Experience
- Manager: Dr. Soheil Darabi , Mentor: Dr. Taesung Park
- Improved the performance of StyleGAN-based generative models for conditional image editing tasks like portrait relighting.
- Working on improving the inference time and memory efficiency of Diffusion models.
- Advisors: Prof. Nicholas Burris , Prof. Jeff Fessler
- Built faster and better 3D segmentation and keypoint detection neural networks for automatically measuring aortic growth.
- Our method greatly reduces the diagnosis time (100x) to detect thoracic aortic aneurysms.
- This work will be presented as an Oral talk at the Radiological Society of North America (RSNA) meet in Chicago.
- Manager: Nilesh Jain
- Investigated various neural video compression algorithms and built a modular codebase that supports the plug and play usage of typical encoders, decoders and entropy models used in neural codecs.
- Performed a comparative analysis (from a systems perspective) of ScaleSpaceFlow and WaveOne ELF-VC, two state-of-the-art end-to-end learned video codecs, both of which were reproduced using the codebase developed.
- Advisors: Dr. Luis Herranz , Dr. Joost van de Weijer
- Developed a mathematical framework for studying neural networks which are trained or tested with compressed images for use cases in distributed autonomous driving data collection.
- Designed dataset restoration, a principled algorithm motivated by the aforementioned framework, that utilizes conditional GANs to mitigate the drop in performance by 10-50% when compressed images are used for training in place of uncompressed images. Katakol et al., IEEE TIP ‘21
- Developed techniques for few-shot adaptation ($\approx$ 20% improvement wrt baselines) and continual learning of learned image compressors in collaboration with BBC R&D. Katakol et al., CVPR-W ‘21
- Advisors: Prof. Ashwin Srinivasan and Dr. Lovekesh Vig
- Improved existing techniques for representation learning of Chest X Rays by developing a specialized loss involving a weighted square loss component and a multi-instance learning based detection loss component.
- The usage of this loss led to better representations that capture the intricate details in the X-rays and ultimately resulted in improved detection accuracies when the learnt representations are transferred to a new task.
- Manager: Amit Pandey
- Built a miniature Meta Search Engine, a system which refines the results of popular search engines according to the expected needs of middle/high school students, using document embeddings and clustering. Github
