Hi, I’m Raisa Fairooz Meem, a research-focused AI/ML engineer with a MSc in Computer Science from the American International University-Bangladesh and a BSc in Electronics and Communication Engineering from KUET. I specialize in medical image classification, computer vision, and transfer learning, with hands-on experience building dataset pipelines, training YOLO-based detectors, and evaluating transformer-based models for healthcare applications. Collaborating across academia and industry, I have led international partnerships, contributed to publications, and worked on real-time detection systems for CCTV and student sentiment analysis for MOOC platforms. I’m passionate about turning complex data into practical ML solutions, mentoring others, and continuously learning to stay at the frontier of AI.…

Raisa Fairooz Meem

Hi, I’m Raisa Fairooz Meem, a research-focused AI/ML engineer with a MSc in Computer Science from the American International University-Bangladesh and a BSc in Electronics and Communication Engineering from KUET. I specialize in medical image classification, computer vision, and transfer learning, with hands-on experience building dataset pipelines, training YOLO-based detectors, and evaluating transformer-based models for healthcare applications. Collaborating across academia and industry, I have led international partnerships, contributed to publications, and worked on real-time detection systems for CCTV and student sentiment analysis for MOOC platforms. I’m passionate about turning complex data into practical ML solutions, mentoring others, and continuously learning to stay at the frontier of AI.…

Available to hire

Hi, I’m Raisa Fairooz Meem, a research-focused AI/ML engineer with a MSc in Computer Science from the American International University-Bangladesh and a BSc in Electronics and Communication Engineering from KUET. I specialize in medical image classification, computer vision, and transfer learning, with hands-on experience building dataset pipelines, training YOLO-based detectors, and evaluating transformer-based models for healthcare applications.

Collaborating across academia and industry, I have led international partnerships, contributed to publications, and worked on real-time detection systems for CCTV and student sentiment analysis for MOOC platforms. I’m passionate about turning complex data into practical ML solutions, mentoring others, and continuously learning to stay at the frontier of AI.

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Skills

Experience Level

Python
Expert
TensorFlow
Expert
Data Science
Expert
PyTorch
Expert
Data Visualization
Expert
GitHub
Expert
SQL
Expert
Keras
Expert
Docker
Expert
Tableau
Expert
Django
Expert
JavaScript
Expert
React JS
Expert
HTML5
Intermediate
C
Intermediate
CSS
Intermediate
C++
Intermediate
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Language

Bengali
Fluent
English
Fluent

Work Experience

Research Engineer at Debug Vision
June 1, 2023 - August 31, 2025
Research and annotate image datasets to train YOLO models for real-time object detection; build and curate datasets for model training; contribute to computer vision research projects.
Head of Internal Affairs at AUS-BAN International Study Edu-Care
September 1, 2020 - September 30, 2021
Build and sustain business relationships with institutes around the world; participate in business planning and collaboration initiatives.

Education

MSc in Computer Science at American International University-Bangladesh
January 1, 2022 - March 19, 2023
BSc in Electronics and Communication Engineering at Khulna University of Engineering and Technology
January 1, 2013 - September 12, 2017

Qualifications

Code in Place
July 3, 2023 - January 14, 2026
Vice Chancellor's Award for Best Thesis for Master of Science in Computer Science
March 19, 2023 - January 14, 2026

Industry Experience

Software & Internet, Education, Healthcare, Media & Entertainment, Professional Services
    paper Domestic Emotional Abuse in Childhood: A Small-Scale Study Based on Adult Retrospective Accounts

    This study explored domestic emotional abuse during childhood through the experiences of 50 adult participants, surveyed according to guidelines from healthline.com. The collected responses were structured and analysed using a relational database, providing a systematic view of the prevalence and severity of such experiences. Analysis revealed that 13.89% of participants reported experiencing extremely high levels of domestic emotional abuse, highlighting the significant, lasting impact of early emotional trauma and the importance of awareness and support in mental health initiatives.

    paper WhatsApp Sorder: Automated Content Summarisation from WhatsApp Links using BERT

    This project developed a tool to extract and summarise content from web links shared in WhatsApp chats. Using BERT for natural language understanding, the system automatically identifies hyperlinks, retrieves the linked content, and generates concise summaries. The final results are compiled into a PDF report, providing an efficient way to digest large volumes of information from messaging platforms. This work highlights the power of AI in automating information processing, transforming scattered digital content into meaningful, actionable insights.