Abdullah Al Mamun
Applied Machine Learning, Data Science & AI Research
Python | Applied AI | Data Visualization | Voice & Audio Analytics | Research Writing
I am Abdullah Al Mamun, an MSc Computing for Data Science graduate from Bangor University with experience in machine learning, Python-based data analysis, data visualization, and applied AI research. My work focuses on turning datasets into clear models, useful insights, and practical research outputs across audio analytics, climate data, robotics, user-centred systems, and AI for real-world decision support.
Education & Current Position
Independent Researcher
Artificial Intelligence, Machine Learning & Data Science
MSc Computing for Data Science
Bangor University, UK • 2022–2023
Focused on machine learning, information visualisation, HCI/UX, Python analytics and research-based project development.
BSc Computer Science & Engineering
Dhaka International University, Bangladesh • 2016–2019
Final project involved AI applications on a DOF-17 humanoid robot using Arduino Mega and C programming.
Skills by Category
Machine Learning
Regression, classification, model evaluation, supervised learning and explainable workflows.
Programming & Data
Python, Pandas, NumPy, Scikit-learn, SciPy, Jupyter and reproducible analysis.
Visualization
Matplotlib, Seaborn, Plotly, chart design and evidence-focused storytelling.
Research Writing
Literature review, academic writing, paper preparation, presentation and publication workflow.
Specialist Areas
Explainable AI
Transparent and interpretable model development.
Voice & Audio Analytics
MFCC, similarity comparison and speaker-pattern analysis.
Computer Vision
Image, pattern and visual-data based AI applications.
HCI & UX
User-centred design thinking for digital systems.
Robotics & Intelligent Systems
AI concepts applied to physical and embedded platforms.
Climate Data Analysis
Trend analysis, regression and statistical interpretation.
Health & Wellbeing AI
Data-driven modelling for support systems and social impact.
NLP & Conversational AI
Planned work around dialogue systems and AI-assisted evaluation.
Career Direction
My goal is to build a focused research and technology profile around practical AI, reliable machine learning, data visualization, and human-centred intelligent systems. I aim to contribute to useful research outputs, publish credible work, and develop projects that connect academic ideas with real-world applications.
Selected AI, data science, voice analytics, visualization and robotics projects. The dedicated publication is highlighted separately in the Publications section.
Current & Completed Projects
Voice Similarity Detection using MFCC and Cosine Similarity
A Python-based voice comparison project using MFCC feature extraction and cosine similarity to analyse speaker-pattern similarity across audio samples.
Focus:
Audio preprocessing, feature extraction, similarity scoring, visualization and clear interpretation of voice-comparison outputs.
Technologies:
Climate Analysis using Machine Learning
Analysis of London weather data using aggregation, regression and statistical interpretation to explore long-term climate patterns.
Focus:
Data cleaning, trend exploration, statistical testing, visual storytelling and model-based interpretation.
Technologies:
AI Applications on DOF-17 Humanoid Robot
Undergraduate final-year project applying AI concepts to a DOF-17 humanoid robot platform using Arduino Mega and C programming.
Focus:
Motion control, embedded programming, robotics logic and practical system demonstration.
Technologies:
Work in Progress
AI-Driven Research: UK Housing
Exploring data-driven approaches to understand housing patterns, affordability and decision support.
Status: In preparation
Human Performance & Wellbeing Analytics
Planned work around AI and data science for wellbeing, behaviour and service-improvement contexts.
Status: In preparation
Research Philosophy
I believe AI and Data Science should be useful, explainable and responsibly developed. My work prioritises clear methods, reproducible analysis, understandable outputs and practical value for real-world users.
This section highlights my formal research publication and academic presentation work.
Published Research
Predicting Yield Strength of 3D-Printed Metal Components Using Machine Learning and Process Parameters
Abdullah Al Mamun, Akter, L., Abdul Based, M., & Almus Fuad, M.
Proceedings of the 3rd International Conference on Big Data, IoT and Machine Learning, BIM 2025
Published in Lecture Notes in Networks and Systems, Springer. DOI: 10.1007/978-3-032-15346-3_34
The paper presents a machine learning approach for predicting yield strength from process-parameter data, with emphasis on practical data modelling and research communication.
Talks & Presentations
Data Science for the Curious Mind – Turning Ideas into Intelligence
YSSE Information & Technology • November 25, 2025
An online session covering the fundamentals of Data Science, Machine Learning and Artificial Intelligence.
Watch RecordingPublication Pipeline
Voice Similarity Detection for Audio Authentication
A planned paper based on MFCC features and cosine similarity for voice-pattern comparison and audio authentication workflows.
ML-Based Mental Health and Wellbeing Analytics
Exploring applied machine learning methods for stress, anxiety, wellbeing and support-system analysis.
I'm always excited to discuss research opportunities, collaborate on projects, or share insights about machine learning and AI. Let's connect!
Contact Information
You can reach out to me directly through the channels below, or use the form to send a message. I typically respond within 1–2 days.
Location
Bangor, Gwynedd,
North Wales, United Kingdom.