Bachelor of Engineering (BE) in Computer Science
Shahjalal University of Science and Technology, Sylhet, Bangladesh
Feb 2019 – Dec 2024
CGPA: 3.74/4.00 (Last 4 Semesters: 3.90/4.00)
Higher Secondary Certificate (HSC) in Science
Notre Dame College, Dhaka, Bangladesh
Jul 2016 – Jun 2018
GPA: 5.00/5.00
Awarded General Grade Government Scholarship for academic excellence.
Secondary School Certificate (SSC) in Science
Shamsul Hoque Khan School & College, Dhaka, Bangladesh
Jan 2009 – Feb 2016
GPA: 5.00/5.00
Work History
- AI Engineer
Synesis IT PLC, Dhaka, Bangladesh
Sep 2024 – Mar 2025
Low Resource Chatbot Development - NLP Engineer
Synesis IT PLC, Dhaka, Bangladesh
Mar 2024 – Aug 2024
Low Resource ASR, and Callbot Development
Extra Curricular Activities
- Reviewed papers for ICCIT 2024.
- Mentoring 2+ undergraduate thesis teams from SUST during undergraduate on: Bangladeshi Regional Audio Classification, Ethnic Language-Based Text-to-Speech (TTS) Synthesis System, and Dataset Creation.
- Mentoring 3+ undergraduate thesis teams from SUST on: LLMs Evaluation, Regional Language Translation, and Dataset Creation.
- Co-hosted DL Enigma 1.0, part of SUST CSE CARNIVAL 2024.
Teaching
- CSE 411: Machine Learning (221_D10) [Summer, 2025]
- CSE 412: Machine Learning Lab (221_D20) [Summer, 2025]
- CSE 436: Data Mining Lab (221_E2) [Summer, 2025] (213_D5, 213_E1) [Spring, 2025]
- CSE 100: Computational Thinking and Problem Solving (250_D4) [Summer, 2025]
- CSE 435: Data Mining (212_D3) [Spring, 2025]
- CSE 406: Integrated Design Project II (221_D15) [Spring, 2025]
- CSE 310: Operating System Lab (223_E2) [Spring, 2025]
- CSE 202: Object Oriented Programming Lab (241_D5) [Spring, 2025]
Thesis Supervision / Co-Supervision
- Supervising 1 thesis group working on Deepfake Speech [Summer, 2025] (No further supervision commitments at GUB planned unless recommend by a Professor or SUST alumni faculty).
- Co-supervising 2+ thesis groups working on Machine Translation and LLM Evaluation with Prof. Dr. Md Saiful Azad [Summer, 2025].
- Co-supervising 1+ thesis groups working on Natural Language Processing with Md. Parvez Hossain [Summer, 2025].
- Co-supervising 1+ project groups working on a Medical Image Diagnosis System with Md. Riad Hassan [Spring, 2025].
- Co-supervising 2+ thesis groups working on Speech Processing and Reinforcement Learning with Dr. Muhammad Aminur Rahaman [Fall, 2024].
Google Scholar: https://scholar.google.com/citations?user=N2dzzwsAAAAJ&hl=en
Journal Publications
- Md Shahidul Islam, Pabel Shahrear, Goutam Saha, Md Ataullha, and M. Shahidur Rahman. “Mathematical analysis and prediction of future outbreak of dengue on time-varying contact rate using machine learning approach.” Computers in Biology and Medicine, vol. 178, 2024, p. 108707. [Journal]
Conference Proceedings
- Md Ataullha, Mushfiqur Rahman, and M. Shahidur Rahman. “PakhiderChobi: A Comprehensive Dataset for Real-time Detection of Bangladeshi Birds.” 2023 26th International Conference on Computer and Information Technology (ICCIT). IEEE, 2023. [Conference]
- Md Ataullha, Soumik Paul Jisun, Ishrat Jahan, Mushfiqur Rahman. “BanFish: A Dataset for Classifying Common Bangladeshi Fish Species.” 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT). IEEE, 2024. [Conference]
- Md Ataullha, Mahedi Hassan Rabby, Mushfiqur Rahman, Tahsina Bintay Azam “Bengali Document Layout Analysis with Detectron2.” arXiv preprint arXiv:2308.13769, 2023. [arXiv]
- Kaggle Notebook Expert (1x Expert) [Kaggle Profile]
My research explores innovative applications of Artificial Intelligence in low-resource environments, with a strong emphasis on:
- Large Language Models (LLMs)
- Bangla Speaker Diarization
- Bangla Automatic Speech Recognition (ASR)
- Bangla Text-to-Speech Synthesis (TTS)
- Machine-learning-driven AI products
- Domain Specific Chatbot
- Time Series Forecasting
- Object Detection
- Dataset Creation
I aim to advance speech processing, natural language understanding (NLU), and text-to-speech (TTS) systems to develop accessible AI products that drive progress in technology, education, and conservation.
Supervisor
- Dr. Mohammad Shahidur Rahman
Professor
Department of Computer Science and Engineering
Shahjalal University of Science and Technology, Sylhet, Bangladesh
Email: rahmanms@sust.edu
Mobile: +880 1914-930807
Faculty Profile: https://www.sust.edu/departments/cse/faculty/rahmanms@sust.edu
Relation: Thesis Supervisor & Mentor
Project Involvement: Actively engaged in 2+ research projects under his guidance.
Featured Projects
- StarGaze BD (ML Algorithms: RF, SVM, LR, CNN, Flask, JavaScript, Streamlit)
A web application for identifying prominent Bangladeshi personalities from photos using machine learning and deep learning. It leverages custom ML algorithms (Random Forest, SVM, Logistic Regression) and a CNN model for high accuracy, with Flask handling backend processing and a user-friendly interface built with JavaScript and Streamlit. [GitHub Link] - Freshman Utilities (Java, Firebase, Google Directions API, Jsoup)
An Android app designed to assist university freshmen with campus navigation using Google Directions API, Firebase-based resource sharing, and web scraping with Jsoup to collect essential campus-related information. [GitHub Link] - CourseTrackr (Java, Servlet, JSP, MySQL)
A web-based course management system featuring role-based access for admins, teachers, and students. Developed using Java Servlets and JSP for dynamic web pages and MySQL for backend database management. [GitHub Link] - QuesTek Pro (Java, JavaFX, Text Files)
A JavaFX-based desktop application for conducting and managing multiple-choice quizzes. It includes admin verification, quiz result exportation, and local data storage using text files—ideal for in-person examinations. [GitHub Link]
Recent Talks & Interviews
- Presentation at ICCIT’23 (26th ICCIT, Cox’s Bazar, December 2023)
Delivered a presentation on PakhiderChobi, a dataset featuring 8,670 annotated images of 33 Bangladeshi bird species. The talk highlighted the importance of accurate bird detection and classification for urban conservation, leveraging the YOLOv8 object detection model to achieve 95.3% mAP with an inference time of 6.6 milliseconds per image. This tool supports real-time bird recognition, even in challenging environments, contributing to conservation efforts in the face of urbanization and habitat loss. [Watch on YouTube]