I am a third-year Computer Science (Data Science) student with hands-on experience in machine learning and deep learning.
I have a strong interest in applying data-driven methods to real-world problems and enjoy building practical, impactful solutions. I am adaptable, quick to learn, and motivated to continuously improve my technical and research skills.
Python, C/C++, Java, Javascript, Kotlin, scikit-learn, NumPy, Pandas
Supervised & unsupervised learning, feature engineering, ensemble methods, CNNs, transformers, PyTorch
MySQL, MongoDB, PostgreSQL, Git, GitHub, PowerBI, DSA, OOP, OS, Networks, Data Processing at Scale
Implementing advanced Sign Language Video Generation (SLVG) using compressed and quantized multi-condition tokenization to generate identity-preserving sign language videos from spoken language text. FSQ-based compression and a text-to-token translation pipeline for efficient, high-fidelity video synthesis.
CGPA: 9.8
BMS College of Engineering, BangaloreAward given for highest academic performance in Mathematics in the department.
CSIR – 4pi, National Aerospace Laboratories (NAL), Bengaluru
BMSCE IEEE Power and Energy Society and Sensors Council
Society for Protection and Animal Welfare, Rotaract Club, BMSCE
React, TypeScript, MediaPipe, OpenCV, Node.js
Built an interactive web application that teaches Indian classical dance mudras using computer vision-based gesture evaluation.
Implemented real-time hand landmark detection and gesture similarity scoring to provide automated feedback on gesture accuracy.
Developed structured UI components for mudra exploration, quiz-based learning, and progress tracking dashboard.
Kotlin, Android Studio, Google Maps API, MongoDB
Designed and developed a women’s safety mobile application focused on real-time emergency response and preventive safety.
Integrated SOS alerts with live location sharing, fake call simulation, nearby police station locator, and one-tap access to verified Indian helpline numbers.
Built secure user authentication and profile management (Login, Registration, Forgot Password) using MongoDB.
Python, OpenCV, Keras, TensorFlow
Built a real-time driver drowsiness detection system using webcam input, face and eye detection, and CNN-based eye-state classification.
Implemented a Convolutional Neural Network (CNN) in Keras to classify eyes as open or closed from live video frames.
Designed a drowsiness scoring and alert mechanism to trigger warnings upon prolonged eye closure.