AI and NLP research
My research includes large language model applications, text classification, hidden meaning discovery, and data-driven analysis of online content.
Why learn with me
Training shaped by the work I do.
I teach coding, data science, analytics, and machine learning with a simple goal: help learners understand the ideas clearly and use them confidently in real work.
Experience
A practical path across teaching, AI research, and engineering.
These experiences shape how I design courses: clear concepts, useful examples, and hands-on practice that learners can continue after the session.
5G and multimedia systems
My PhD and later research focused on using machine learning to understand video quality, network behavior, and user experience in modern networks.
Full-stack software development
Before moving deeper into research and teaching, I built web applications, database-backed systems, responsive interfaces, crawlers, and practical software tools.
Projects
Projects that connect research with practical systems.
YourScholar.ai
A research discovery and professor-insight platform that applies data and AI ideas to support academic search and exploration.
Coded language discovery
Research work on using language models and machine learning to detect hidden meanings and emerging coded terms in online text.
QoE prediction
Machine learning systems for estimating video experience from network and wireless metrics, including encrypted traffic scenarios.
Teaching approach
Clear explanation first. Practice immediately after.
I avoid training that becomes a long list of commands. Each topic is connected to a real task: writing clean code, analyzing data, visualizing results, or building a simple model.
Learners practice during the session, ask questions, and leave with examples they can reuse after the course.
What makes the training different
Practical, patient, and grounded in real work.
Beginner-friendly clarity
Ideas are broken down carefully, with examples that make the logic visible instead of hiding it behind jargon.
Hands-on learning
Every course includes exercises, notebooks, and guided coding so learners can build confidence step by step.
Real-world awareness
Examples are informed by teaching, software projects, research workflows, data analysis, and modern AI tools.
Ready to learn?
Start with the course that matches your current level.
Share your background, goals, and schedule. I can suggest a practical learning path for coding, data science, analytics, or machine learning.