I’m a Computer Science postgraduate student specializing in building real-world software systems, machine learning solutions, and research-driven applications. I enjoy transforming complex ideas into efficient, scalable products.
I am a Computer Science postgraduate student currently pursuing my M.Tech in Computer Science and Engineering at Amity University, Bengaluru. I hold a strong academic foundation with a B.E. in Computer Science and Engineering and a Diploma in Computer Science, which has helped me build a deep understanding of core computing concepts, software development, and problem solving.
My technical interests lie at the intersection of Software Engineering, Machine Learning, and Computer Vision. I have hands-on experience developing real-world applications using Python, Flutter, and modern development tools. During my internship with the Samsung PRISM Program, I worked on a research-driven project in Semantic Image Editing, where I optimized SPADE and SEASAME architectures to reduce computational redundancy and improve inference performance. This work led to an IEEE publication, strengthening my passion for research and applied AI.
In addition to research, I have industry experience building production-ready applications. I have developed machine learning–integrated mobile applications, secure file-sharing systems, and NLP-based solutions. I enjoy transforming ideas into scalable, efficient systems and continuously upskilling myself in emerging technologies. I am actively seeking opportunities where I can contribute as a Software Engineer, Machine Learning Engineer, or Research Intern, while continuing to grow as a technologist.
This was my first deep exposure to research. I worked on Semantic Image Editing, optimizing SPADE and SEASAME architectures. I reduced redundant computation and improved inference speed while maintaining visual quality. The project resulted in an IEEE publication, which shaped my interest in ML research.
I developed a real-time Bag Counter Application using image classification. This project taught me how ML models integrate with production mobile apps and how automation reduces human errors in industrial workflows.
I led iOS development for a Flutter-based interview preparation app. I handled REST APIs, testing, and App Store deployment using Xcode, gaining real-world product release experience.
Optimized SPADE and SEASAME architectures by eliminating redundant computation. Improved inference speed while maintaining visual quality.
Tech: Python, PyTorch, Computer Vision
📄 View PaperBuilt a Flutter-based application integrated with machine learning models to automate bag counting and reduce manual errors.
Tech: Flutter, Python, Image Processing
💻 View Source CodeA secure file sharing system with integrated network monitoring support. Enables safe transmission and oversight of file access patterns. (Full source on GitHub)
💻 View Source CodeA Python-based voice assistant built with speech recognition, text-to-speech, and command parsing to handle audio interactions.
💻 View Source CodeA PyTorch implementation of image compression using generative adversarial networks with evaluation metrics like PSNR and SSIM.
💻 View Source CodeA natural language processing based spelling correction system built in Python using tokenization and probability-based candidate generation.
💻 View Source Code