I bridge the gap between complex hardware logic and intelligent software systems. From designing elevator control systems using pure ICs to deploying Deep Learning models for galaxy classification and digit recognition.
- Languages: Python (ML/Automation), Assembly (PIC18F), C++ (Arduino)
- AI/ML: TensorFlow, AlexNet, AWS Bedrock (Nova Pro 1.0)
- Hardware: Digital System Design (ICs), Microcontrollers (PIC, Arduino), Circuit Simulation
- Tools: Git/GitHub, Linux, Boto3 (AWS SDK), Twilio API
Astronomical data presents unique challenges, including low signal-to-noise ratios and complex morphological features. This model leverages the deep feature extraction layers of AlexNet to identify structural patterns in deep-space imagery, moving beyond simple shape recognition to automated morphological classification.
Implementing a modified AlexNet architecture to achieve high-accuracy digit classification. Focused on deep feature extraction and spatial pattern recognition.
A 5-floor elevator logic system built entirely with digital ICs (Priority Encoders, Comparators, Flip-Flops). No software, just pure logic gates.
- Public Speaking: Debated and Adjudicated in national level debates, along with getting promoted to panelist in one.
- Leadership: Working along with a very dedicated team to revive TEDx culture in college
- Debating: Active participant in campus/national level debats debate and policy discussions.
- LinkedIn: ayush-kr-rao
- GitHub: Ayush-212
- Instagram: Ayush
- Email: aayyuusshh0909@gmail.com