Teaching

Courses, Mentoring, and Educational Activities

Teaching Philosophy

I believe in fostering a learning environment that combines theoretical foundations with practical applications. My teaching approach emphasizes hands-on experience with cutting-edge AI technologies, encouraging students to explore, experiment, and develop innovative solutions to real-world problems.

With over 15 years of teaching experience across multiple institutions, I have taught courses ranging from fundamental mathematics to advanced deep learning and computer vision.

Teaching Experience

National University of Uzbekistan, Djizakh Branch

2025 - Present

Teaching advanced courses in computer vision and deep learning to graduate students.

  • Deep Learning in Computer Vision Graduate

Teaching advanced courses in Cybersecurity to undergraduate students.

  • Cybersecurity UnderGraduate

Karshi Irrigation and Agrotechnology Institute

2022 - 2024

Delivered courses on deep learning fundamentals and mathematical modeling for engineering applications.

  • Deep Learning Undergraduate
  • Mathematical Modeling Undergraduate

Tashkent University of Information Technology

2013 - 2018

Taught courses covering machine learning algorithms, numerical methods, and computational modeling.

  • Machine Learning Graduate
  • Numerical Analysis Undergraduate
  • Mathematical Modeling Undergraduate

Jizzakh Polytechnical Institute

2010 - 2013

Introduced students to computational mathematics and modeling techniques.

  • Numerical Analysis Undergraduate
  • Mathematical Modeling Undergraduate

Inha University - Research Assistant

2019 - 2025

Supported AI and medical AI courses while guiding student research projects.

Mentoring Activities

  • Guided undergraduate and graduate research projects
  • Supervised student thesis work in AI and computer vision
  • Led lab sessions for AI and Medical AI courses
  • Mentored students in deep learning implementations

Courses Taught

Deep Learning

Neural networks, CNNs, RNNs, Transformers, and practical applications in computer vision and NLP.

Computer Vision

Image processing, object detection, segmentation, and deep learning approaches for visual understanding.

Machine Learning

Supervised and unsupervised learning, model evaluation, feature engineering, and practical implementations.

Numerical Analysis

Numerical methods for solving equations, interpolation, integration, and differential equations.

Mathematical Modeling

Formulation and analysis of mathematical models for engineering and scientific applications.