Recent advances in imaging data acquisition and the momentum in modern machine intelligence have led to exciting research into exploiting the power of images and solving problems that cannot be solved by manual analysis. Extracting and understanding information from images requires a multifaceted paradigm that leverages the complementarity of low-level image processing and high-level vision and machine learning approaches.
My primary interest is developing theoretical foundations and computational methods for inferring semantic information from imaging data.
My research is an interdisciplinary endeavor that spans computational computer vision, medical image analysis, machine learning, and optimization. Much of my current focus is on probabilistic modeling and deep learning, and how the computational solutions that emerge in this space can enrich statistical shape analysis, subspace learning, generative image and shape modeling, and 3D shape reconstruction.
As well, I am fascinated with the implications of advances in these fields for society and industry. Meanwhile, I enjoy collaborating with scientists and domain experts of different disciplines and backgrounds to conduct interdisciplinary research projects.
- Google Scholar
I am looking for motivated and enthusiastic students who are interested in conducting advanced research in machine/deep learning, image understanding, and statistical analysis. You might want to see this announcement from more details.
Important: Please apply to the MS/PhD program in School of Computing at the University of Utah.
- New paper on generative modeling of multi-label probabilistics maps has been accepted for publication in the IEEE Transaction of Medical Imaging, 2020.
- In Fall 2019, Praful Agrawal has successfully defended his PhD dissertation. Congratulations Praful!
- New paper on nonparameteric Bayesian models for shape representation has been accepted for publication in the 34th AAAI Conference on Artificial Intelligence (AAAI), 2020.
- New paper on learning interpretable shared hidden structure across data spaces for design space analysis and exploration has been accepted for publication in ASME Journal of Mechanical Design, 2020.
- New paper on self-regularizing CNNs for image registration has been accepted for publication in the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019.
- In Fall 2019, Jadie Adams has joined my research group as a PhD student.
- In Summer 2019, Anupama Goparaju has successfully defended her master thesis. Congratulations Anu!
- In Summer 2019, Atefeh Ghanaatikashani has joined my research group as a PhD student.
- In Summer 2019, Hong Xu has joined my research group as a PhD student.
- In Fall 2018, Xiaoni Cao has joined my research group as a PhD student.
- In Fall 2018, Kyli McKay-Bishop, Oleks Korshak, and Shalin Parikh have joined my research group as master students.
- New paper on predicting statistical shape models directly from images via deep learning has been accepted in ShapeMI-MICCAI 2018 (oral).
- New paper on evaluating different statistical shape modeling tools in a clinical scenario has been accepted in ShapeMI-MICCAI 2018 (oral).
- New paper on shape and appearance modeling for automatic left atrium segmentation has been accepted in STACOM-MICCAI 2018 (poster).
- New paper on predicting atrial fibrillation recurrence from MRI images using deep learning has been accepted in CinC 2018 (oral).
- New paper on population-level interactive visualization of left atrium shape in atrial fibrillation patients has been accepted in CinC 2018 (poster).
- In Summer 2018, Archanasri Subramanian has joined my research group as a master student.
- In Spring 2018, Riddhish Bhalodia and Tim Sodergren have joined my research group as PhD students.
- In Fall 2017, Anupama Goparaju has joined my research group as a master student.
- New paper on learning deep features for correspondence models has been accepted in MICCAI 2017 (oral).
- New paper on learning mixtures of shape models has been accepted in IPMI 2017 (oral).
- New paper on Bayesian learning of shape models has been accepted in CVPR 2017 (splotlight).