Available for Research & Software Projects

Hello, I'm

Mejbah Uddin Bhuiyan

Passionate Computer Science and Engineering undergraduate at BRAC University with research experience in Artificial Intelligence, Deep Learning, and Bioinformatics. Experienced in Laravel, Python, Machine Learning, and Software Development, with a strong interest in building impactful solutions through technology and research.

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Mejbah Uddin Bhuiyan

Currently

Building Laravel Projects

Focus

AI • Research • Web

About Me

About Mejbah

A brief overview of my academic, research and development journey.

About Mejbah

I am an undergraduate student in Computer Science and Engineering at BRAC University with a strong passion for Artificial Intelligence, Machine Learning, Data Science, and Software Engineering. My academic journey has provided me with opportunities to work on research projects, software applications, and real-world problem-solving tasks. As a co-author of an IEEE conference publication, I have gained valuable experience in research methodology, deep learning, and multimodal data analysis. Alongside research, I actively develop web applications using Laravel and modern development tools, focusing on transforming ideas into practical digital solutions. I enjoy continuously learning emerging technologies and applying them to solve meaningful problems. My long-term goal is to contribute to innovative AI-driven systems that create a positive impact on education, healthcare, and society.

3+

Experience

8+

Projects

AI, ML, Deep learning Web Development

Research Interest

Download Resume
Academic Background

Education

My academic journey and educational achievements.

BSc in Computer Science and Engineering

BRAC University

Undergraduate student focusing on Software Engineering, AI and Data Science.

Higher Secondary Certificate

BAF Shaheen College Chattogram

Science background with strong academic performance.

Secondary School Certificate

CMP school and College

Science background with strong academic performance.

Technical Expertise

Skills

Technologies, programming languages and tools I work with.

Python

99%

Programming Languages

Java

90%

Programming Languages

JavaScript

80%

Programming Languages

PHP

93%

Programming Languages

SQL

90%

Programming Languages

Assembly (EMU8086)

85%

Programming Languages

Node.js

85%

Frameworks & Technologies

Laravel

95%

Frameworks & Technologies

Bootstrap

85%

Frameworks & Technologies

jQuery

85%

Frameworks & Technologies

MySQL

90%

Frameworks & Technologies

Git & GitHub

90%

Frameworks & Technologies

NumPy

90%

Data Science & AI

Pandas

90%

Data Science & AI

Matplotlib

90%

Data Science & AI

Machine Learning

90%

Data Science & AI

Deep Learning

90%

Data Science & AI

Data Analysis

90%

Data Science & AI

OpenGL

70%

Other

Unreal Engine

65%

Other

Problem Solving

98%

Other

Research Methodology

90%

Other

C language

70%

Programming Languages

Professional Growth

Certificates

Professional certifications, training programs and academic achievements.

Introduction to Data Analytics

Introduction to Data Analytics

BOHUBRIHI

June 26

Verify Download PDF
Certificate of Presentation – Multi-Modal Deep Learning for Single-Cell Mapping of Somatic-to-Germline Reprogramming in Infertility

Certificate of Presentation – Multi-Modal Deep Learning for Single-Cell Mapping of Somatic-to-Germline Reprogramming in Infertility

IEEE Bangladesh Section – WIECON-ECE 2025

22 December 2025

Download PDF
Undergrade Startup Challenge

Undergrade Startup Challenge

BUEDF

July 2025

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Introduction to Programming Using Python

Introduction to Programming Using Python

Datacamp

Jun 2026

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Featured Work

Projects

Selected projects from my GitHub and development work.

Jupyter Notebook

food-waste-prediction-ml

A machine learning regression project for predicting food waste using kitchen operational and environmental data.

Jupyter Notebook

GitHub
Arcade-Collection-in-8086-Assembly
Assembly

Arcade-Collection-in-8086-Assembly

GitHub project synced from repository.

Assembly

GitHub
Edupulse
Blade

Edupulse

GitHub project synced from repository.

Blade

GitHub
MejbahPortfolio
Blade

MejbahPortfolio

GitHub project synced from repository.

Blade

GitHub
Bullet-Frenzy
Python

Bullet-Frenzy

GitHub project synced from repository.

Python

GitHub
Car-Racing
Python

Car-Racing

GitHub project synced from repository.

Python

GitHub
DX-Ball
Python

DX-Ball

GitHub project synced from repository.

Python

GitHub
Catch-The-Diamond
Python

Catch-The-Diamond

GitHub project synced from repository.

Python

GitHub
Crimsys
HTML

Crimsys

GitHub project synced from repository.

HTML

GitHub
Research Focus

Research

Research projects, academic works, and scientific contributions.

Multi-Modal Deep Learning for Single-Cell Mapping of Somatic-to-Germline Reprogramming in Infertility
Deep Learning Completed

Multi-Modal Deep Learning for Single-Cell Mapping of Somatic-to-Germline Reprogramming in Infertility

IEEE

Infertility is a complex disease commonly linked with the disturbed germline development and oocyte maturation process. Here, we adopted a multimodal deep learning platform coupled with single-cell transcriptomic analysis to reveal the transcriptional variation and developmental differentiation trajectory of infertility. Public datasets, including three disease states with more than 25,000 cells and 3,000 expressed features, were harmonized using PCA and Harmony for batch effect c orrection, f ollowed b y U MAP visualization and Leiden clustering, which yielded seven transcriptionally distinct clusters. Diffusion pseudotime analysis inferred a developmental continuum between early somatic-like and late germline-enriched populations, identifying progressions in the absence of defined panels of canonical oocyte and germline markers. Differentially expressed top 10 marker genes per cluster indicated candidate control factors for reproductive failure. Functional enrichment analysis validated the remarkable participation of pathways in meiotic recombination, germline stem cell maintenance, and oocyte maturation. Predictive modeling with Logistic Regression and Random Forest classifiers fitted on integrated embeddings achieved a maximum AUROC of 0.998 and AUPRC of 0.996 using Random Forest and MLP models, confirming strong discriminative performance. Together, our findings s how t hat t he c ombination of deep learning and single-cell multi-omics offers a potent approach to map cellular heterogeneity in infertility, reconstruct developmental dynamics, and discover new biomarkers and therapeutic targets. Index Terms—Multi-modal deep learning, Single-cell multi-omics, Somatic-to-germline reprogramming, Infertility, Pseudotime trajectories, Biomarker discovery, Predictive modeling

Collaborators: Tanvinur Rahman Siam , Tanjina Pioush Bhuiyan , Mejbah Uddin Bhuiyan , Tahmidur Rahman Sitam

View Publication
Latest Articles

Blog

Thoughts, tutorials, research notes, and development articles.

AI-Assisted Software Engineering: The Future of Development
Software Engineering

AI-Assisted Software Engineering: The Future of Development

AI-Assisted Software Engineering: The Future of Development Category: Software Engineering Artificial Intelligence...

Jun 28, 2026

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Why Data Quality Matters More Than Bigger Models
Machine Learning

Why Data Quality Matters More Than Bigger Models

Modern machine learning models are becoming increasingly powerful, but their success still depends on one critical facto...

Jun 28, 2026

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The Rise of AI Agents: The Next Revolution Beyond Chatbots
Artificial Intelligence

The Rise of AI Agents: The Next Revolution Beyond Chatbots

Artificial Intelligence is rapidly evolving from simple chatbots into autonomous AI agents capable of planning, reasonin...

Jun 28, 2026

Read More
Get In Touch

Contact

Feel free to contact me for research collaborations, projects, academic discussions, or professional opportunities.

Contact Information

Email

mejbahu475@gmail.com

Phone

01707851390

Location

Dhaka , Bangladesh

Connect With Me

Social Platforms

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