APURVA CHAUDHARI Pune, Maharashtra | apurvachaudhari1910@gmail.com CSE (AI & ML) STUDENT | ASPIRING SOFTWARE DEVELOPER Jun 2024 - July 2024 Python Intern, V2V EdTech LLP Developed small-scale Python automation projects Wrote clean, modular Python code following best practices Collaborated with team members to debug and improve scripts Learned basics of version control and documentation Jan 2025 - Mar 2025 Project Intern , Kaizen FutureTech Learned EEG signal acquisition, filtering, and pattern recognition Integrated ESP32 with motor control mechanisms Assisted in implementing joystick and IoT-based Blynk control modes Contributed to the team developing a BCI-driven mobility solution PROFESSIONAL EXPERIENCE 2025 – 2028 (Pursuing) BTech, CSE (AI & ML) Pimpri Chinchwad College of Engineering (PCCOE) EDUCATION Polytechnic, Computer Engineering Shivajirao S. jondhle Polytechnic, Asangaon 2022 – 2025 Percentage: 94.59% (1st Rank) Certifications: Fundamentals of Java Programming (Board Infinity), CSS Training Courses (Infosys Springboard) Activities: Content & Design Team Member, OWASP Student Chapter PCCoE (Sep 2025 - Present) — Created event posters, social media creatives, and supported content writing. Languages: English, Hindi, Marathi ADDITIONAL INFORMATION SKILLS Languages: Python, Java, C++, C Web: HTML, CSS (Basics) Tools: Git, GitHub, VS Code, Arduino IDE, ESP32, Blynk App Concepts: OOP, DSA Basics, Embedded Systems, Signal Processing Basics Soft Skills: Communication, Teamwork, Documentation Aspiring Software Developer with experience in Python programming, embedded systems, and real-world project development. Completed two internships and built an EEG-based Mind-Controlled Wheelchair prototype using ESP32, Blynk, and signal processing. Strong foundation in C, C++, Java, and Python with interest in AI/ML, software development, and hardware-integrated systems. SUMMARY Jun 2024 – May 2025 Mind-Controlled Wheelchair (Prototype Model) Developed a functional wheelchair prototype using EEG brain signals for hands-free control Implemented three control modes: EEG, Joystick, and Blynk App Worked on signal filtering, preprocessing, pattern detection, and gesture-based movement Integrated ESP32 microcontroller with motor drivers for real-time control Focused on accessibility solutions for users with severe mobility impairments PROJECTS