Chief Strategy and Technology Officer
Md. Musfiqur Rahman
Applied Deep Learning · IEEE & Springer Nature Research
Md. Musfiqur Rahman is the Chief Strategy and Technology Officer of HaorGrix, a machine learning engineer and full-stack developer specializing in deep learning, computer vision, and AI-first product systems. He holds a B.Sc. in Computer Science & Engineering (major in Data Science) from United International University and works as a Machine Learning Engineer at Metamorphosis Ltd. He is a prolific IEEE-published researcher, with work including "Deep Learning-Based Recognition of Recaptured Images for Digital Media Authentication" (ICCIT 2026) and "A Reproducible Benchmark for Prompt Injection Vulnerability Assessment in Small-to-Medium Language Models" (ICCIT 2026). He is the founder and lead developer of ViolenceTracker.org, a fully automated LLM-driven political violence tracking system, and has delivered branding and web design for clients internationally.
Experience
Chief Strategy and Technology Officer
HaorGrix
- —Sets technology strategy and leads engineering across AI-first and full-stack product builds.
Machine Learning Engineer
Nov 2025 – PresentMetamorphosis Ltd
- —Builds machine learning solutions and drives digital transformation initiatives.
Graphic & Web Designer
2020 – PresentIndependent Freelancer
- —Delivers end-to-end branding and website design solutions for clients across multiple industries.
- —Level 1 Fiverr freelancer with 20+ international projects at consistent 5/5-star ratings.
Intern, Graphic Design Department
Youth School for Social Entrepreneurs (YSSE)
- —Created visual content for entrepreneurship campaigns while maintaining brand consistency and deadlines.
Publications & Research
- ◦Deep Learning-Based Recognition of Recaptured Images for Digital Media Authentication — IEEE Xplore, ICCIT 2026
- ◦A Reproducible Benchmark for Prompt Injection Vulnerability Assessment in Small-to-Medium Language Models — IEEE Xplore, ICCIT 2026
- ◦Plant Disease Recognition from the Perspective of Bangladesh: A Comparative Study of Deep Learning Models and Ensemble Techniques — IEEE Xplore, ECCE 2025
- ◦A Comparative Analysis of Various Deep Learning Models for Traffic Signs Recognition from the Perspective of Bangladesh — Springer Nature, BIM 2023
- ◦Bengali Fake News Detection: A Multi-Layered LSTM Ensemble Approach — IEEE Xplore, QPAIN 2025
- ◦Predictive Assessment and Social-Cost Estimation of Methane Emissions in Bio-Slurry Amended Systems — IEEE Xplore, STI 2025
- ◦Multi-Level Ensemble Learning for Fine-Grained Classification of Traditional Bangladeshi Dress Using Deep Transfer Models — IEEE Xplore, EICT 2025