I am a Ph.D. Candidate in Computer Science at the University of Maryland advised by Prof. Amitabh Varshney. My research lies at the intersection of graphics, vision, and imaging. I develop machine learning algorithms for image and 3D data processing, with applications ranging from mixed reality to natural science. My goal is to enhance our ability to perceive, represent, and create information with positive societal impact.
Embedding hidden signals in NeRF renderings from arbitrary viewpoints. Opens up possibility of NeRF model and data ownership identification and protection as more and more NeRF models are exchanged and distributed online.
View interpolation without 3D reconstruction or correspondence.
Training GAN only on blurry images from a single scene to recover a sharp image without estimating the blur kernels or acquiring a large labelled dataset.
Further improved compression ability on light fields with implicit neural representations.
Interesting findings from evaluating AlphaFold2 on protein-protein docking, highlighting areas for future development using deep learning methods.
Remarkable compression rates by representing light fields as neural network weights. Simple and compact formulation also supports angular interpolation to generate novel viewpoints.
Visualizing "who's looking at who" from static profile images as people always turn off their videos. Synthesized eye gazes are obtained by leveraging a general-purpose neural network.
Training neural networks for 360° monocular depth and normal estimation. Proposed a novel approach of combining depth and normal as a double quaternion during loss computation.
Neural networks with multimodal input disproportionately rely on certain modality while ignoring the rest. Developed a new strategy to overcome such bias and adapt to missing modalities.