0 (Z. Wang, Y. Sun, and U. S. Kamilov)
• Autocalibration for regularization by denoising (M. Xie, Y. Sun, and U. S. Kamilov)

• 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)
• Autocalibration for Regularization by Denoising (M. Xie, Y. Sun, and 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)
• Stochastic Algorithms using Deep Priors (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)
• 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 (Z. Wu, Y. Sun, U. S. Kamilov)
• Dictionary Learning for Fourier Ptychographic Microscopy (S. Xu, X. Xu, U. S. Kamilov)