I am a Computer Engineering graduate, AI Research Engineer, and Developer focused on building practical software and deep learning systems through research, development, 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 • Self-Supervised Learning • Representation Efficiency
Research manuscript submitted for peer review.
A deep learning framework for plant disease classification using ResNet50 with DCBAM attention refinement on the PlantVillage dataset.
The project investigates attention-based feature enhancement for image classification and demonstrates a reproducible PyTorch training and evaluation pipeline.
Technologies: PyTorch • ResNet50 • DCBAM • Computer Vision • Attention Mechanisms
A modular PyTorch implementation of ResNet50 enhanced with Channel and Spatial Attention mechanisms inspired by CBAM.
Technologies: PyTorch • CNN • Attention Mechanisms
🛡️ SafeIPA lightweight network security checker that verifies your current IP location before accessing sensitive online services. |
A structured collection of JavaScript concepts, examples, and best practices for learning and reference. |
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A responsive frontend project built with HTML5, CSS3, and JavaScript. |
A lightweight responsive grid system inspired by Bootstrap, built using CSS and Sass. |
I believe impactful AI systems are built by combining research understanding, engineering discipline, and continuous experimentation.
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