About

I’m Nirmal Kumar, a Computer Science undergraduate at the University of Petroleum and Energy Studies (UPES), Dehradun, pursuing my Bachelor of Technology in Computer Science with a major in Artificial Intelligence and Machine Learning and a minor in Cyber Security. My academic path and personal curiosity revolve around building practical and scalable software that combines intelligence with usability. I’m deeply interested in how AI systems can be integrated seamlessly into modern web platforms to create efficient and impactful digital experiences.

Over the past few years, I’ve built and deployed full-stack applications using Next.js, React, Node.js, Express.js, and Spring Boot. I focus on clean design, modular architecture, and robust automation pipelines. I enjoy transforming ideas into reliable, production-ready systems—everything from intuitive user interfaces to backend logic that scales on AWS with CI/CD integration.

My technical journey in AI/ML has been equally rewarding. I’ve worked extensively with TensorFlow, PyTorch, and OpenCV on projects ranging from image classification to real-time object detection. During my research internship at IIT Roorkee, I developed a Crop and Weed Detection Model using the YOLOv8 architecture to support precision agriculture. The system classified multiple plant types under field conditions and improved accuracy through data augmentation, preprocessing, and transfer learning on high-performance clusters. This experience strengthened my understanding of computer vision pipelines—from data collection to final model optimization.

In parallel, I contributed to several large-scale software projects. Granth is an AI-powered assistant that combines Next.js, FastAPI, Firebase, and GPT-based models to deliver contextual document search and semantic understanding. Code Yantra is a coding platform where I built the backend using Node.js, Express.js, and Firestore to handle real-time authentication, test management, and interactive dashboards. Zapis, my patented project, is a secure attendance system integrating Wi-Fi IP verification, facial recognition, and geofencing for identity validation.

My experience extends beyond software engineering into automation and IoT. I worked on integrating sensors and robotic control using Python, ROS, and OpenCV, automating physical tasks through machine vision. These projects helped me understand the blend of hardware and software—turning real-world data into actionable intelligence.

I take pride in a detail-oriented and methodical approach to problem-solving—writing clear code, documenting thoughtfully, and designing systems that are both scalable and maintainable. I believe that technology should be built with purpose and empathy, serving real human needs rather than just demonstrating complexity.

Outside academics, I enjoy chess and singing. Chess sharpens my ability to think several steps ahead, while music helps me stay calm and creative during challenging problem-solving sessions. Both teach patience, discipline, and balance—the same qualities I apply in my engineering work.

Looking forward, I aim to explore advanced AI systems, cloud automation, and human-centered design. My long-term goal is to build intelligent applications that bridge machine learning with intuitive user experiences. Every project I take on is a step toward that vision—combining logic, creativity, and innovation to make technology more accessible, adaptive, and meaningful.