Welcome to my GitHub! I'm a Data Science Master's student at Stony Brook University, passionate about Machine Learning, Big Data, and AI-driven Solutions. I enjoy exploring advanced technologies and applying them to solve real-world problems. My interests lie in Building Scalable Systems, Experimenting with Transformers, and Data-Driven Innovation.
- 🌱 Currently Learning: LLM models, RAG, LLM Agents, Transformer models, distributed computing, and advanced deep learning architectures.
- 🔭 Current Focus: Working on cutting-edge projects like long-context transformers, RAG models, and scalable cloud solutions.
- 👯 Looking to Collaborate: On open-source projects related to AI, machine learning, and data engineering.
- 💬 Ask Me About: Natural Language Processing (NLP), cloud-based computation, and end-to-end ML workflows.
- ⚡ Fun Fact: I'm a basketball enthusiast and a fan of Michael Jordan, LeBron James, and Stephen Curry.
- Description: A clustering algorithm applied to segment customers for targeted marketing.
- Tech Stack: Python, scikit-learn, Matplotlib.
- Description: Enhanced document understanding by integrating long-context transformers with RAG models.
- Tech Stack: HuggingFace Transformers, PyTorch, Google Colab.
- Description: Simplified ordering and payment system for restaurants using QR-based technology.
- Tech Stack: JavaScript, Firebase, Node.js.
- Description: Implemented MapReduce to compute customer spending totals from a large dataset using distributed computing.
- Tech Stack: Python, Hadoop, AWS EMR.
- Description: Applied Principal Component Analysis (PCA) for dimensionality reduction on the USPS handwritten digit dataset and reconstructed images.
- Tech Stack: Python, NumPy, Matplotlib.
- Description: Built a feed-forward neural network to predict part-of-speech tags for English tweets using skip-gram word embeddings.
- Tech Stack: Python, PyTorch, Word2Vec.
- Description: Implemented binary logistic regression using a partial MNIST dataset for digit classification, visualizing decision boundaries and training curves.
- Tech Stack: Python, NumPy, Matplotlib.
