Research Interests
- Deep Learning
- Machine Learning
- Artificial Intelligence
- Data Science
Education
University of Liberal Arts Bangladesh
Bachelor of Science - Computer Science and Engineering
Dhaka, Bangladesh
June 2019 - February 2023
CGPA: 3.96/4.00
Courses: Programming, Differential and Integral Calculus, Co-ordinate Geometry and Linear Algebra, Statistics and Probability, Digital Image Processing, Artificial Intelligence, and Introduction to Robotics.
Govt. Mohammadpur Model School and College
Higher Secondary School Certificate - Science
Dhaka, Bangladesh
February 2014 - February 2016
GPA: 5.00/5.00
Mohammadpur Government High School
Secondary School Certificate - Science
Dhaka, Bangladesh
January 2012 - January 2014
GPA: 5.00/5.00
Publications
1. Md. Mutasim Billah Abu Noman Akanda, Maruf Ahmed, AKM Shahariar Azad Rabby, and Fuad Rahman. 2024. Optimum Deep Learning Method for Document Layout Analysis in Low Resource Languages. In Proceedings of the 2024 ACM Southeast Conference (ACM SE ’24). Association for Computing Machinery, New York, NY, USA, 199–204. https://doi.org/10.1145/3603287.3651184
2. Akanda, M.B.A.N., Prodhan, M., Sarwar, S., Raatul, A.M., Paul, B. (2023). Voice Controlled Home Automation with Cloud-Based Environment Monitoring System. In: Joshi, A., Mahmud, M., Ragel, R.G. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2022). ICTCS 2022. Lecture Notes in Networks and Systems, vol 623. Springer, Singapore. https://doi.org/10.1007/978-981-19-9638-2_21
Work Experience
Green Pants Studio
AI Engineer I
Remote (Texas, United States)
June 2024 - present
Assigned Projects: Vendidit
Responsibilities: Conducting research and developments regarding Vendidit Scraper, Fair Market Valuation, Mapping AI pipelines, etc.
Apurba Technologies Ltd.
Machine Learning Engineer
On-site (Dhaka, Bangladesh)
Mar 2023 - May 2024
Assigned Projects: Bengali OCR (funded by the ICT Ministry of Bangladesh)
Responsibilities: Conducting research and developments regarding Noise Removal, Document Layout Analysis, Scene Text Detection, and Word Recognition pipelines.
University of Liberal Arts Bangladesh
Teaching Assistant, Department of Computer Science and Engineering
On-site (Dhaka, Bangladesh)
Oct 2022- Jan 2023
Assigned Courses: Introduction to Computer Programming, Structured Programming, Artificial Intelligence, and Software Engineering.
Responsibilities: Conducting tutorials and labs, reviewing sessions, helping with coursework evaluation, preparing course files, and counseling students.
University of Liberal Arts Bangladesh
Peer Mentor, Student Affairs Office (SAO)
On-site (Dhaka, Bangladesh)
Feb 2022- Nov 2022
No. of Students: 30
Responsibilities: Mentoring a group of students very closely both in academic and non-academic life, guiding students with proper resources.
University of Liberal Arts Bangladesh
Student Prefect, Department of Computer Science and Engineering
On-site (Dhaka, Bangladesh)
Feb 2021- Jan 2022
Assigned Courses: Structured Programming, and Object-oriented Programming.
Responsibilities: Preparing assignments, evaluating student works, counseling students, and preparing grade reports.
Honors & Awards
- Summa Cum Laude (April 2024)
- Appreciation for Peer Mentor (Nov 2022)
- Vice Chancellor’s Honors List Scholarship Summer 2021 (Jan 2022)
- Best Organizer Award of “Idea Hunters 2.0” organized by ULAB Digital Marketing Club (Oct 2021)
- Dean’s List Scholarship Spring 2021 (Sep 2021)
- Dean’s List Scholarship Fall 2020 (May 2021)
- Dean’s List Scholarship Summer 2020 (Dec 2020)
Community Activities
University of Liberal Arts Bangladesh
President, ULAB Computer Programming Club
On-site (Dhaka, Bangladesh)
Jul 2022- Feb 2023
Responsibilities: Communicating with the internal and external communities of ULAB for organizing various events, and delegating the required tasks to the other executive members.
University of Liberal Arts Bangladesh
Vice President, ULAB Computer Programming Club
On-site (Dhaka, Bangladesh)
Feb 2022- Jul 2022
Responsibilities: Assisting the President as required in the fulfillment of tasks and goals, and leading other executive members as required.
Skills Summary
Programming Languages: Python, C++, C.
Machine Learning Skills: PyTorch, TensorFlow, Scikit-learn, OpenCV, Matplotlib, Numpy, Pandas, Tesseract, Ultralytics, Hugging Face, Docker, Streamlit, Uvicorn, Roboflow, ScrapeGraphAI, LangChain, Ollama, Retrieval-Augmented Generation (RAG), ChromaDB, LanceDB.
API Skills: JMeter, Postman, FastAPI.
Server Management Skills: AWS S3, EC2, ECS, ECR, Termius.
Project Management Skills: Slack, Git & Github, Trello, Jira, Notion, Whimsical.
Miscellaneous: Apify, Playwright, BeautifulSoup4, LaTeX, Anaconda, Linux, Bash, NMAP, ZAP, Metasploit Framework.
Soft Skills: Research, Communication Skills, Team Leadership, Public Speaking, Analytical Skills, Presentation Skills, Time Management, Problem Solving.
Achievements
- Problem Setter in “ULAB Take off Programming Contest Summer 2022” (Aug 2022)
- 2nd Runners Up in ”ULAB Take Off Programming Contest Fall 2021” (Oct 2021)
- Qualified for the prototype round in the “CTO Forum Innovation Hackathon” (Sep 2021)
- 1st Runners Up in ”ULAB Take Off Programming Contest Spring 2021” (Feb 2021)
Machine Learning Projects
RAG-based Local Chat Box
Developed a Streamlit-based web application designed to integrate a Local Language Model (LLM) with Retrieval-Augmented Generation (RAG) capabilities. This project allows users to index documents, create embeddings, and interact with their data through an intuitive chat interface powered by state-of-the-art language models. Ideal for researchers and developers, the rag-based-local-chat-box facilitates efficient data retrieval and conversational interactions within a local environment.
Tech: Python, ChromaDB, Langchain, Ollama, Pypdf, Pandas, Streamlit
GitHub Link: https://github.com/noman024/rag-based-local-chat-box.git
Langchain-based Web Scraping with ScrapeGraphAI
Implemented Langchain-based web scraping using ScrapeGraphAI. It enables users to extract data from any website without understanding the HTML or page layout using customizable graph pipelines and prompts. The scraped data is then structured and saved into JSON, facilitating easy analysis and further processing.
Tech: Python, ScrapeGraphAI, Langchain, WebKit, Ollama, Playwright
GitHub Link: https://github.com/noman024/Langchain-based-Web-Scraping-with-ScrapeGraphAI.git
Parking Spot Tracking
Created a tracking system that implements centroid tracking with Euclidean distance and YOLOv9 object detection. It allows for the extraction of frames from an overhead CCTV video, performs object detection on each frame to detect vehicles, and tracks their centroids across frames.
Tech: Python, OpenCV, Torch, Ultralytics, YOLOv9, Numpy, JSON.
GitHub Link: https://github.com/noman024/parking-spot-tracking.git
Web Scraper
Created a web scraping tool designed to extract data from any kind of web page. It provides users with the flexibility to define any number and structure of schema as long as the associated attributes are present in the webpage’s HTML elements.
Tech: Streamlit, FastAPI, JSON, BeautifulSoup4, Python Request Module.
GitHub Link: https://github.com/noman024/web-scraper.git
Game Addiction Analysis with Neural Network (Deep Learning)
Created a neural network from scratch that can predict whether a student can have a physical disorder (eyesight or hearing issue) due to game addiction. The model was trained on a new dataset which was prepared based on some survey question-answers collected from different private universities in Dhaka, Bangladesh. The model has achieved 80.70% of accuracy.
Architecture: Neural Network
Tech: Tensorflow, Keras, Scikit-learn, Seaborn, Matplotlib, Numpy, Pandas.
GitHub Link: https://github.com/noman024/game-addiction-analysis-neuralnet.git
News Classification with Naive Bayes Theorem (Machine Learning)
Applied Multinomial Naive Bayes classifier that can classify news between ham and spam based on Naive Bayes Theorem. The model was trained on a dataset that is already provided in Pandas. The model has achieved 98.85% of accuracy.
Architecture: Multinomial Naive Bayes classifier
Tech: Scikit-learn, Numpy, Pandas.
GitHub Link: https://github.com/noman024/news-classification-with-naive-bayes.git
Diabetic Classification with Support Vector Machine (Machine Learning)
Applied Support Vector Classifier that can classify diabetics between positive and negative based on some features collected from patients. The model was trained on a dataset collected from Kaggle. The model has achieved 80.51% of accuracy.
Architecture: Support Vector Machine
Tech: Scikit-learn, Seaborn, Matplotlib, Numpy, Pandas.
GitHub Link: https://github.com/noman024/diabetic-classification-with-svm.git