Md. Zahid Hassan Rabbi is a Lecturer in the Department of Computer Science and Technology at Bangladesh Army University of Science and Technology (BAUST), Khulna. He completed his B.Sc. in Computer Science and Engineering from Khulna University with a CGPA of 3.93 out of 4.00, securing 1st position, and completed his M.Sc. in Computer Science and Engineering from Khulna University with a perfect CGPA of 4.00 out of 4.00.

His academic and research interests include Artificial Intelligence, Machine Learning, Natural Language Processing, Large Language Models (LLMs), Log Parsing, Transformer-based Models, Parameter-Efficient Fine-Tuning, and intelligent software systems. His MSc thesis focused on Type-Aware and Syntax-Constrained Log Parsing using Compact Encoder–Decoder Transformers.

He has teaching and academic experience at BAUST Khulna, Khulna University, North Western University Khulna, and Northern University of Business and Technology Khulna, along with research, scholarship, innovation, and professional training experience.

M.Sc. in Computer Science and Engineering
Khulna University, Khulna, Bangladesh | January 2025 – July 2026
CGPA: 4.00 / 4.00
Thesis: Type-Aware and Syntax-Constrained Log Parsing using Compact Encoder–Decoder Transformers
Supervisor: Dr. Kamrul Hasan Talukder, Khulna University

B.Sc. in Computer Science and Engineering
Khulna University, Khulna, Bangladesh | January 2020 – December 2025
CGPA: 3.93 / 4.00 | 1st Position

Lecturer
Department of Computer Science and Technology, BAUST, Khulna
August 2026 – Present

Part-time Teacher
Khulna University, Khulna
January 2026 – July 2026

Lecturer
Northern University of Business and Technology Khulna (NUBTK)
April 2025 – July 2026

Teaching Assistant
Khulna University, Khulna
July 2025 – December 2025

Adjunct Lecturer
North Western University, Khulna
January 2025 – July 2025

1. Md. Zahid Hassan Rabbi and Kamrul Hasan Talukder, “Type-Aware and Syntax-Constrained Log Parsing using Compact Encoder–Decoder Transformers,” 2026 IEEE International Conference on Signal Processing, Information, Communication and Systems (SPICSCON), Faculty of ECE, Bangladesh Army University of Engineering & Technology (BAUET), Qadirabad, Natore-6431, Bangladesh, 13–14 August 2026. Status: Accepted conference paper.

• Large Language Models (LLMs)
• Natural Language Processing (NLP)
• Machine Learning and Deep Learning
• Log Parsing and Log Analysis
• Transformer-based Models
• Encoder–Decoder Architectures
• Parameter-Efficient Fine-Tuning (LoRA / QLoRA)
• Artificial Intelligence for Software Engineering
• Intelligent Software Systems
• Type-Aware and Syntax-Constrained NLP

• Received research funding from Khulna University for MSc thesis research.
• Received annual academic scholarships from Khulna University for excellent academic results.
• Received a startup grant for the startup “ROOT” through the University Innovation Hub Program (UIHP).
• Received a scholarship under the Directorate of Secondary and Higher Education (DSHE/).
• Completed B.Sc. in Computer Science and Engineering with CGPA 3.93/4.00, securing 1st position.
• Completed M.Sc. in Computer Science and Engineering with CGPA 4.00/4.00.

2026 IEEE International Conference on Signal Processing, Information, Communication and Systems (SPICSCON)

13–14 August 2026

Faculty of ECE, Bangladesh Army University of Engineering & Technology (BAUET), Qadirabad, Natore-6431, Bangladesh

Research Work: Type-Aware and Syntax-Constrained Log Parsing using Compact Encoder–Decoder Transformers

Authors: Md. Zahid Hassan Rabbi and Kamrul Hasan Talukder

Python Programming & Data Science

Organized by the EDGE Project, ICT Division / Bangladesh Computer Council, in collaboration with Khulna University.

The training covered Python programming, data analysis, and data science-related concepts and practical skills.

Research Profile

His research focuses on applying modern machine learning and transformer-based architectures to software and system log analysis. His MSc research explores type-aware and syntax-constrained log parsing using compact encoder–decoder transformer models, with emphasis on parsing accuracy and generalization across diverse log datasets.

Technical / Professional Areas

• Python, C/C++, Java, SQL
• Machine Learning and Deep Learning
• Natural Language Processing
• Large Language Models
• Transformer Architectures
• LoRA and QLoRA
• Log Analysis and Parsing
• Web and Software Development