I am an aspiring Machine Learning Engineer and Data Scientist who is passionate about building data-driven solutions to address real-world problems. With a background in deep learning models and a keen interest in audio classification, natural language processing, and community-focused tech initiatives, I strive to create impactful projects that blend innovation, accuracy, and scalability.
- Machine Learning & AI: I specialize in developing models for music genre classification, text-based classification, and large-scale data insights.
- Full-Stack Development: I leverage frameworks like Next.js, Node.js, and cloud-based databases to create end-to-end solutions that empower communities and drive positive social impact.
Description: This Fall AI Studio project employs a Convolutional Recurrent Neural Network (CRNN) to classify music genres from spectrograms. It highlights my expertise in deep learning architectures, data processing, and evaluation metrics.
Technologies: Python, Jupyter Notebooks, TensorFlow/PyTorch, Audio Data Processing
Key Features:
- Comprehensive README including project overview, methodology, results, and next steps
- Sample datasets and notebooks for testing
- Installation and setup instructions
- Full documentation to replicate results
Description: A collaboration to classify patent abstracts into industry-specific categories using large language models and few-shot learning. Demonstrates my experience with NLP, data preprocessing, and domain-specific model fine-tuning.
Technologies: Python, LLM APIs, Pandas, Data Preprocessing Pipelines
Description: A full-stack platform designed to foster in-person community engagement and address local social challenges through data analysis, web scraping, and geolocation services.
Technologies: Next.js, Material-UI, Node.js, PostgreSQL, Web Scraping, Geolocation APIs, Cloud Hosting