Kaamesh Saravanan

Motivated and detail‑oriented Computer Science student with strong foundations in algorithms, software development, and problem‑solving. Seeking an internship where I can apply my technical skills, contribute to real projects, and continue growing as an engineer.

• AI Flashcard Generator – Developed a web app using Python, Flask, and OpenAI GPT API to automatically generate study flashcards for any topic. Designed a user-friendly interface where users input a subject and receive structured Q&A flashcards instantly. Gained experience in web development, API integration, and automated content generation for educational purposes.
• Autoschedule AI – an AI-powered system that reads course syllabi and extracts assignments, exams, and deadlines using NLP and automated date parsing. Built a full semester calendar generator with workload predictions and intelligent event organization. Developed using Python (FastAPI), JavaScript, React, spaCy, and dateparser, following a full-stack architecture with modular services. Implemented Google Calendar sync, enabling users to push deadlines directly to their personal schedules with seamless integration.

• Harvard CS50 – Intro to Computer Science (C, Memory, Algorithms)

Covers core computer science fundamentals using C, including memory management, data structures, algorithms, and problem-solving. Demonstrates strong understanding of low-level programming and computational thinking.

• Nand2Tetris – Computer Architecture & CPU Design

Hands-on course building a computer from logic gates up to a working CPU, assembler, VM, and OS components. Shows deep knowledge of digital logic, hardware-software interaction, and system architecture.

• Google IT Automation with Python – Scripting, Linux, Automation

Focuses on Python automation, command-line tools, Linux systems, debugging, and version control. Demonstrates practical skills for scripting, troubleshooting, and workflow automation in real-world environments.

• Machine Learning by Andrew Ng – ML Algorithms, Python, Data Prep

Introduces supervised and unsupervised learning, model evaluation, gradient descent, and data preprocessing. Builds strong foundations in machine learning using Python and real datasets.