Aditi Shreya

Electronics and Communication Engineering student with strong analytical and problem-solving skills, backed by a solid foundation in IoT systems, digital design, and embedded programming. Experienced in developing projects integrating hardware (ESP32, GSM, sensors) with software (Node.js, SQLite, Python). Skilled in C++, Python, and Verilog, with growing expertise in machine learning applications. Quick to adapt and eager to learn new technologies, bringing a detail-oriented and collaborative mindset to competitive environments.”

• Thapar School on Machine Learning and Deep Learning,2023

• Positions of Responsibility
• Collaborated in team-based academic projects, contributing to both technical implementation and documentation.
• Presented projects during college evaluations, explaining design, methodology, and outcomes to faculty.
• Took responsibility for report preparation and group presentations, ensuring clear communication of results.

• Project Management Web Application (Full Stack – MERN)
• Built a scalable project management platform using React.js, Node.js, Express.js, MongoDB.
• Implemented secure user authentication & authorization (JWT, bcrypt) with role-based access.
• Designed REST APIs for task creation, team collaboration, and progress tracking.
• Developed a responsive, mobile-friendly UI with React.js & Material-UI.
• Added real-time updates using WebSockets for task status changes.
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• Water Quality Monitoring System (IoT + Sensors + Cloud)
• Designed an IoT-based system to monitor water quality parameters such as pH, turbidity, and temperature.
• Used ESP32 microcontroller with water sensors to collect real-time data.
• Transmitted data to a Firebase/Node.js backend for logging and analysis.
• Built a simple dashboard interface to alert users if water falls below safe standards.

• Telemetry Glove (IoT + Embedded System)
• Built a wearable glove with sensors (Temperature, GSR, Flex, Accelerometer, Gyroscope).
• Used ESP32 + GSM (SIM800) for real-time health data transmission.
• Developed a Node.js + SQLite dashboard for live visualization.

• Fake News Detection System (ML + NLP)
• Implemented text classification model using Python, Scikit-learn, TF-IDF.
• Achieved 90%+ accuracy on benchmark dataset.
• Designed a simple CLI/web interface for testing news headlines.