Assaduzzaman Munna
Optimizing Intelligence. Scaling Architectures.
Bridging cutting-edge deep learning research with production-grade, edge-ready software. Specializing in computer vision models, knowledge distillation, multimodal temporal systems, and high-throughput deployment pipelines.
The Engineering Perspective
My technical core lies at the convergence of machine learning theory and system-level execution.
I am an AI/ML Engineer driven by the challenge of deployment efficiency. I believe artificial intelligence shouldn't just run on high-end remote server racks—it must be compressed, optimized, and ready to deploy at the edge to impact real-world latency-constrained operations.
Currently, I serve as a Junior AI Engineer at The Data Island, where I construct data processing pipelines and integrate robust machine learning frameworks for enterprise systems.
Through academic endeavors at North South University (CSE Department, CGPA: 3.75) and selective industry programs like the Nippon AI Dojo 2025 (mentored by Japanese industry leads from Chowa Giken and AI Samurai Japan), I have cultivated deep expertise in model distillation, neural network compression, and low-latency computer vision backend services.
Technical Matrix
Quantified competencies categorized by specialization layers.
Deep Learning & AI
Edge & Systems
Engineering Languages
Full-Stack Integration
Timeline of Impact
Industry roles and specialized training programs focused on high-performance AI deployment.
Junior AI Engineer
April 2026 - PresentThe Data Island
- Design, develop, and integrate enterprise AI frameworks, handling massive scale data ingestion.
- Establish high-efficiency, reliable data processing pipelines for machine learning operations.
- Collaborate closely with core developers to optimize deep learning training routines, significantly reducing inference compute times.
On-the-Job AI Trainee
Sep 2025 - Jan 2026Nippon AI Dojo 2025 (Chowa Giken & AI Samurai Japan)
- Selected as one of the top candidates for a highly intensive AI engineering and model optimization program.
- Engineered production-level vision systems and model pipelines under direct training and review of industry architects in Japan.
- Successfully completed hands-on challenges regarding neural compression, quantization, and real-time edge processing constraints.
Undergraduate Teaching Assistant
June 2025 - PresentNorth South University (ECE Dept.)
- Conduct review sessions and facilitate laboratory exercises in core computer science and engineering coursework.
- Provide direct instruction and programming mentorship to over 80+ undergraduate engineering students.
- Support faculty in structural curriculum reviews and evaluate engineering course deliverables.
Research & Publications
Peer-reviewed publications and manuscripts demonstrating optimized diagnostic systems and deep architectures.
Hybrid CNN-MobileViT Model with Knowledge Distillation
Submitted to SPICSCON 2026 (Under Review)Engineered a compact hybrid student model combining spatial CNN features and transformer-based self-attention loops for efficient Renal Calculi Detection in CT Images. Used knowledge distillation to transfer features from a massive ConvNeXt-Tiny teacher.
Bone Fracture Detection Using Vision Transformers
2026 IEEE 2nd International Conference on Secure IoT (SATC)Conducted a comparative analysis of Pooling-based Vision Transformer (PiT) and CaFormer architectures against standard convolution models using a dataset of 4,083 annotated clinical X-ray images. Demonstrated superior generalization vectors and robustness in localized anomaly classification.
Featured Systems
Industrial and experimental software systems engineered for efficiency, concurrency, and real-world deployment.
MIST-ER
Deep Learning ArchitectA multimodal temporal emotion recognition architecture processing high-frequency audio, video, and text streams concurrently to perform micro-expression classification.
- Achieved 54% categorical accuracy on the highly challenging and noisy MESC dataset.
- Designed a bidirectional attention synchronization layer matching video and audio frames in time.
- Implemented parameter quantization, allowing real-time deployment on edge devices.
Foil Stamping Inspection
System ArchitectA production-ready industrial automation computer vision system designed to inspect foil stamping lines for high-throughput manufacturing facilities.
- Designed high-performance backend pipelines using Python and multithreaded OpenCV frameworks.
- Optimized algorithms to handle sub-millisecond inspection cycles with extreme constraint boundaries.
- Deployed on client site manufacturing lines, improving structural inspection defect rates.
Establish Contact
I am open to discuss engineering roles, research collaborations, or technical architecture challenges. Reach out via direct email or professional networks.