Computer Science undergrad focused on AI/ML and robotics - building small, fast systems at the edge and solid training/inference pipelines in the cloud. I like shipping useful demos more than collecting badges.
- 🤖 Building AI/ML systems – from lightweight edge deployments to production-grade cloud pipelines with MLOps
- 🔬 Currently exploring Deep Learning, Generative AI, and end-to-end MLOps workflows
- 🛠️ Love combining software with hardware – IoT sensors, robotics, embedded systems, and real-time data
- 🎯 Goal: Become an AI/ML Engineer with a strong focus on robotics and edge computing
- 💡 Open to collaborations on impactful tech projects – let's build something cool together!
| Project | Description | Tech |
|---|---|---|
| Telco Churn MLOps Pipeline | Production-grade MLOps pipeline for customer churn prediction with Kafka streaming & Airflow orchestration. 84.66% ROC-AUC, 8.2ms inference latency. | Python MLflow Kafka Airflow Docker |
| Smart Beehive Monitor | ESP32-based IoT system for real-time beehive monitoring with Firebase integration. Tracks temperature, humidity, bee activity, and weight. | ESP32 IoT Firebase PlatformIO |
| Gait Authentication System | Behavioral biometric authentication using walking patterns. Achieved 3.43% EER with 92.68% accuracy using multi-sensor fusion. | MATLAB Deep Learning Signal Processing |
| TinyGPU | Educational GPU simulator demonstrating SIMT architecture with per-thread registers, synchronization barriers, and visual execution. | Python GPU Architecture Visualization |
def my_journey():
skills = ["AI", "ML", "Robotics", "Computer Vision", "IoT", "Agentic AI", "NLP", "Edge Computing"]
goals = ["AI/ML Engineer", "MLOps/LLMOps Engineer", "Robotics Researcher"]
passion = float('inf')
while passion > 0:
learn(skills)
apply(skills)
innovate(skills)
share_knowledge()
passion += 1 # Passion only grows with time!
my_journey() # Currently executing...
