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.

Spatial, Temporal, and Spectral Superresolution in Microscopy

Status: available Description: There is a growing interest in using deep learning to improve the resolution of various bio-microscopy systems. The goal of this collaborative project is to explore the potential of resolution enhancement in microscopy jointly along spatial, temporal, and spectral dimensions. This project will be conducted in collaboration with Prof. Shankar Mukherji from Physics. Supervision: Yuyang Hu (h.yuyang@wustl.edu), Ulugbek Kamilov Areas: Computational Microscopy, Deep Learning

Video Restoration in Two-Photon Microscopy

Status: available Description: Two-photon microscopy (TPM) is a fluorescence imaging technique that allows imaging of living tissue up to about one millimeter in thickness. The fundamental challenges in creating high-quality videos using TPM are the noise and motion artifacts. In this collaborative project with the NeuroPhoto Lab, we want to develop video restoration algorithms for TPM using machine learning (ML). Supervision: Weijie Gan (weijie.gan@wustl.edu), Shengxuan Chen, Joe Culver, Ulugbek Kamilov Areas: Computational Microscopy, Deep Learning

Noise Adaptive Deep Denoisers for Imaging

Status: reserved (Emma McMillian) Description: The goal of image restoration is to recover a high-quality image from a noisy lower-quality image. One traditional approach is to train a deep neural network to map low-quality image to a high-quality one. However, such networks are not usually not flexible enough to address multiple noise levels simultaneously. The goal of this project is to explore noise-adaptive deep denoisers that can work at multiple noise levels. Supervision: Yuyang Hu (h.yuyang@wustl.edu), Shirin Shoushtari, Ulugbek Kamilov Areas: Image Restoration, Deep Learning

Interactive Digit Recognition for ML Education

Status: reserved (Jason Ti) Description: The goal of this project is to develop an interactive machine learning (ML) program for recognizing handwritten numbers within a 16×16 grid. The program will load the images from the web-cam, analyze them using ML, and finally display the feedback to the users. This tools will be used as part of a summer educational program on computational imaging for high-school students. Supervision: Flora Sun (zhixin.sun@wustl.edu), Ulugbek Kamilov Areas: Image Analysis, Deep Learning

Detecting Multiple Myeloma using Deep Image Analysis

Status: reserved (Xiaonan Yang) Description: Multiple myeloma (MM) is a devastating form of cancer. The development of imaging tools for MM is an active area with the potential to enable breakthroughs in its diagnosis and treatment. CIG is looking for a motivated student to develop new deep learning methods in the context of our MM collaboration with the researchers at the School of Medicine. Supervision: Flora Sun (zhixin.sun@wustl.edu), Ulugbek Kamilov Areas: Image Analysis, Deep Learning

Deep Learning for 3D Quantitative Brain MRI

Status: reserved (Kerri Prinos) Description: Quantitative magnetic resonance imaging (qMRI) is a class of imaging techniques based on estimating quantitative tissue parameters from MRI signals. There is a growing interest in using deep learning (DL) for qMRI, however, a key challenge in this context is the computational and memory complexity of DL. The goal of this project is to develop efficient 3D qMRI DL methods. Supervision: Yuyang Hu (h.yuyang@wustl.edu), Ulugbek Kamilov Areas: Medical Imaging, Deep Learning

Machine Learning Biomarkers for Fetal Growth Restrictions

Status: reserved (Zezhen Xiang) Description: One primary cause of stillbirth is fetal growth restriction (FGR), in which a maldeveloped placenta is insufficient to sustain the needs of the fetus. CIG is collaborating with the pregnancy specialists to develop machine learning (ML) biomarkers based on imaging data for predicting placental growth. The project will involve the development of algorithms for analyzing ultrasound and optical coherence tomography (OCT) images of placenta. Supervision: Yuyang Hu (h.yuyang@wustl.edu), Ulugbek Kamilov Areas: Image Analysis, Deep Learning

Deep Learning for Digital Mammography

Status: reserved (Eddy Sul) Description: The goal of this project is build on the recent progress on deep learning to enable new image processing tools for digital mammography. We will explore tools to facilitate the analysis of mammograms in order to better predict the risk of breast cancer. Supervision: Shirin Shoushtari (s.shirin@wustl.edu), Ulugbek Kamilov Areas: Deep Learning, Medical Imaging

Deep Learning for the Next-Gen Scientific Imaging

Status: reserved (Junyi Yao and Wenxuan Zhang) Description: Deep learning (DL) has the potential to transform scientific imaging modalities—such as neutron scattering, atomic force microscopy, and magnetic resonance spectroscopy—by accelerating data collection, reducing noise, and enabling automatic analysis. We are seeking motivated students for an exciting collaboration with the BioProducts Engineering Lab on developing state-of-the-art DL algorithms for scientific imaging that can lead to unprecedented insights into materials. Supervision: Shirin Shoushtari (s.shirin@wustl.edu), Marcus Foston, Ulugbek Kamilov Areas: Scientific Imaging, Deep Learning

Deep learning for diagnosis of post-traumatic elbow disease

Status: reserved (Vincent Siu) Description: Deep learning may revolutionize the study of musculoskeletal conditions by automating the evaluation pipeline. This project seeks to develop tissue and modality-specific DL algorithms for automatic evaluation of disease stage and explore multi-modal in post-traumatic elbow conditions using histology, computed tomography, and magnetic resonance imaging. This project is in collaboration with the Musculoskeletal Soft Tissue Laboratory. Supervision: Weijie Gan (weijie.gan@wustl.edu), Ulugbek Kamilov Areas: Image Analysis, Deep Learning

Deformation-Compensated Learning

Status: reserved (Chicago Park) Description: The goal of this project is to explore self-supervised deep learning under object motion. Recently, CIG has developed Deformation Compensated Learning (DeCoLearn) as a novel technique for such scenarios. The goal of this project is to extend DeCoLearn to be more applicable in medical imaging. Supervision: Weijie Gan (weijie.gan@wustl.edu), Ulugbek Kamilov Areas: Deep Learning, Medical Imaging

Continuous Regualarier for Inverse Problems

Status: reserved (Nan Huang) Description: Traditional image regularization techniques are based on discrete-domain representation of images. However, before being discretized, images are continuous-domain objects. The goal of this project to explore continous regularization strategies based on deep learning. Supervision: Weijie Gan (weijie.gan@wustl.edu), Ulugbek Kamilov Areas: Image Processing, Deep Learning

Breast Cancer Risk Assessment using Machine Learning

Status: reserved (Chaoye Jin) Description: Breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. Machine learning has the potential to predict breast cancer based on features hidden in data. The goal of this project is to develop machine learning models for predicting breast cancer risk in several populations. Supervision: Shirin Shoushtari (s.shirin@wustl.edu), Ulugbek Kamilov Areas: Medical Imaging, Deep Learning

Robust deep learning for computational imaging

Status: reserved (Eddie Chandler) Description: Robustness is an important consideration when designing computational imaging methods. The design of robust methods can be formulated as an optimization problem that exploits recent ideas emerging in machine learning. CIG is seeking a motivated student interested in designing robust machine learning methods for computational imaging. Supervision: Jiaming Liu (jiaming.liu@wustl.edu), Ulugbek Kamilov Areas: Deep Learning, Large-Scale Optimization

Efficient and effective data-driven image representations

Status: reserved (Harry Gao) Description: There is a growing interest to efficiently represent images using deep neural networks. Unlike traditional methods based on fixed transformations, neural networks are data adaptive. This project explores this topic for representing 2D or 3D medical imaging data. Supervision: Weijie Gan (weijie.gan@wustl.edu), Ulugbek Kamilov Areas: Medical Imaging, Deep Learning

Deep learning for the next-generation microscopy

Status: reserved (Yubo Wang) Description: Lightsheet microscopy has had an impact on fields as diverse as developmental and cell biology, anatomical science, biophysics and neuroscience. The goal of this collaborative project is to develop a new image restoration, processing, and analysis pipeline for light-sheet microscopy based on deep learning. Supervision: Jiaming Liu (jiaming.liu@wustl.edu), Ulugbek Kamilov Areas: Computational Microscopy, Deep Learning

• 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)