My Projects

Developed an advanced analysis pipeline for gravitational wave detection data, implementing machine learning algorithms to identify potential signals in noisy detector data.

• Designed specialized neural network architectures for detecting faint gravitational wave signals in LIGO data
• Implemented time-frequency analysis techniques to characterize signal properties
• Achieved 94% accuracy in detecting simulated gravitational wave events
• Created interactive visualizations to explore detection results and waveform characteristics

Created an interactive web-based dashboard for exploring and analyzing anime ratings, popularity trends, and genre preferences across different demographics.

• Scraped and processed data from MyAnimeList using Python, creating a dataset of 10,000+ anime titles
• Developed interactive visualizations for exploring trends in genre popularity over time
• Implemented recommendation features based on user preferences and viewing history
• Deployed the application using Streamlit Cloud for public access

Engineered a system to extract insights from unstructured financial PDFs using transformer-based LLMs fine-tuned on domain-specific data.

• Utilized OCR and PDF parsing to extract textual content from scanned financial statements
• Leveraged fine-tuned BERT and GPT-4 models to summarize and answer questions on earnings reports
• Built an RAG (Retrieval-Augmented Generation) pipeline with Pinecone vector search and LangChain
• Deployed system with fastAPI and integrated GPT-based feedback loops to improve accuracy

• Implemented collaborative filtering and content-based recommendation algorithms
• Designed a real-time feature engineering pipeline using Kafka and Spark Streaming
• Optimized model serving with Redis caching to achieve 50ms average response time
• Deployed using containerized microservices architecture on AWS ECS