Teaching
Teaching
I want students to understand why a method works, implement it themselves, and recognise the conditions under which it fails.
Teaching philosophy
Understanding before memorisation
I learn best by building, testing, and asking why. I would use the same sequence in teaching: begin with a concrete problem, develop the idea behind the solution, implement it, and use the result to expose what the theory explains.
In programming and algorithms, incorrect solutions are valuable evidence. A program that passes simple examples but fails on a boundary case can reveal more about a student's mental model than a memorised definition. I would make those failures visible and guide students to diagnose them.
In machine learning, students should work with data that are incomplete, imbalanced, or locally relevant. Clean benchmark datasets are useful for introducing a method, but they should not hide the work required to evaluate a real system.
My goal is to adapt explanations to the student's current understanding, connect theory with implementation, and assess both the result and the reasoning used to reach it.
Teaching areas
Courses I am prepared to teach
Primary teaching areas
- Structured Programming
- Object-Oriented Programming
- Data Structures
- Algorithm Design and Analysis
- Database Systems
- Software Engineering
- Operating Systems
AI and machine learning
- Machine Learning
- Deep Learning
- Computer Vision
- Natural Language Processing
Advanced and special-topic interests
- Explainable AI
- Scientific Machine Learning
- GeoAI and Remote Sensing
- Large Language Model Systems
- Cloud Computing
Academic preparation
Academic and Technical Preparation
2022 – 2026
Sustained competitive problem solvingCodeforces Specialist (maximum rating 1431), CodeChef 3-star (maximum rating 1735), 1,500+ problems across five platforms, and participation in two national inter-university onsite contests.
Algorithms
2025 – 2026
Research collaboration at SUSTWorked in a multi-author research group under Dr. Ahsan Habib, contributing analysis, visualisation, interpretation, and manuscript development.
Research
2025
Backend engineering in the BRAC Tech & SELP-RHRN AI FellowshipTranslated technical requirements into a working backend, explained engineering decisions within a team, and delivered under project constraints.
Engineering
2024 – 2025
Collaborative technical competitionsSix placed finishes, including one championship, through small-team problem solving under time constraints.
Teamwork
Undergraduate research & project mentoring
Areas I would support as a lecturer
I would be interested in mentoring undergraduate research and project work that connects a clear computer-science question with a realistic dataset or working system.
- Applied machine learning and computer vision
- Explainable and reliable AI
- GeoAI and Earth observation
- Scientific machine learning
- Large language model applications
- Intelligent web and backend systems