Aditya Jain

I’m an AI/ML intern focused on building practical, production-ready machine learning systems. I enjoy turning data-driven insights into real-time experiences—especially where computer vision, emotion understanding, and user sentiment connect to meaningful outcomes.

At Infosys Springboard, I engineered a real-time facial emotion recognition system using CNN and OpenCV, paired with automated music recommendations driven by detected emotions. I delivered a full-stack Flask web application for live video processing, optimized user engagement by 40% using mood-synced audio logic, and maintained smooth real-time performance at 30+ FPS.

I also built scalable solutions with model inference designed to handle concurrent user sessions. Through this work, I developed an instinct for balancing model accuracy, latency, and reliability—so the system performs consistently under real-world usage.

In my projects, I focused on biometric reliability and end-to-end ML delivery: SnapClass reached 95%+ accuracy across 500+ student enrollments, and reduced manual attendance time by 90% with comprehensive audit trails. With AuraStream, I built an end-to-end emotion-to-music platform (7 emotion classes) using TensorFlow and Flask, combining robust CNN modeling with responsive, emotion-aware UI for clear feedback.