I'm a passionate Computer Science graduate from CMR Technical Campus, Hyderabad, fascinated by technology and driven to build meaningful solutions. With a strong foundation in software development and a natural curiosity for problem-solving, I aspire to grow as a software developer in today's fast-paced tech world. I stay up-to-date with the latest trends in coding and software engineering and am committed to continuous learning and growth in this ever-evolving field.
🌱 I’m currently learning React and excited to build projects that bring ideas to life.
🤖 Passionate about Machine Learning and Artificial Intelligence — I love exploring how tech can solve real-world problems.
💬 Ask me anything about ML, AI, or Software Development — always happy to share and learn!
🚀 I'm also open to freelance opportunities in AI/ML — excited to collaborate and contribute to impactful projects!
- 📫 How to reach me epicsriram15@gmail.com
- College: CMR Technical Campus, Hyderabad
- Degree: B.Tech in Computer Science and Engineering
- Year of Graduation: 2025
- CGPA: 8.71 / 10
- ✅ Solved over 700 Data Structures and Algorithms (DSA) problems across various coding platforms.
- 🏅 Earned prestigious 50-Day and 100-Day Coding Streak badges on LeetCode.
- 🥇 Top 5 Finalist in Internal Smart India Hackathon (SIH) at CMR Technical Campus.
Tech Stack: Gemini API, Voice Input, Natural Language Processing
Developed a standalone AI-powered visa interview preparation tool integrating Gemini API for real-time feedback, voice input, and NLP to create an interactive user experience. Focused on continuous improvement through experimentation and user feedback.
Link: Code
Tech Stack: Machine Learning, Python, NLP, Flask, TensorFlow
Collaborated in a team of 3 to build an AI medical chatbot for infectious disease prediction using deep learning and NLP. Integrated multilingual support and voice input to enhance user interaction.
Link: Code
Tech Stack: Machine Learning, Signal Processing
Worked as part of a team of 3 to develop a predictive model for liver fibrosis severity in chronic HBV patients. Used physical layer signal features combined with machine learning to analyze medical data for accurate disease staging, aiding early diagnosis and treatment planning.
Link: Code
