Collaborated on a mental wellness companion application using MERN stack, recognized as a top project by Google Developers Club Delhi. Designed responsive UI with React and TailwindCSS, integrated Firebase analytics and MongoDB for secure data management. Prepared stress analysis techniques using sentiment analysis and mouse tracking with 92.71% accuracy.

Created custom ranking algorithm using sentence transformers achieving 85% accuracy in source ranking. Integrated WebSocket for real-time streaming responses and implemented RAG pipeline for accurate information synthesis. Utilized Tavily API for web search and Gemini API for response generation, built responsive UI with Flutter supporting cross-platform deployment.

Innovated a raw-to-dense feature pipeline, transforming raw bid data using contrastive embeddings & autoencoders, followed by an ANN-based feature compression for efficient representation. Devised a low-latency RTB model predicting bid price and bidding decisions within 5ms per request, optimized via Grid Search & Adaptive Learning Rate Scheduling. Achieved 82% classification accuracy, scaling to handle 100K+ bid requests per second in large-scale ad exchanges.

Prepared an ML-based system to identify distinct market states from high-frequency financial data. Engineered 20+ custom features from order book data, consolidated 3 clustering algorithms achieving 87% silhouette score. System successfully identified 4 distinct market regimes with 92% classification accuracy, reducing strategy drawdowns by 15% through adaptive position sizing. Analyzed 500,000+ data points across multiple timeframes to enable real-time regime classification within 50ms.