BSc in Computer Science & Engineering graduate, focused on developing innovative AI/ML solutions with expertise in TinyML and IoT applications. Currently working on scalable ML systems and exploring the frontiers of deep learning architectures, with a particular interest in emotion detection and real-time processing systems.
β’ Developing production-ready machine learning pipelines with emphasis on scalability and performance
β’ Exploring advanced neural architecture search techniques for optimized model designs
β’ Implementing efficient model deployment strategies using modern MLOps practices
β’ Contributing to open-source ML projects to advance the field of AI
β’ Exploring advanced neural architecture search techniques for optimized model designs
β’ Implementing efficient model deployment strategies using modern MLOps practices
β’ Contributing to open-source ML projects to advance the field of AI
graph TD
A[Tech Stack] --> B[Languages]
A --> C[ML/DL]
A --> D[Web]
A --> E[Tools]
B --> B1[Python]
B --> B2[PHP]
B --> B3[JavaScript]
C --> C1[TensorFlow]
C --> C2[PyTorch]
C --> C3[TinyML]
D --> D1[Laravel]
D --> D2[HTML/CSS]
D --> D3[SQL]
E --> E1[Linux/Unix]
E --> E2[Git]
E --> E3[SDLC/Scrum]
Automated architecture discovery with 60% reduced search time while maintaining state-of-the-art accuracy Key Features
β’ Multi-objective optimization |
Production-grade ML deployment pipeline with automated A/B testing capabilities Key Features
β’ Automated model versioning |
Research on optimizing emotion detection algorithms for IoT wearable devices using TinyML approaches Key Features
β’ Voice signal processing |
I'm always open to collaborating on innovative AI/ML projects or discussing the latest developments in the field. Feel free to reach out!
"The future belongs to those who can untangle complexity and forge intelligence from chaos."