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fatemafaria142/README.md

Hi there 🙋‍♀️
I am Fatema Tuj Johora Faria, currently working as an Application Developer (AI/ML) at Dexian (Bangladesh) Limited. In my professional role, I focus on designing, developing, and deploying generative AI-based applications that address real-world challenges across diverse industries. My expertise includes building proof-of-concept prototypes, architecting intelligent pipelines, and translating cutting-edge research into production-ready solutions. I obtained my Bachelor's degree in Computer Science and Engineering from Ahsanullah University of Science and Technology.

Primary Research Interests 🎯

I am primarily interested in the following areas, where I actively engage in research and development:

◉ Large Language Models (LLMs)
◉ LLM Agents
◉ Large Multimodal Models (LMMs)
◉ NLP for Social Good
◉ NLP for Low-Resource Languages
◉ AI in Healthcare
◉ Vision-Language Models (VLMs)
◉ Trustworthy AI
◉ Multimodal Agents
◉ Large Vision Models (LVMs)
◉ Computer Vision

Profile Views

Connect with me 🌐

Technical Skills 🧰

🔹 Programming Languages: Python (NumPy, SciPy, Matplotlib, Pandas, Seaborn), Java, C++
🔹 Web Development: HTML5, CSS3, JavaScript, FastAPI, Flask, React JS, Streamlit
🔹 Database: MySQL, MongoDB
🔹 Deep Learning Frameworks: TensorFlow, Keras, PyTorch
🔹 LLM Application Frameworks: LangChain, LangGraph, LangSmith, LlamaIndex, DeepEval, CrewAI
🔹 Cloud Services: Azure OpenAI, Azure SQL Database, Azure App Service, Azure Blob Storage
🔹 Others: Vector Database, Apache Airflow, Docker, OpenCV, GitHub, GitHub Copilot

Favorite Quote ✨

"The future of AI is not about creating machines that think like humans, but about building systems that learn from data and improve over time."
— Geoffrey Hinton

Github Stats 📊

GitHub Streak

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  1. MultiBanFakeDetect-An-Extensive-Benchmark-Dataset-for-Multimodal-Bangla-Fake-News-Detection MultiBanFakeDetect-An-Extensive-Benchmark-Dataset-for-Multimodal-Bangla-Fake-News-Detection Public

    This study introduces MultiBanFakeDetect, a novel multimodal dataset for Bangla fake news detection, combining textual and visual information. It features TextFakeNet for text analysis and MultiFus…

    Jupyter Notebook 4 2

  2. SentimentFormer-A-Transformer-Based-Multi-Modal-Fusion-Framework-for-Sentiment-Analysis-of-Memes SentimentFormer-A-Transformer-Based-Multi-Modal-Fusion-Framework-for-Sentiment-Analysis-of-Memes Public

    This research developed a multimodal sentiment analysis framework for Bengali memes using the MemoSen dataset, leveraging both text and image data. It introduces SentimentFormer, which employs Ear…

    Jupyter Notebook 1 1

  3. BanglaCalamityMMD-A-Comprehensive-Benchmark-Dataset-for-Multimodal-Disaster-Identification BanglaCalamityMMD-A-Comprehensive-Benchmark-Dataset-for-Multimodal-Disaster-Identification Public

    Forked from Mukaffi28/BanglaCalamityMMD-A-Comprehensive-Benchmark-Dataset-for-Multimodal-Disaster-Identification

    This study presents a novel multimodal fusion technique for disaster identification in Bangla, combining text and image data using the "BanglaCalamityMMD" dataset. Employing DisasterTextNet, Disast…

    Jupyter Notebook

  4. Retinal-Fundus-Classification-using-XAI-and-Segmentation Retinal-Fundus-Classification-using-XAI-and-Segmentation Public

    This research enhances early disease diagnosis by analyzing retinal blood vessels in fundus images using deep learning. It employs eight pre-trained CNN models and Explainable AI techniques.

    Jupyter Notebook 7 3

  5. Large-Language-Models-Over-Transformer-Models-for-Bangla-NLI Large-Language-Models-Over-Transformer-Models-for-Bangla-NLI Public

    This research examines the performance of Large Language Models (GPT-3.5 Turbo and Gemini 1.5 Pro) in Bengali Natural Language Inference, comparing them with state-of-the-art models using the XNLI …

    Jupyter Notebook 3 1

  6. Vashantor-A-Large-scale-Multilingual-Benchmark-Dataset Vashantor-A-Large-scale-Multilingual-Benchmark-Dataset Public

    Forked from Mukaffi28/Vashantor-A-Large-scale-Multilingual-Benchmark-Dataset

    This study addresses the gap in translating Bangla regional dialects into standard Bangla by creating a large-scale multilingual benchmark dataset of 32,500 sentences in Bangla, Banglish, and Engli…

    Jupyter Notebook