I’m Harsh Porwal, a final-year Computer Science and Engineering student at Vellore Institute of Technology, specializing in IoT. I have hands-on experience in software development through internships at Clear Demand, Samsung R&D India, and Coelentera Technologies. My work spans web development, AI integration, and research in machine unlearning. I’ve developed impactful projects like an AI-based mock interview platform and a real estate web application, leveraging technologies such as Node.js, React, Docker, Prometheus, and AWS. I’m passionate about building scalable systems, automating workflows, and solving real-world problems through technology.
SDE Intern
Highlights
Working on a web crawling tool using Node.js, focusing on data extraction and automation tasks.
Research Intern, Certificate
Highlights
Researched on machine Un-learning, analyzing current scenarios and applications.
Developed an algorithm for selective forgetting in deep neural networks using CIFAR-10.
Enhanced accuracy, achieving a 76% performance improvement.
Authored a research paper on machine unlearning methodologies.
Web Development Intern
Highlights
Developed a Generic Payment Gateway Integration for e-commerce applications using MERN stack.
Worked on both frontend and backend development to ensure seamless integration.
Technologies learned: MongoDB, Express.js, React.js, Node.js, API integration, Payment Gateway Integration.
B.Tech
Computer Science and Engineering with Spec. in IOT
Grade: 9.45/10
Courses
Operating Systems
Database Management System
Computer Networks
Data Structures & Algorithms
Sensors & Actuators
Published by
International Journal of Online and Biomedical Engineering
Summary
Examined the impact of data bias in healthcare applications on 20+ metrics, exploring its effects on data collection, processing, and model building.
NodeJS, ReactJS, NextJS, ExpressJS, Prometheus, Grafana.
Docker, GIT, Postman, PostgreSQL, MySQL, Figma.
C/C++, Java, JavaScript, Python, SQL, HTML/CSS, Shell/Bash.
Linux, Web, Windows, AWS, Arduino, ESP32.
Summary
Developed an AI-based mock interview platform that provides real-time feedback and performance evaluation, integrating Gemini AI to analyze user responses and deliver personalized interview preparation insights. The platform successfully engaged over 200 college students, helping them enhance their interview skills through interactive and data-driven evaluations.
Summary
Engineered a comprehensive real estate platform enabling seamless property buying, renting, and listing functionalities, integrated real-time communication with Socket.io to facilitate instant messaging between buyers and sellers, and implemented advanced performance monitoring with Prometheus and Grafana to track over 30 key system metrics, including request latency, database performance, and user activity.
Summary
Implemented a robust CI/CD pipeline using GitHub Actions to automate frontend testing, build, and deployment. Developed and integrated unit and integration tests using Jest and React Testing Library to ensure frontend reliability. Deployed and managed backend services on AWS EC2 using Docker, while integrating Prometheus and Grafana for real-time API monitoring and tracking of key metrics such as request latency, active users, and error rates.