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shreyanshu09/README.md

Hi there, Shreyanshu Bhushan here 👋🏼👨🏻‍💻

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As an AI Researcher, I am deeply passionate about developing and applying diverse algorithms to tackle real-world challenges. My primary research areas include Natural Language Processing (NLP), particularly with Large Language Models (LLMs/sLLM), computer vision (image processing), and Vision-Language Models (VLMs). I focus on creating compact models that work efficiently on-device and offline.

I have worked extensively on Document AI, focusing on processing documents that contain not only text but also images, block diagrams, charts, and tables. I convert these documents into textual or structured data formats, enhancing both accessibility and interpretability. Additionally, I have contributed to projects that involve file translation while preserving the original layout, a task that combines both NLP and computer vision techniques to ensure seamless file transformation.

My academic background includes a Master of Science degree in Artificial Intelligence from Kyungpook National University, South Korea. During my tenure at the university, my research efforts were concentrated on the formulation and implementation of algorithms pertinent to natural language processing (NLP) and computer vision (CV).

I am excited to utilize my skills and experience to make a positive impact on the world through AI.

  • 💬   Ask me about anything! I am happy to help.
  • 📫   How to reach me: [shreyanshubhushan@gmail.com] LinkedIn
  • 😄   Learned a lot from the open-source community and i love how collaboration and knowledge sharing happened through open-source.
  • 👾   By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.

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  1. BlockNet BlockNet Public

    Code and dataset introduced in the paper: "Unveiling the Power of Integration: Block Diagram Summarization through Local-Global Fusion" (ACL 2024).

    Jupyter Notebook 4

  2. BD-EnKo BD-EnKo Public

    Dataset and code introduced in the paper: "Unveiling the Power of Integration: Block Diagram Summarization through Local-Global Fusion" (ACL 2024).

    Jupyter Notebook 2

  3. Block-Diagram-Datasets Block-Diagram-Datasets Public

    Block Diagram datasets used in the paper: "Block Diagram-to-Text: Understanding Block Diagram Images by Generating Natural Language Descriptors"

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  4. Network-Intrusion-Detection-System-using-Machine-Learning Network-Intrusion-Detection-System-using-Machine-Learning Public

    In this implementation, presented the performances of different classifier algorithms of machine learning to identified risks speedily and accurately.

    Jupyter Notebook 6 1

  5. CIFAR-10 CIFAR-10 Public

    A Complete Study on Image Classification on CIFAR-10 Dataset Using DeepLearning

    Jupyter Notebook 1

  6. Semantic-Segmentation-Cityscapes Semantic-Segmentation-Cityscapes Public

    Semantic Segmentation on Cityscapes Dataset Using SegNet, U-Net, and FCN: A Study Approach

    Jupyter Notebook 2 1