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hamidhosen42/README.md

Hi there, I'm Md. Hamid Hosen 👋

Software Engineer (Mobile) | Lead Researcher | AI Researcher

Website LinkedIn Kaggle Hugging Face Email


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👨‍💻 About Me

I am Md. Hamid Hosen, a Software Engineer (Mobile) at
Project 2morrow Software Ltd. (P2M) and a Lead Researcher at
ELITE Research Lab LLC.

I develop scalable and high-performance mobile applications using Flutter, Dart, Kotlin, Jetpack Compose, and modern mobile software architectures.

Alongside software engineering, I conduct research in Large Language Models (LLMs), Mathematical Reasoning, Computer Vision, Multimodal AI, Explainable AI, and Natural Language Processing.

My work focuses on connecting advanced AI research with reliable, production-ready software systems.

  • 💼 Software Engineer (Mobile) at P2M Software Ltd.
  • 🔬 Lead Researcher at ELITE Research Lab LLC
  • 🎓 B.Sc. in Computer Science and Engineering
  • 🏫 East Delta University, Bangladesh
  • 🌍 Interested in international M.Sc. and Ph.D. research opportunities
  • 🤝 Open to AI research, software engineering, and open-source collaboration

🏆 International Achievements

🏆 AIMO Proof Pilot — Prize Winner

Recognized as a Prize Winner in the AI Mathematical Olympiad Proof Pilot, an invitation-only AI research evaluation hosted on Kaggle as part of the AIMO Prize initiative.

  • One of only seven invited teams worldwide
  • Worked with fully open-source Large Language Models
  • Developed mathematical reasoning and proof-generation workflows
  • Conducted inference optimization and reproducible evaluation
  • Prepared containerized deployment and technical reports

🥇 AIMO Progress Prize 3 — Gold Medalist

Earned a Gold Medal in the AI Mathematical Olympiad Progress Prize 3.

  • Global Rank: 14th
  • Overall Score: 43.5
  • Competed among 4,138 teams worldwide

🏆 Hardest Problem Prize Winner

Received the Hardest Problem Prize for being the only contestant among 4,138 teams to solve the highly resistant mathematical problem internally referred to as “ACUTES” in both evaluation attempts.

🔬 Research Interests

  • 🧠 Large Language Models
  • ➗ Mathematical Reasoning
  • 🔀 Multimodal AI and Multimodal Fusion
  • 👁️ Computer Vision
  • 🔍 Explainable and Trustworthy AI
  • 💬 Natural Language Processing
  • 📱 AI-powered Mobile Applications
  • 🩺 Medical AI and Mobile Health
  • 🎨 Vision-based UI Understanding
  • ⚙️ Reproducible AI Evaluation
  • 🚀 AI Inference and Optimization

💼 Current Roles

Software Engineer (Mobile)

Project 2morrow Software Ltd. — P2M

  • Building scalable cross-platform mobile applications with Flutter
  • Developing native Android applications with Kotlin and Jetpack Compose
  • Integrating REST APIs, Firebase, authentication, and real-time services
  • Creating responsive applications for phones and tablets
  • Applying clean architecture and maintainable state management

Lead Researcher

ELITE Research Lab LLC — Remote

  • Leading research in LLMs, NLP, Computer Vision, and Multimodal AI
  • Designing model evaluation and benchmarking pipelines
  • Conducting AI experiments and performance analysis
  • Supporting research collaborators and publication workflows
  • Contributing to technical reports and open-source AI systems

💻 Tech Stack

📱 Mobile Development

Flutter Dart Kotlin Android Jetpack Compose Swift

🧑‍💻 Programming Languages

Python C C++ LaTeX

🤖 Artificial Intelligence and Data Science

PyTorch Keras Scikit-learn Pandas NumPy Matplotlib Hugging Face Kaggle

⚙️ Backend, Database and Cloud

FastAPI GraphQL Firebase Supabase MongoDB SQLite JWT

🛠️ Tools and Platforms

Git GitHub Figma Anaconda

📊 GitHub Statistics

GitHub Profile Details



GitHub Statistics

GitHub Productive Time



Repositories per Language

Most Commit Language



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GitHub statistics are generated by third-party services. A card may temporarily become unavailable because of API rate limits.

🌐 Connect With Me

Website LinkedIn Kaggle Hugging Face Facebook Instagram X YouTube Email

💡 Building intelligent mobile systems and advancing open-source AI research.


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    This project presents an explainable AI-based glaucoma diagnosis system using deep learning and Vision Transformers (ViTs). Retinal fundus images are preprocessed with techniques like CLAHE and edg…

    Jupyter Notebook

  2. Predicting-Stress-in-Bangladeshi-University-Students-A-LIME-Interpretable-Machine-Learning-Approach Predicting-Stress-in-Bangladeshi-University-Students-A-LIME-Interpretable-Machine-Learning-Approach Public

    This study predicts stress levels among Bangladeshi university students using machine learning and explainable AI (LIME). The model, primarily using Support Vector Classifier (SVC), identifies at-r…

    Jupyter Notebook

  3. Unveiling-Predictive-Factors-in-Apple-Quality-Leveraging-LIME-SHAP-and-the-Synergy-of-Machine Unveiling-Predictive-Factors-in-Apple-Quality-Leveraging-LIME-SHAP-and-the-Synergy-of-Machine Public

    This repository contains research from "Unveiling Predictive Factors in Apple Quality," presented at ICEEICT 2024. It covers apple quality classification using ANN (92.87% accuracy) and XAI tools (…

    Jupyter Notebook

  4. Enhancing-Food-Security-EfficientNet-B0-XAI-in-Plant-Disease-Classification Enhancing-Food-Security-EfficientNet-B0-XAI-in-Plant-Disease-Classification Public

    This project leverages deep learning models, particularly EfficientNet-B0, to classify 38 plant diseases with an accuracy of 99.86%. Using advanced techniques like image segmentation (DeePLabV3+) a…

    Jupyter Notebook

  5. Pothole-Detection-Using-Transfer-Learning-Models-A-Comparative-Study Pothole-Detection-Using-Transfer-Learning-Models-A-Comparative-Study Public

    Pothole Detection Using Transfer Learning Models: A Comparative Study

    Jupyter Notebook

  6. CIFAKE-Explainable-Deep-Learning-for-Classifying-Real-and-AI-Generated-Images-Using-CNN-and-3D-CNN CIFAKE-Explainable-Deep-Learning-for-Classifying-Real-and-AI-Generated-Images-Using-CNN-and-3D-CNN Public

    I work on deep learning models that identify AI-generated images using CNNs, 3D-CNNs, and interpretable AI. My projects focus on binary classification of real vs synthetic imagery, dataset engineer…

    Jupyter Notebook