About Me
Hello, I'm Moinul Islam. I have professional skills in Python programming, deep learning, software testing, SQL, and excellent written & spoken communication abilities. Love to explore the programming world and solve real-life problems. Interested in working in research-based projects in deep learning, neural networks, and image processing fields. Aspiring to develop autonomous solutions for a better world. Online CV
Experience
Software Engineer II, QA at Enosis Solutions
November 2021 – Present• Implementing test automation, CI/CD, writing test cases, formulating bug reports, cross browser testing.
• Performing various web and mob testing schemes (functional, regression, system, integration, exploratory, non-fuctional).
• Perform R&D on tools and technologies to improve daily activities, Client communication
• Test environments setup, configurations, and troubleshooting
Research Assistant at Multimedia Signal & Image Processing Research Group
January 2021 – September 2021• Under the supervision of Dr. Jia Uddin, Woosong University.
• Research areas: Deep learning, image processing, and fault signal detection.
• Writing journal papers, code implementation, creating graphical designs.
Undergraduate Teaching Assistant at BRAC University Dept. of CSE
September 2019 – September 2020• Course Taught: Digital Logic Design.
• Gave consultation hours to a large community of students, checked & graded assignments.
• Took lab classes alongside the faculty, worked with supervisor in developing strategies.
Education
Bachelor of Science in Computer Sceince and Engineering at BRAC University
May 2016 – October 2020• CGPA 3.75+ (Out of 4)
Publication
Federated Learning and CNN Ensemble Architectures for Identifying Brain Tumors
August 2022• Authors: Moinul Islam, Md Tanzim Reza, Mohammed Kaosar, and Mohammad Zavid Parvez
• Journal: Neural Processing Letters - [Springer link of the article]
Projects
CNN ensemble architectures and federated learning for classifying brain tumor
July 2020 – October 2020• Extracted 3D MRI NIfTI images into 2D. Used six CNN architectures: VGG16, VGG19, Inception V3, ResNet50, DenseNet121, and Xception. Designed average & voting ensemble by taking the best 3 models. Further, FL was created by implementing a global model with an average CNN model in a central server that receives an updated weight trained by local data (in client-server). Calculated sensitivity, specificity, AUC, DSC, and test accuracy to justify models’ performance.
Deep CNN based model to detect Covid-19 from X-ray scans
January 2021 – March 2021• Applied manual CNN architecture and transfer learning to detect covid-19 from chest X-ray images. Manual CNN model built with five Conv2D layers, 2 FC, and the sigmoid activation function to classify covid+ or covid-. DenseNet121 was applied as a transfer learning model. After comparison, received 96.93\% & 97.43\% accuracy.
Industrial fault diagnosis using Hilbert transform and RNN-CNN
March 2021 – April 2021• Took input of standard signal & 3 faults signal of inner, outer, centered of bearing motors. Implemented Hilbert transform to produced absolute signal and constructed 2D images of that. Last, performed CNN-LSTM model to train and test for identifying fault signals.
TensorFlow Guided Projects (Coursera)
July 2020• RPS Categorical Classification
• Sign langauge Multiclass Classification
• Horse vs Human with InceptionV3
Software quality assurance
May 2021 – Present• Test automation: Selenium and Cypress
• Interacting with MySQL database
Front End Web Designing
May 2020 – PresentCisco Networking Project
September 2019 – December 2019• Based on routing algorithm, NAT-PAT, ACL, DHCP
Relevant Coursework
Neural Networks and Deep Learning
Coursera• Gradient Descent
• Logistic Regression
• Activation Function
• Neural Network with Hidden Layers
Machine Learning with Python
Cognitive AI• Support Vector Machine (SVM)
• Decision Trees
• Intro to Recommender Systems
• K-Nearest Neighbors
Python for Data Science
Cognitive AI• Working with Pandas
• Numpy 2D Arrays
• Expressions and Variables
• Lists and Tuples and Functions
CNN in Tensorflow
Coursera• Transfer Learning
• Image Augmentation
• Multiclass Classification
• Dropout Layer
Volunteer Experience
Student Mentor at BRAC University
May 2019 – September 2020• Guided new students to various resources opportunities that are available on campus
• Acted as a point of contact for the students and monitored their performance regularly
Intern at Institute of Wellbeing
September 2017• Organized seminar and fair at BRAC University Savar Campus
• Proposal about car-free city, healthy canteen, car parking, etc
Extracurricular Activities
Graphic Designing
April 2020• Adobe Illustrator | Certificate
• Canva
Book Reading Competition at British Council
April 2016• Under BRAC University | Certificate
Animated Flim Making
January 2017 - April 2017• BRACU RS activity | Certificate
Content Creation
July 2022Programming Skills
Python - Professional working proficiency
Javascript - Elementary proficiency
Language Skills
1. Bengali (Native)
2. English (Full professional proficiency)
3. French (Elementary proficiency)
4. Chinese (Elementary proficiency)
Other Skills
Libraries & frameworks: Cypress, Selenium, TensorFlow, Keras, Pandas, Bootstrap, Django
Development tools: Git, Sourcetree, Postman, Sauce Labs, ClickUp, Azure DevOps, MySQL workbench, VS Code, LaTeX
Fields of Interest: Machine Learning, Deep Learning, Neural Networks, Data Science.
Personal Skills: Teaching, Adaptibility, Teamwork, Leadership.
Awards
Academic Distinction: High distinction academic honors at graduation.
Vice Chancellor's List certificate: Won a place on VC's list 5 times for achieving a GPA above 3.90. Undergraduate students must meet the minimum GPA of 3.7 and above with no retake or repeat are eligible for VC's List certificate.
Dean's List: Awarded for having a decent academic performance.
BRACU Performance Based Scholarship: Based on the upper percentile CGPA.