I research and develop Machine Learning systems in the domain of Computer Vision; using Convolutional Neural Networks with Transformers.
I'm a science enthusiast with a passion for Computer Science, Mathematics, History, and Philosophy. I spend my time exploring new ideas and concepts that catch my interest.
During the day, I work with numbers, writing programs and solving mathematical puzzles using Neural Networks. In my downtime, I'm a movie buff who enjoys discussing films of all genres. If you're a movie lover too, let's chat!
I also have a creative side—I write poems and essays, and I love connecting with people through public speaking. If you're curious to learn more about me, click the button below.
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Developing a robust multi-label classification model employing Self-Supervised Learning to accurately identify various findings and diseases within chest X-ray images. Utilizing a combination of CNNs and a Mask Auto Encoder - Vision Transformer (ViT) based model to improve the AUROC score of the prediction and classification model.
Additionally, incorporating Contrastive CLIP loss for generating detailed reports.
Developing a deep learning model that uses CNNs and Transformers to process sequences of image frames in videos, applied in multi-task video processing like handling frame breakages, next frame prediction, and object tracking.
Particularly within the context of Nepal's unique dataset, I developed a CNN model which classifies the leaf of a plant to different classes of disease.
To achieve this, I employed Meta's Segment Anything Model (SAM) for dataset generation. For classification, I utilized the EfficientNet architecture and trained the model in a Domain Adversarial Setup, ensuring robust classification with domain-invariant feature vectors from the available standard Plant Village dataset and the collected dataset.
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In a nutshell, I specialize in crafting cutting-edge computer vision AI systems, and I've got quite the knack for design too. Dive into my CV to discover more about my accomplishments, skills, and the journey that got me here. Let's connect!"
