I’m Mohammadreza Javadzadegan, working at the intersection of machine learning, generative AI, and educational data, with a background in structural / earthquake engineering.
My path started in structural / earthquake engineering and moved into educational data science, learner modeling, and AI-driven course recommendation systems for higher education.
I combine a strong quantitative background with hands-on experience in machine learning, neural networks, and recommender systems. My recent work focuses on human-centered AI for education, leveraging Python, MATLAB, and modern ML libraries.
I enjoy designing systems that are not only accurate, but also interpretable and aligned with how learners actually make decisions.
A mix of engineering foundations and modern data science tools, from numerical modeling to neural networks, NLP, and recommendation systems.
A sample of research and applied projects. I’m happy to share more technical details, code, and reports upon request.
Designed a course recommendation system using enrollment histories and learner performance to support better course selection. Focus on explainable, skill-based recommendations aligned with students’ long-term goals.
Used MATLAB and neural networks to detect damage in beam-to-column connections under seismic loading. Currently extending the work with SVM and ensemble learning for comparative performance analysis.
Designing a concept for a personal development app that uses large language models to help users track goals, build habits, and reflect on progress, with a focus on transparency and user control.
Open to PhD positions, research collaborations, and data / AI roles where I can work on learner modeling, recommender systems, or applied ML.
If you think my background could be a good fit for your research group or organization, feel free to reach out with a short description of your work and how I might contribute.