CV
Contact Information
| Name | Jubair Ahmed Nabin |
| Professional Title | Lecturer | Computer Science |
| jahmed.cse@iubat.edu | |
| Location | Dhaka, |
Professional Summary
Lecturer in Computer Science with research focus on machine learning, cybersecurity, and deep learning applications. Experienced in teaching core computing courses and conducting research in areas such as federated learning and anomaly detection. Interested in advancing academic research and pursuing PhD opportunities in intelligent and secure systems.
Experience
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2023 - Present Dhaka, Bangladesh
Lecturer, Department of Computer Science and Engineering
IUBAT-International University of Business Agriculture and Technology
Teaching undergraduate courses and supervising student projects in core computing subjects.
- {“Courses taught”=>”Data Structures, DBMS, System Analysis & Design, Engineering Ethics”}
- Designed assignments aligned with outcome-based education (OBE)
- Supervised database and software development projects
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2023 - 2023 Dhaka, Bangladesh
Internship Trainee
Vivasoft Limited
Led a team of front-end and back-end trainees.
- Led a team of 6
- Worked on integrating front-end and back-end
- Built an end to end api for serving medicine information
Education
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2018 - 2023 Chittagong, Bangladesh
BSc
Chittagong University of Engineering & Technology
Computer Science and Engineering
- Specialized in Machine Learning and Cyber Security
- Research on deep learning-based intrusion detection systems
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2015 - 2017 Chittagong, Bangladesh
HSC
Chittagong College
Science
Publications
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2025 CNN-LSTM Based Federated Learning for Intrusion Detection
IEEE Conference (Under Preparation)
Proposed a federated learning framework integrating CNN-LSTM for efficient and privacy-preserving intrusion detection.
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2024 Deep Learning-Based Anomaly Detection in Video Data
Developed a two-stage model using CNN and Vision Transformer for anomaly detection and classification.
Skills
Languages
Interests
Projects
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Intrusion Detection System
Developed a deep learning-based IDS using CNN, LSTM, and attention mechanisms.
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Meme Sentiment Analysis
Multimodal system for sentiment detection using image and text features.