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MH

Md. Mehedi Hasan

AI × Healthcare × Data × Research

CSE graduate and AI/ML researcher with experience in data annotation, data analysis, machine learning, deep learning, and Explainable AI. Research experience spans healthcare, biomedical AI, medical imaging, and graph-based learning, with an IEEE conference publication and a manuscript under review. Currently working as a Data Annotation Analyst at Rooya AI and Research Assistant at AMIRL.

Open to
MSc / PhD positionsResearch collaborationAI/ML engineering roles
Md. Mehedi Hasan — portrait

At a glance

Research

Research directions

Deep learning for clinical imaging. Evidenced by the structural-MRI dementia classification manuscript under review at Neuroscience Informatics, and by the NeuroVision AI and RetinaVision AI systems.

Focus

  • Medical imaging
  • Biomedical AI
  • Classification
  • Segmentation

Methods

  • Multiple instance learning
  • Transfer learning
  • Patient-level splitting

Graph and transformer architectures applied to biological interaction data. Evidenced by X-GNN, published at IEEE ICECTE 2026, on human protein–protein interaction prediction.

Focus

  • Graph neural networks
  • Protein–protein interaction
  • Ensemble learning

Methods

  • Graph neural networks
  • Transformers
  • Ensemble methods
ActiveFeatured

Explainable AI

Making model decisions inspectable, which is a precondition for clinical use rather than an add-on. Evidenced by Grad-CAM in NeuroVision AI and RetinaVision AI, SHAP in the Parkinson's work, and "Explainable" in the title of the published paper.

Focus

  • Model interpretability
  • Saliency methods
  • Clinical trust

Methods

  • Grad-CAM
  • SHAP

Research methods

Methods I work with

Problem

  • Problem Formulation
  • Research Question
  • Use-case Analysis
  • Stakeholder / Domain Analysis
  • Literature Review
  • Research Gap Identification

Technical stack

Languages, frameworks and tools

AI / ML

  • PyTorchResearch use
  • Scikit-learn
  • NumPy
  • Pandas
  • Matplotlib
  • Seaborn
  • TensorFlow
  • TransformersResearch use
  • CUDA
  • OpenCV

Data

  • MySQLApplied
  • PostgreSQLProduction use
  • MS SQL Server
  • FirebaseApplied
  • SupabaseProduction use
  • CVATApplied
  • Image annotationProduction use
  • Object detection
  • Image segmentationResearch use
  • Object trackingApplied
  • Keypoint annotationApplied
  • Annotation quality controlProduction use

Engineering

  • PythonResearch use
  • SQLApplied
  • C++
  • JavaApplied
  • JavaScriptApplied
  • HTMLApplied
  • CSSApplied
  • JSPApplied
  • ServletApplied
  • JDBC
  • MavenApplied
  • GitApplied
  • Visual Studio
  • EclipseApplied
  • Apache TomcatApplied
  • TypeScriptProduction use
  • ReactProduction use
  • Next.jsProduction use
  • Tailwind CSSProduction use
  • Node.jsProduction use

Tools

  • Docker
  • Linux
  • Google Colab
  • Kaggle
  • Anaconda
  • LaTeX
  • draw.io
  • Canva
  • MS Office

Projects

Selected projects

Medical AI / Medical ImagingFeaturedAug 2026 – Aug 2026

Cardio Vision AI

Multimodal Cardiovascular AI for Echocardiography, CCTA, and ECG

A research prototype integrating trained AI models for echocardiography, CCTA, and 12 lead ECG analysis with explainability, evidence aggregation, and MedGemma assisted reporting.

0.9044

Dice

Echocardiography test Dice on the CAMUS held out split of 75 patients and 300 image/mask pairs.

0.8282

IoU

Echocardiography test IoU on the CAMUS held out split of 75 patients and 300 image/mask pairs.

  • Python
  • PyTorch
  • FastAPI
  • React
  • Streamlit
  • SQLite
  • +8 more

0 stars on GitHub0 forks on GitHubPython

Healthcare AI / Medical ImagingFeatured

NeuroVision AI

Explainable Brain MRI Classification

End-to-end deep learning system for three-class brain tumor classification using an ImageNet-pretrained EfficientNet-B0 model.

91.14%

Accuracy

90.27%

Macro F1

  • Python
  • PyTorch
  • EfficientNet-B0
  • Grad-CAM
  • FastAPI
  • React
  • +2 more
Healthcare AI / Medical ImagingFeatured

RetinaVision AI

Explainable Retinal Image Analysis

End-to-end deep learning system for multi-label retinal disease classification, retinal vessel segmentation, and Grad-CAM explainability.

0.81210

ODIR validation ROC-AUC

0.55224

ODIR validation Macro F1

  • Python
  • PyTorch
  • ResNet50
  • U-Net
  • EfficientNet-B0
  • Grad-CAM
  • +2 more

Experience

Research and engineering roles

  1. Sep 2026 – PresentCurrent

    Data Annotation Analyst

    Rooya AI (opens in a new tab) · Grand Tower, ECB Chattar, Dhaka 1206, Bangladesh

    Responsibilities2
    • Annotate and validate industrial data to support AI/ML model development and training.
    • Apply annotation guidelines while maintaining data quality, consistency, and labeling accuracy.
    • Data Annotation
    • AI/ML Data
    • Computer Vision
    • Data Quality
  2. Jan 2026 – PresentCurrent
    Responsibilities2
    • Develop machine learning and deep learning models for healthcare, biomedical AI, and scientific applications, from data preparation to model evaluation.
    • Design experimental pipelines for data analysis, optimization, explainability, and research publication while mentoring interns and supporting collaborative research activities.
    • Machine Learning
    • Deep Learning
    • Explainable AI
    • Data Analysis
  3. Jan 2025 – Dec 2025
    Responsibilities2
    • Assisted undergraduate students through laboratory sessions, coursework support, grading, debugging, and academic mentoring.
    • Supported Introduction to Computer Science lab courses for BBA students.
    • Computer Science and Engineering

Education

Degrees

  1. Jul 2018 – Mar 2020

    Higher Secondary Certificate (H.S.C.)

    GPA4.00 / 5.00

    Begum Sheikh Fazilatun Nessa Mujib Govt. College

    Dhaka · Science

Gatherings

Conferences, workshops and research events

ieeepsbdcgala

Image 1 of 2

4 July 2026MeetingCommittee Member

IEEE Photonics Society Bangladesh Chapter Executive Committee Meeting

IEEE Photonics Society Bangladesh Chapter · Sky Lounge, Mirpur-1,Dhaka,Bangladesh

A productive evening with brilliant minds at the IEEE Photonics Society Bangladesh Chapter Executive Committee Meeting. The meeting brought together executive committee members to discuss upcoming…

Event page (opens in a new tab)

Publications

Peer-reviewed work

PublishedConference paper2026

X-GNN: An Explainable Ensemble Graph–Transformer Framework for Human Protein–Protein Interaction Prediction

Author list not yet recorded.

“X-GNN: An Explainable Ensemble Graph–Transformer Framework for Human Protein–Protein Interaction Prediction”, 2026 5th International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE), 2026.

Rajshahi, Bangladesh

BibTeX
@inproceedings{x-gnn-explainable-graph-transformer-protein-interaction,
  title     = {X-GNN: An Explainable Ensemble Graph–Transformer Framework for Human Protein–Protein Interaction Prediction},
  booktitle = {2026 5th International Conference on Electrical, Computer \& Telecommunication Engineering (ICECTE)},
  publisher = {IEEE},
  year      = {2026},
  doi       = {10.1109/ICECTE69292.2026.11429277},
}

Generated from the fields above. Check against the publisher record before citing.

Leadership & engagement

Roles and service

  1. Jan 2024 – Dec 2025

    Mentor

    IEEE

    IEEE Computer Society Bangladesh Chapter

    CS BDC Student Activities Committee

    The group worked on establishing new Computer Society student branch chapters; IEEE CS CUET SBC was successfully opened.

Recognition

Awards and honours

Oct 2025ScholarshipUS $1,000

IEEE Computer Society Richard E. Merwin Student Scholarship 2025, Spring Cycle

Recipient

IEEE Computer Society (opens in a new tab)

Recipient of the IEEE Computer Society Richard E. Merwin Student Scholarship 2025, Spring Cycle. The scholarship recognizes promising students in computing and related fields based on academic achievement, leadership, professional involvement, and potential contribution to the computing community.

One of the top 20 recipients worldwide in the Spring Cycle

Selection

Announcement (opens in a new tab)

Get in touch

Open to research collaborations, PhD and MSc supervision enquiries, and applied AI work in healthcare.

Contact