Yamini Chandu

A highly skilled postgraduate student completing an MSc in Artificial Intelligence & Robotics, with strong knowledge in deep learning, robotic systems and experimental design. Seeking a role as an AI Engineer to apply advanced technical skills in building and deploying intelligent, working solutions. Experience creating AI prototypes, analysing complex data and successfully moving research ideas into practical applications. Interested in applying this knowledge to intelligent systems, especially patient-monitoring technologies, autonomous robots and designing inclusive digital solutions. A strong team member with excellent transferable skills in communication, leadership and managing complex tasks efficiently in collaborative environments.

Advanced Masters Project: Hybrid CNN + ML for EEG-Based Hand Gesture Recognition | 2025-2026

• Developed a complex hybrid deep learning pipeline, combining CNN feature extraction with machine learning classifiers (SVM, RF, KNN) to significantly improve signal classification accuracy.
• Applied strict digital signal processing methods (denoising, artefact removal) to EEG signals, followed by analysis of spatiotemporal EEG features to classify patient hand gestures.
• Achieved better classification accuracy using CNN embeddings and checked performance using metrics such as confusion matrices and cross-validation. The project aims to enable gesture-based communication for patients with restricted mobility.

Autonomous Mobile Robot : SLAM & Navigation (ROS2) | Start - end date

• Developed a complete mapping and localisation process by implementing the AMCL and GMapping algorithms, successfully processing Lidar data within the ROS2 framework.
• Built a working navigation stack and a dependable obstacle avoidance algorithm, thoroughly verifying its performance within the Gazebo simulation environment.

CNN-Based Gesture Recognition System (Embedded AI) | Start - end date

• Built a high-performing Convolutional Neural Network classifier that reached 93% accuracy for hand gesture detection, demonstrating skills in deep learning for vision tasks.
• Successfully deployed the AI model onto a Raspberry Pi, allowing for real-time, fast, vision-based interaction, showing valuable skills in embedded AI systems.

Reinforcement Learning Agent for Path Optimisation | Start - end date

• Implemented both Proximal Policy Optimisation and Deep Q-Network agents, specifically for autonomous navigation and finding the optimal path.
• Adjusted the reward functions and performed detailed testing to check agent consistency in various complex simulation environments.

Major Undergraduate Project: Health Monitoring & Location Tracking for Soldiers | 2022-2023

• Designed and built a wearable IoT system using GPS, a GSM module and an Arduino Uno to monitor soldier vital signs and location continuously.
• Programmed the system to automatically collect health data and integrated the GSM module to send immediate emergency alerts, greatly improving safety through continuous monitoring.

Research Methods Module: Mixed-Methods Experimental Data Collection | Start - end date

• Carried out a multi-phase robotics experiment in a controlled lab setting, strictly following defined ethical and data collection rules, including studies that involved both human and ChatGPT interactions.
• Ensured full ethical compliance by managing participant consent forms and secure data upload, showing a responsible approach to research.

Completed an online certification course in Python for Data Science from Analytix Labs.