Full-Stack MERN Developer & AI/ML Engineer
Computer Science undergraduate skilled in building robust React, Node.js, and SQL database ecosystems, with a strong passion for Artificial Intelligence, Machine Learning, and modern prompt engineering. Currently researching ML applications in medical imaging while shipping full-stack MERN products.
I'm Sudeep P, a 7th-semester Computer Science and Engineering student at Vivekananda College of Engineering & Technology, Puttur, and a Full-Stack MERN Developer skilled in building robust React, Node.js, and SQL database ecosystems. From the beginning of my engineering journey, I've been driven by curiosity — not just about how technology works, but about how it can solve real-world problems.
My interest spans across full-stack development, Artificial Intelligence, and Machine Learning. I believe in learning by building — each project I take on pushes the boundaries of what I know and challenges me to grow further, whether it's shipping a MERN application end-to-end or fine-tuning a deep learning model.
Beyond code, I hold distinction in both Drawing Grade Examinations, which reflects my eye for design, patience, and attention to detail — qualities I bring into everything I build.
I hail from Madnoor Village, Puttur Taluk, Dakshina Kannada, Karnataka — a place that taught me the value of roots, hard work, and community.
A full-stack multimodal personal AI assistant with conversational AI, voice interaction, and task automation — built to handle chat, reminders, task management, file analysis, and personalized user assistance.
A research-driven machine learning project for identifying and classifying colorectal cancer based on tissue invasion depth using medical image analysis and deep learning classification techniques. Built with CNN architectures for automated histopathology diagnostics, with a focus on low-cost, rapid diagnosis for remote healthcare settings.
An AI-powered chatbot built to answer questions specifically about Vivekananda College of Engineering & Technology — helping prospective students and parents get instant, accurate answers on admissions, courses, fees, and campus life without waiting on manual queries.
A machine learning model that predicts employee salaries from role, experience, and other workforce attributes, built with Python's data science stack.
A full-stack application designed to digitize and streamline the institutional no-due clearance process — eliminating paperwork, reducing delays, and enabling transparent multi-department approvals.
A real-time messaging platform built on the MERN stack, enabling instant conversations with a responsive, high-performance interface.
A MERN stack application for collecting, organizing, and analyzing customer feedback, turning raw responses into structured, actionable insights.
A front-end e-commerce storefront for shoes, built while learning React — focused on component structure, state management, and building a clean, browsable shopping UI.
A web-based smart parking platform that automates slot allocation, handles OTP-based authentication, and provides a complete admin analytics dashboard.
Developing responsive system components and optimizing high-performance interfaces for a live real-time chat application. Architecting backend features and structured data pipelines to streamline a robust customer feedback collection and analytics system.
Explored core machine learning concepts and applied them to real-world problem scenarios. Gained hands-on experience understanding how AI systems are designed, trained, and evaluated. This experience cemented my passion for intelligent systems and data-driven application development, providing the foundation for my ongoing research in medical image classification.
Secured first place in an advanced full-stack web engineering competition organized by the Department of AI & IEEE.
Won first place in a logic-based Swift Coding competition organized by the Department of AI & IEEE.