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Hi! I am a first-year master's student at Texas A&M, currently working as a Graduate Research Assistant in the Visual and Spatial Gradient Lab under Prof. Suryansh Kumar. Previously, I worked as a backend developer at Deloitte for two years before starting my master's. Prior to that, I earned my Bachelor's degree in Electrical and Electronics from Manipal Institute of Technology, advised by Prof. Harish Kumar J.R.. During my final year, I was also selected in Google Summer of Code (GSoC). My research interests lie at the intersection of 3D vision, Generative AI, and computer vision, with a particular focus on optical flow, stereo and depth estimation, and 3D reconstruction. Currently work with models such as Vision Transformers (ViT-16, Swin), DINO, Diffusion Models, NeRF, and 3D Gaussian Splatting. |
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Praroop Chanda, Suryansh Kumar Developing a unified model for optical flow, stereo, and depth estimation using continual learning to enhance cross-task knowledge transfer and efficiency. |
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Praroop Chanda, Rutwik Palaskar, Suryansh Kumar Extending Freestyle Layout-to-Image Synthesis (FLIS) to 3D by integrating spatial masks and multi-view consistency cues for generating coherent 3D structures or multi-view 2D renderings. |
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Praroop Chanda, J.H. Gagan, B Vaibhav Mallya, Harish Kumar J.R. IEEE INDICON, 2023 | Link Automated optic cup segmentation method using affine snakes in a gradient vector field. |
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Praroop Chanda, Ishaan Rawal Report / GitHub Optimized contrastive learning under small batch constraints using HyperOpt with ASHA scheduler. SogCLR with AdamW outperformed CLIP and CyCLIP, achieving 28.45% Mean Recall on COCO and 55.99% Top@10 accuracy on ImageNet. |
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Praroop Chanda, Rutwik Palaskar Meghana Chintalapati Report / GitHub Developed a multimodal predictive framework for calorie estimation by integrating meal images, CGM data, and Viome features. Utilized ResNet50, BERT, and cyclic encoding, training with a custom RMSRE loss function to achieve a 0.2294 score. |
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Supervised by Puneet Sharma and Pradeeban Kathiravelu Google Summer of Code (GSoC), 2022 GitHub Developed two key modules for MRI Quality Control analysis: High Contrast Spatial Resolution and Low Contrast Object Detectability. Utilized image preprocessing, NCC, multi-Otsu thresholding, and Hough Transform techniques to extract and analyze MRI phantom features. |
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Last updated: March 2025
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