Teaching & Mentoring

Teaching is an important part of my academic profile. My teaching and mentoring style emphasizes conceptual clarity, implementation, debugging, reproducibility, and connecting theory to working AI systems.

Teaching Philosophy

I teach by connecting intuition, formal concepts, and implementation. Students learn most effectively when they can explain the principle, implement the method, test it, and understand where it fails.

In mentoring, I emphasize reproducible experiments, careful debugging, clear baselines, and responsible use of AI tools. My goal is to help students become independent problem-solvers who can build and communicate reliable technical work.

Teaching Areas

Programming and problem solving.
Data structures and algorithms.
Database systems and data management.
Big Data systems and tools: Hadoop MapReduce, Spark, Pig, MongoDB, Hive, and HBase.
Artificial intelligence and machine learning.
Computer vision and applied deep learning.
Research methods and reproducible experimentation.

Big Data Tools and Teaching Repositories

I maintain teaching-oriented GitHub repositories for hands-on Big Data tools and distributed data processing examples. These demos help students connect system concepts with runnable code and reproducible workflows.

Hadoop MapReduce

Introductory MapReduce examples for word counting and graph-style PageRank workflows.

WordCount PageRank

Apache Spark

Spark demo materials for distributed data processing and scalable analytics concepts.

Spark Demo

Apache Pig

Pig demo materials for dataflow-style Big Data query and transformation examples.

Pig Demo

MongoDB

MongoDB demo materials for document-oriented data management and NoSQL practice.

MongoDB Demo

Apache Hive

Hive demo materials for SQL-style analytics over large-scale data systems.

Hive Demo

Apache HBase

HBase demo materials for column-family data modeling and distributed NoSQL storage concepts.

HBase Demo HBase Demo v2

Teaching / Instruction Experience

Lecturer, Computer Science and Engineering, BUBT, 2018-2022.
Graduate Teaching Assistant, Missouri University of Science and Technology, during PhD study.

Mentoring and Student Support

I support students with programming, applied ML workflows, debugging, experiment planning, and research implementation. I especially value helping students turn vague project ideas into testable technical questions.

Sample Topics

Object detection and tracking concepts.
Machine learning workflows.
Big Data tools: Hadoop MapReduce, Spark, Pig, MongoDB, Hive, and HBase.
Python/PyTorch implementation.
Data preprocessing and evaluation.
Research paper reading and reproduction.