Computer Vision Researcher in Training
Exploring efficient deep learning and computer vision for practical AI systems, especially on resource-constrained devices.
I am a Computer Science & Engineering undergraduate at Dhaka University of Engineering & Technology (DUET). My work focuses on computer vision, object detection, and lightweight deep-learning models. Currently, I am conducting research on rice panicle detection and growth-stage recognition across a 4,500-image dataset, aiming toward practical AI deployment on edge and mobile hardware.
Rice Panicle Detection and Growth-Stage Recognition
Exploring efficient vision models for edge devices across 3 key physiological stages.
Md Sayedul Islam
B.Sc. CSE Student (2023 – 2027)
Academic & Personal Projects
A selection of software applications and mobile platforms built while exploring computer science principles, utility features, and modern software architectures.
MyDUET
Integrated Mobile Application for DUET Services, designed to streamline student utility and academic support features.
Traffic Control System
Vehicle detection, camera-based image capture, and number-plate analysis.
Skills
Technologies, tools, and domains I work with
AI & Deep Learning
Embedded & Edge Systems
Programming & Web Development
Data Science & Analytics
Cyber Security & Tools
Currently Exploring
CURRENTLY RESEARCHING
Research
Ongoing research investigations, research interests, and academic inquiry in computer vision, deep learning, and efficient AI systems.
Areas of Focus
Artificial Intelligence
Learning fundamental concepts and exploring practical applications.
Machine Learning
Studying machine-learning concepts and experimenting with models and datasets.
Computer Vision
Exploring image-based problems, object detection, and visual recognition.
Deep Learning
Learning neural-network concepts and understanding modern deep-learning approaches.
Software Development
Strengthening programming and application-development skills through practical projects.
Ongoing Research Project
Rice Panicle Detection and Growth-Stage Recognition
Ongoing research investigating agricultural computer vision for rice panicle detection and growth-stage recognition across booting, heading, and maturity stages. Developing a custom dataset of 4,500 images (1,500 images per stage), with image annotation currently in progress using CVAT. Geared toward practical object detection and deep learning deployment on resource-constrained devices using efficient AI models.
Academic Background & Degrees
Formal engineering degrees, institutional academic achievements, academic grades, and computer science coursework.
B.Sc. in Computer Science & Engineering
Diploma in Engineering
Computer Technology
Leadership & Campus Activities
Active involvement in student organization management, technical event coordination, and campus extracurricular activities beyond formal academic coursework.
Executive Member
DUET Computer Society
Organizing competitive programming contests, technical seminars, student developer workshops, and department community events.
Joint Secretary
DUET Math Club
Coordinating mathematical problem-solving sessions, campus olympiads, and managing student club operations.
Let's Connect & Collaborate
Interested in AI research collaboration, joint project discussions, or software engineering opportunities? Send a message directly to my email or connect on academic networks.
Contact Channels
Email Address
mdsayedul764@gmail.com
LinkedIn Connection
linkedin.com/in/saidul07
GitHub Workspace
github.com/saidul-07
Phone & WhatsApp
+880 1727-183143