Shradha Agarwal leads a dynamic research team at the University of Tennessee in strong collaboration with Oak Ridge National Laboratory that specializes in applying computer vision techniques to expedite image and video analysis. This includes building custom neural network architectures from scratch as well as fine-tuning and implementing pretrained models such as GANs, VAEs, diffusion models, Faster R-CNN, Mask R-CNN, U-Net, SSD and YOLO. The main application of this work is to accelerate nuclear material characterization and additive manufacturing process optimization by leveraging these advanced deep learning algorithms for tasks like microscopy image generation, reconstruction, detection and segmentation. Her team also develops novel approaches for model parameter tuning and domain-specific customization to best capture attributes of interest in the analysis of visual nuclear materials data. Through tailored integration of deep learning and computer vision methods, her group aims to greatly speed up research workflows reliant on imaging and image-based modeling.