AI Research Developer & Software Engineer passionate about building intelligent systems and scalable applications
I'm currently pursuing my Master's in Computer Science at the University of Colorado, Boulder, while working as an AI Research Developer at the Leeds School of Business. With a strong background in full-stack development and machine learning, I specialize in creating intelligent systems that solve real-world problems.
My experience spans from developing enterprise-scale applications at Wells Fargo to building cutting-edge AI-powered platforms. I'm passionate about leveraging technology to create meaningful impact, whether it's through intelligent conversation orchestration or predictive modeling systems.
Leeds School of Business, CU Boulder
Wells Fargo
Penzigo Technology
Comprehensive full-stack platform with 6 specialized AI agents, PostgreSQL database, and AI-powered visa interview simulator.
A sophisticated movie recommendation system that visualizes movie relationships as interactive constellation graphs using machine learning algorithms.
Turn your repository into an AI agent. Chunk code, query it via chat, generate tests, and push intelligent updates.
ML-driven platform achieving 82% accuracy for wildfire risk assessment with NASA FIRMS integration.
Image-based Q&A model combining LSTM and VGG19 with multi-layer feature fusion and stacked attention.
Wallet-based mobile application designed for seamless online and offline transactions, reducing payment failures in low connectivity areas. Published in SN Computer Science 2022.
Aug 2024 - Present
Master of Science in Computer Science
GPA: 3.94/4.0
Boulder, CO
Aug 2018 - May 2022
Bachelor of Technology in Information Technology
GPA: 3.4/4.0
Karnataka, India
I'm always interested in discussing new opportunities, innovative projects, or just connecting with fellow developers. Whether you have a question or just want to say hi, feel free to reach out!
Phone
(720) 736-0799Location
Boulder, Colorado
GradCompass is an AI-powered graduate application assistant built with React 19, FastAPI, and Google Gemini AI that guides students through their entire grad school journey via 5 specialized AI agents. The platform features real-time WebSocket-powered mock visa interviews, intelligent university matching, and document assistance - all backed by async PostgreSQL operations and LangGraph workflow orchestration. Think of it as having a personal admissions counselor who never sleeps, powered by modern full-stack architecture that makes the overwhelming world of graduate applications actually manageable. Built with the kind of tech stack that makes both users and developers happy.
Cine-Stellation is an interactive movie recommendation system that visualizes movie relationships as constellation graphs using machine learning algorithms. Built with Next.js frontend and FastAPI backend, it leverages TF-IDF vectorization and cosine similarity to identify movie connections based on content and user preferences. The system features real-time plot-based search, genre filtering, user authentication with MongoDB persistence, and force-directed graph visualizations rendered on HTML5 Canvas. Users can explore movie recommendations through an immersive space-themed interface while tracking their personal watchlists and discovering new films through intelligent similarity matching.
Talk to your Repo is an AI-powered code assistant that enables natural language conversations with GitHub repositories using a modern full-stack architecture. Built with FastAPI (Python backend) and React (frontend), it leverages Google Gemini AI for advanced code embeddings and semantic understanding, automatically cloning and indexing repositories into searchable chunks. Users can ask questions about code functionality, structure, and implementation through an intuitive chat interface, receiving intelligent responses with precise source citations, file references, and real-time processing status updates. The system features vector similarity search for relevant code discovery, file tree visualization, conversation history, and intelligent filtering that processes only relevant code files while maintaining context across multiple queries.
This project developed an integrated wildfire risk prediction system for Queensland, Australia using multi-source data from NASA FIRMS (satellite fire detection) and Open Meteo weather APIs covering 2021-2024. The team implemented and compared seven machine learning models including XGBoost, LSTM, Random Forest, and SVM using features like temperature, humidity, soil conditions, wind speed, and precipitation. LSTM achieved the best performance with 82% accuracy and 90% recall, while XGBoost delivered strong balanced metrics at 80% accuracy. The system is designed for real-time wildfire occurrence prediction and early warning applications.
This project develops a Visual Question Answering system that combines Stacked Attention Networks with Multi-Layer Feature Fusion to improve image understanding. The system uses VGG-19 for image feature extraction and LSTM for question processing, but introduces a novel approach of fusing features from multiple CNN layers (rather than just the final layer) using addition and concatenation operators. The stacked attention mechanism helps reduce noise and focus on image regions relevant to the question, while multi-layer feature fusion preserves important information that would otherwise be lost in deeper layers. The approach achieves 63.72% accuracy on the VQA dataset, outperforming baseline methods including vanilla VQA (57.58%) and single attention mechanisms (59.8%).
This project develops a mobile wallet payment application using React Native, Node.js, and MongoDB that enables secure transactions in both online and offline environments. The system uses AsyncStorage for offline transaction storage, WebSockets for secure online communication, and bcrypt for password encryption, addressing connectivity issues in areas with poor internet access. Key technical features include document-level database locking for concurrency control and automatic synchronization of offline transactions when connectivity returns. Performance testing showed 600ms average transaction times, outperforming existing wallet applications that typically require 700-800ms. Published in SN Computer Science (2022).