Teaching

My teaching approach connects theoretical foundations with computational practice and real engineering problems. I aim to help students understand not only how to apply a method, but also why it works, when it is appropriate, and how its assumptions affect real decisions.

Student mentoring

I have mentored graduate and undergraduate researchers through project formulation, literature review, data preparation, model development, experimental evaluation, technical writing, presentation, and professional development.

Areas of instruction and support

Machine learning and deep learning
Optimization and quantitative modeling
Computer vision and image analysis
Research methodology and reproducibility
Python, GitHub, and technical computing workflows
Technical writing and research presentation

The complete record of teaching and mentoring activities will be connected to the academic CV as the website documents are added.