AI & ML Developer | Computer Vision and Robotics Enthusiast
Aspiring AI and ML Engineer passionate about building vision-based intelligent systems that bridge perception and action. Experienced with TensorFlow, OpenCV, and FastAPI, with a focus on real-time robotics and applied AI. Demonstrated success in YOLO model tuning, gaze-based control systems, and self-balancing robotics. Currently advancing skills in RAG and Agentic AI.
Techno International New Town (2024–2028)
B.Tech in Computer Science and Engineering -Pursuing
1st Year Y.G.P.A - 8.03
Saifee Golden Jubilee English Public School (2010–2024)
Class XII - ISC: 84.67%
Class X - ICSE: 92.33%
Developed a YOLOv8x segmentation-based pipeline to isolate head-torso regions of students. Integrated a TensorFlow CNN model to classify focused vs. distracted students and served the model via a FastAPI backend.
Repository Links:
Classroom-Person-Segregation-Model,
Classroom-Compass-Classifier
Trained a SVM model to predict heart disease risk based on patient attributes, achieving 90% accuracy. Showcased various data preprocessing techniques. Trained on open source dataset from Kaggle.
Repository Link: Heart-Disease-Prediction
Built a camera-based control interface where an ESP32-driven robotic vehicle aligns its direction with the user's head position tracked via MediaPipe and OpenCV.
Repository Link: ML-Vision-Guided-GazeBot
Designed a two-wheeled bot using PID control algorithms for dynamic balance correction. Integrated sensor feedback and motor control loops for stability optimization.
Tuned a YOLO-based object detector for post-disaster search-and-rescue tasks to identify survivors or critical objects in rubble imagery.
Implemented a Decision Tree Classifier using scikit-learn to classify iris flower species, demonstrating understanding of supervised learning workflows.