🚀 Master’s in Computer Science & Engineering @ Santa Clara University, California
💡 Passionate about Scalable Architectures, Software Optimization, and AI
🎯 Experienced in tackling complex challenges and building user-centered solutions
🌎 Open to collaborations in AI, Cloud Computing, and Full-Stack Development

• 🎖 AWS Certified Solutions Architect - Professional
• 🎖 Microsoft Certified: Azure Fundamentals
• 📄 Published IEEE Paper (2020): Machine Learning-Based Selection of Optimal Cricket Team Based on Players’ Performance

🚀 Overview
This project focuses on predicting the future performance of MLB players (prospects) using Google Cloud AI (Vertex AI AutoML). It utilizes historical home run data, player stats, and performance metrics to predict Wins Above Replacement (WAR) based on Exit Velocity, Hit Distance, and Launch Angle.

💡 Key Features
✅ Full-stack web app using Next.js + Tailwind CSS for seamless user interaction
✅ AutoML Model trained using Google Cloud Vertex AI
✅ Cloud-based deployment for real-time predictions

🛠 Tech Stack

• Programming: Python, JavaScript (React.js, Next.js)
• Cloud: Google Cloud Platform (GCP)
• ML Frameworks: Vertex AI AutoML
• Frontend: Next.js, Tailwind CSS
• Backend: Node.js
• Storage: Google Cloud Storage (GCS)

🔗 Project Repo

🚀 Overview
A decentralized peer-to-peer (P2P) file-sharing system that enables secure and efficient file transfers without relying on a central server. The project integrates key distributed system concepts such as gossip protocols, leader election, fault tolerance, and stabilization mechanisms.

💡 Key Features
✅ Decentralized architecture with dynamic peer connections
✅ Gossip Protocol for propagating peer information
✅ Leader Election using Ring Algorithm
✅ Efficient file chunking and transfer
✅ Fault tolerance with auto-reassignment of lost files

🛠 Tech Stack

• Language: C++
• Networking: Boost.Asio for efficient peer communication
• Concurrency: Multithreading for scalable operations

🔗 Project Repo

🚀 Overview
An AI-powered learning tool designed to help users master complex topics with ease. Using Snowflake Cortex AI and Mistral LLM, it provides customized learning experiences through summaries, guides, FAQs, and quizzes.

💡 Key Features
✅ Personalized learning paths (Beginner, Intermediate, Expert)
✅ Semantic search for retrieving relevant educational material
✅ AI-powered quiz generation
✅ Interactive UI with Streamlit

🛠 Tech Stack

• AI Models: Snowflake Cortex AI, Mistral LLM
• Frontend: Streamlit
• NLP: Sentence-Transformers
• Data Processing: PDFMiner for extracting text from PDFs

🔗 Project Repo

💌 Let’s connect and collaborate to create technology that makes a meaningful impact! 🚀🔥