Shiraz University
I am a research-oriented Master's student in Artificial Intelligence and Robotics at Shiraz University, driven by a strong passion for scientific inquiry, reproducible research, and continuous learning.
I ranked 1st among my classmates in Computer Engineering, received the official top-rank certificate, and graduated with a GPA of 18.4/20 (≈ 3.7/4.0).
My work focuses on developing interpretable and practical solutions at the intersection of Computer Vision, Medical AI, Remote Sensing / GeoAI, and Explainable Machine Learning. I am particularly interested in implementing research papers whose official code is not publicly available and in building end-to-end, reproducible pipelines that bridge classical methods with modern deep learning techniques.
During my Bachelor's studies, I fully taught the Operating Systems Laboratory course and prepared the complete course materials. In my Master's program, I serve as a Teaching Assistant for the Artificial Intelligence course, where I design homework assignments and educational content.
I am currently working on two interdisciplinary research papers and preparing my Master's thesis in the field of Remote Sensing.
- Two research papers in Medical Imaging and Construction Risk Management (in preparation)
- Master's thesis in Remote Sensing / GeoAI
- Computer Vision & Medical Image Analysis
- Remote Sensing & Geospatial Artificial Intelligence (GeoAI)
- Explainable AI & Interpretable Models
- Graph Neural Networks & Kernel Methods
- Fuzzy Systems & Soft Computing
- Multi-Agent Reinforcement Learning
- Bioinformatics & Computational Biology
- Large Language Models
| Project | Description | Domain |
|---|---|---|
| Attention U-Net for Sentinel-2 Land Cover Segmentation | Custom Attention U-Net with spectral indices for agricultural land cover semantic segmentation (mIoU 0.926) | Remote Sensing / Deep Learning |
| Sentinel-2 Unsupervised Land Cover Labeling | Explainable unsupervised pseudo-labeling pipeline with Google Earth Engine, spectral features, and ensemble clustering | Remote Sensing / Unsupervised Learning |
| GeoAI Crop Mapping Platform with Satellite Foundation Models | End-to-end GeoAI platform using AlphaEarth & Galileo embeddings for crop classification and vegetation analysis | GeoAI / Foundation Models |
| Sliced Wasserstein Graph Kernel for Brain Network Diagnosis | Independent reproduction of MICCAI 2023 paper on graph kernels for brain disease classification | Medical AI / Graph Learning |
| Fuzzy Time Series from Scratch | Complete from-scratch implementation of First-Order and High-Order Fuzzy Time Series with hierarchical back-off | Soft Computing / Forecasting |
| Kernel Methods and Regularized Learning | From-scratch Kernel SVM (CVXOPT), Kernel K-Means, and regularized regression methods | Classical Machine Learning |
| Retinal Vessel Segmentation using Mathematical Morphology | Fully interpretable vessel segmentation pipeline implemented from scratch using multi-scale morphological operations | Medical Image Processing |
| Air Combat Multi-Agent Reinforcement Learning | Multi-agent RL framework for autonomous decision-making in adversarial air combat scenarios with curriculum learning | Reinforcement Learning |
Languages & Frameworks
Python PyTorch TensorFlow / Keras Scikit-learn OpenCV NumPy Pandas
Specialized Domains
Google Earth Engine Remote Sensing GIS Graph Neural Networks Fuzzy Logic Kernel Methods Mathematical Morphology Reinforcement Learning
Others
Jupyter Git LaTeX QGIS
- GitHub: hannah-fathi
- LinkedIn: hannah-fathi
- University Email: hana.f@hafez.shirazu.ac.ir
- Personal Email: hannahfathi99@gmail.com
I am passionate about conducting rigorous, transparent, and reproducible research.
I enjoy implementing scientific papers from scratch and developing practical AI systems that can be reliably used in real-world settings.
Feel free to explore my repositories — most of them include detailed documentation, experimental results, and complete pipelines.
⭐️ From hannah-fathi