Spring 2025

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.

Advanced segmentation methods for magnetic resonsance imaging

Status: available
Description: MRI has become an indispensable tool for diagnosing degenerative diseases in the brain. However, current diagnostic process is time consuming and requires significant human involvement. The goal of this project is to develop novel segmentation tool for brain MRI scans using deep learning.
Supervision: Chicago Park (chicago@wustl.edu), Ulugbek Kamilov
Areas: Medical Imaging, Image Analysis

Fall 2024

Robust NMR spectroscopy using deep learning

Status: reserved (Naveen Asokan)
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

Advanced spectral unmixing for hyperspectral imaging

Status: reserved (Rahul Chavali)
Description: Spectral unmixing is a critical process in hyperspectral imaging that involves decomposing mixed pixel spectra into their constituent spectral signatures and abundances. This project aims to develop and implement innovative methods to enhance the accuracy and efficiency of spectral unmixing using deep learning and optimization.
Supervision: Edward Chandler (e.p.chandler@wustl.edu), Ulugbek Kamilov
Areas: Computational Miscroscopy, Deep Learning

Super-Resolution for Magnetic Resonance Imaging

Status: reserved (Eugene Joo)
Description: The spatial resolution in magnetic resonance imaging (MRI) is fundamentally related to the scan time. This project seeks to develop a machine learning method for super-resolving 3D MRI that was acquired in an accelerated fashion. This project is part of a collaboration with the WashU School of Medicine.
Supervision: Edward Chandler (e.p.chandler@wustl.edu), Ulugbek Kamilov
Areas: Medical Imaging, Deep Learning

Generative models for texture synthesis

Status: reserved (Hanson Li)
Description: Texture synthesis is the process of algorithmically constructing a large digital image from a small digital sample image by taking advantage of its structural content. The goal of this project is to design algorithms to generate textures by leveraging state-of-the-art generative models. The application will be in generation of soil textures from a sample patch. This project is done in collaboration with Prof. Tao Ju.
Supervision: Eddie Chandler (e.p.chandler@wustl.edu), Ulugbek Kamilov
Areas: Computed Tomography, Generative Models

Stabilizing Langevin dynamics for image sampling

Status: reserved (Jason Zhao)
Description: There is a growing interest in generative models based on sampling from the statistical distribution of images. This project seeks to develop stable Langevin sampling methods for image generation. Such methods can be broadly applicable accross computational imaging.
Supervision: Chicago Park (chicago@wustl.edu), Ulugbek Kamilov
Areas: Optimization, Computational Imaging

Image Registration in Digital Mammography

Status: reserved (Kyle Wolford)
Description: Mammography is an x-ray imaging method used to examine the breast for the early detection of cancer. This project seeks to develop a deep learning method for aligning two mammograms obtained over two different scans. This project is done in collaboration with the WashU School of Medicine.
Supervision: Shirin Shoushtari (s.shirin@wustl.edu), Ulugbek Kamilov
Areas: Medical Imaging, Deep Learning

Robust diffusion inverse solvers for computational imaging

Status: reserved (Kevin Kotzbauer)
Description: Diffusion models are powerful tools for various computational imaging tasks. The goal of this project is to improve these models by making them more robust to changes in the imaging systems, such as different noise levels and possible calibration errors. We seek to develop adaptive models that will dynamically adjust to correct these mismatches.
Supervision: Shirin Shoushtari (s.shirin@wustl.edu), Ulugbek Kamilov
Areas: Generative Models, Computational Imaging

Generative AI for Art

Status: reserved (David Chen)
Description: There has been rapid progress in Generative AI technology accross many applications. The goal of this project is to develop a new tool to generate images of certain artistic style. The project is in collaboration with the WashU School of Design & Visual Arts.
Supervision: Shirin Shoushtari (s.shirin@wustl.edu), Ulugbek Kamilov
Areas: Generative Models, Computational Art

Advanced Image Segmentation of Placenta

Status: reserved (Daedalus Chen)
Description: Imaging Placenta is essential for ensuring healthy pregnancy. This project seeks to develop deep learning methods for automatically segmenting placenta in order to understand its functioning. This project is done in collaboration with specialists focused on maternal health.
Supervision: Yuyang Hu (h.yuyang@wustl.edu), Ulugbek Kamilov
Areas: Medical Imaging, Deep Learning

Self-supervised Learning for MRI

Status: reserved (Albert Peng)
Description: Training deep neural networks usually requires high-quality ground truth images. However, such images are sometimes unavailable in MRI. The goal of this project is to develop methods for enabling training deep models without ground truth.
Supervision: Yuyang Hu (h.yuyang@wustl.edu), Ulugbek Kamilov
Areas: Medical Imaging, Deep Learning

Generative Models for Computational Imaging

Status: reserved (Harry Gao)
Description: There is a growing interest in developing Generative AI models for computational imaging. This project seeks to develop a new type of Gen AI approach that requires less training data. We will explore applications in medical imaging.
Supervision: Weijie Gan (weijie.gan@wustl.edu), Ulugbek Kamilov
Areas: Generative Models, Medical Imaging

Deep Learning for Blind Inverse Problems

Status: reserved (Haoyu Zhang)
Description: Blind inverse problems arise when the imaging instruments have uncertainty in their parameters. This project seeks to extend the recent method developed in CIG that can solve blind inverse problems in image restoration and medical imaging.
Supervision: Weijie Gan (weijie.gan@wustl.edu), Ulugbek Kamilov
Areas: Deep Learning, Medical Imaging

Diffusion Models for Medical Image Synthesis

Status: reserved (David Wang)
Description: Diffusion models are state-of-the-art generative models. This project will explore their application in medical image synthesis, where the goal is to be able to generate diverse set of images using diffusion models.
Supervision: Weijie Gan (weijie.gan@wustl.edu), Ulugbek Kamilov
Areas: Generative Models, Medical Imaging

Posterior Sampling using Diffusion Models

Status: reserved (Anqi Li)
Description: There is a growing interest in posterior sampling techniques to generate images. This project will explore new techniques for posterior sampling based on diffusion models. We will apply these techniques in computational imaging.
Supervision: Weijie Gan (weijie.gan@wustl.edu), Ulugbek Kamilov
Areas: Generative Models, Computational Imaging

Optimal Restoration Operators for Computational Imaging

Status: reserved (Yusheng Tan)
Description: The recent work by CIG has shown the potential of deep restoration operators for forming high-quality images. This project will explore the extension of this line of work by considering optimality of corresponding restoration operators.
Supervision: Weijie Gan (weijie.gan@wustl.edu), Ulugbek Kamilov
Areas: Deep Learning, Computational Imaging

Using Diffusion Models as Image Priors

Status: reserved (Harry Mellow)
Description: Diffusion models are extensively used for image generation. The goal of this project is to explore their ability to represent statistical distribution of images for other tasks such as image reconstruction. The goal is to use a pre-trained diffusion model with no or minimal re-training.
Supervision: Flora Sun (zhixin.sun@wustl.edu), Ulugbek Kamilov
Areas: Computational Imaging, Generative Models

Implicit Neural Representations for MRI

Status: reserved (Jianan Wu)
Description: Implicit neural representations (INRs) use a coordinate-based neural networks to represent images. This project seeks to use INRs as a method to perform data-driven compression of medical images.
Supervision: Yuanhao Wang (yuanhao@wustl.edu), Ulugbek Kamilov
Areas: Medial Imaging, Deep Learning

Spring 2024

  • 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)

Spring 2023

  • 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)

Fall 2022

  • 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)

Spring 2022

Fall 2021

  • 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)

Spring 2021

Fall 2020

Spring 2020

  • 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)

Fall 2019

  • 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)

Spring 2019

  • 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)

Fall 2018

  • 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)