I am a Computer Engineering graduate and AI Research Engineer focused on developing practical deep learning systems through research and experimentation.
My research interests include Speech Processing, Computer Vision, and efficient AI systems, with a focus on representation learning and reproducible experiments.
Currently, I am a member of an applied AI team, collaborating on real-world AI solutions in computer vision and speech processing.
- 🔎 Research-driven: studying ideas, papers, and architectures before implementation.
- 💻 Engineering-focused: building modular, clean, and reproducible systems.
- 🚀 Continuous learner: exploring new domains and turning ideas into working solutions.
Deep Learning • Speech Processing • Computer Vision • Representation Learning • Efficient AI Systems
A research framework comparing Log-Mel and WavLM representations using lightweight deep learning architectures.
Concepts: CNN • ECA Attention • GeM Pooling • Representation Efficiency
Research manuscript submitted for peer review.
A modular PyTorch implementation of ResNet50 enhanced with Channel and Spatial Attention mechanisms inspired by CBAM.
Technologies: PyTorch • CNN • Attention Mechanisms
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A structured collection of JavaScript concepts, examples, and best practices for learning and reference. |
A responsive frontend project built with HTML5, CSS3, and JavaScript. |
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A lightweight responsive grid system inspired by Bootstrap, built using CSS and Sass. |
🖼️ Art-NFTA responsive NFT landing page designed in Figma and implemented using HTML5, CSS3, and Bootstrap 5. |
I believe impactful AI systems are built by combining research understanding, engineering discipline, and continuous experimentation.
