Fazri Gading fazrigading

👋 Hi, my name is Fazri Gading

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👔 Professional Role:

🤖 Applied AI & Systems Engineer with a background in Computer Science and applied AI research.
🧩 Work at the intersection of Artificial Intelligence, Software Engineering, and Systems Development.
🎓 Hold a Bachelor of Computer Science (B.CS. / S.Kom.) from Universitas Mulawarman, with a strong academic and research background in Deep Learning and Computer Vision, especially in object detection, instance segmentation, and pattern recognition.

🧠 Research Profile:

🔬 My research portfolio also extends across deep learning-based detection, image analysis, agricultural automation, and medical imaging.
🌾 My research includes agricultural AI and intelligent vision systems, with Scopus-indexed research publications involving Smart Agricultural Technology (Q1).
📊 I am particularly interested in research that connects AI models, data, software systems, and domain-specific applications rather than treating machine learning models as isolated components.

⏳ Current Phase:

⚙️ Expanding into AI-powered applications, LLMs, RAG systems, developer tooling, Linux desktop systems, cross-platform software, and software architecture.
🔧 Enjoy experimenting with software and hardware, building productivity tools, exploring Linux, and turning ideas inspired by games and everyday problems into software projects.
📈 Improving online presence via digital branding and focusing on professional self-development such as building meaningful projects, learning more programming languages, newer frameworks and models, also advanced algorithms.

Design software systems that connect AI models with APIs, databases, user interfaces, and external services.
Build back-end services and data pipelines for AI applications.
Develop developer tools and productivity-oriented software.
Explore Linux, desktop environments, system integration, and automation.
Build cross-platform applications that communicate across devices and operating systems.

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Develop and experiment with Deep Learning and Machine Learning systems.
Work with Computer Vision, particularly object detection, image analysis, segmentation, and visual recognition.
Explore LLM applications, Retrieval-Augmented Generation (RAG), and AI-assisted software systems.
Experiment with model architectures, training workflows, evaluation methodologies, and deployment.
Apply AI to domains including agriculture, medical imaging, and other real-world problems.

Translate research ideas into reproducible software implementations.
Investigate model architectures and compare their practical performance.
Build datasets, experimentation pipelines, and evaluation workflows.
Work on applied research involving agricultural AI, medical imaging, and computer vision.
Bridge the gap between academic experimentation and usable engineering systems.

Former Machine Learning Mentor at Bangkit Academy 2024 by Google, GoTo, and Traveloka, supporting more than 25 students throughout their ML learning path and projects.
Served as a laboratory/teaching assistant at Universitas Mulawarman, Samarinda, Indonesia for subjects including Machine Learning, Deep Learning, Data Structures & Algorithms, and Object-Oriented Programming.
Help students understand both the theoretical foundations and practical implementation of software and AI systems.
Occasionally helped students to write undergraduate thesis by guiding the research flow implementation, programs development, and deliverables preparation.

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