My name is Salma Hassan!
🎓 Second-Year Master’s Student in Machine Learning at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) 📚, holding a Bachelor’s in Computer Engineering 🖥️ with a Minor in Data Science 📊. Specializing in AI and ML development 🤖, I have hands-on experience across data analytics, medical imaging 🧠, bioinformatics 🧬, and survival analysis 📈. Driven to advance intelligent systems, I blend a strong engineering foundation with deep expertise in ML, aiming to make impactful contributions through data-driven solutions. 🌟
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Machine Learning & AI Development
- 📊 Data Analysis & Model Development: Leveraging data-driven insights to design ML models across various domains.
- 🤖 Deep Learning: Experienced with neural networks for classification, segmentation, and generative models, including CNNs, LSTMs, and GNNs.
- 🎓 Predictive Modeling: Building robust models for predictive analytics in genomics, medical imaging, and clinical applications.
- 🌐 Transformers & NLP: Developing language models and transformer-based models for tasks like text classification, named entity recognition, and sequence modeling.
- 🕵️♀️ Explainable AI (XAI) & Interpretability: Using techniques like SHAP, LIME, GNNExplainer, and attention mechanisms to make models more transparent, particularly for sensitive applications.
- 🔒 Federated Learning & Privacy-Preserving AI: Implementing models that allow decentralized learning while preserving data privacy, particularly relevant in healthcare.
- 🌱 Few-Shot and Zero-Shot Learning: Applying minimal labeled data to train models, ideal for domains with limited annotations like rare diseases.
- 🔄 Self-Supervised Learning: Leveraging unlabeled data for pre-training, then fine-tuning on smaller labeled datasets, useful in NLP and vision tasks.
- ⏳ Time-Series Analysis & Forecasting: Utilizing temporal models like RNNs, Transformers, and temporal GNNs to handle sequential data, essential for patient monitoring and finance.
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Domain-Specific AI Applications
- 🧬 Genomics & Bioinformatics: Working on the Genetic Foundation Model (GFM) project, analyzing DNA sequences for mutation detection, gene expression, and variant impact predictions.
- 🧠 Medical Imaging: Advanced expertise in MRI/CT segmentation, brain structure analysis, and radiomics, especially for neuroimaging applications in Alzheimer’s and Parkinson’s disease.
- 📉 Survival Analysis: Using foundational and survival models for patient risk stratification and prognosis, with applications in cancer recurrence prediction and other clinical outcomes.
- 🗣️ NLP Applications: Implementing transformer-based NLP models for text analysis, document classification, and biomedical language processing tasks.
- 🧩 Multimodal Learning: Integrating multiple data types (e.g., images, text, genomics) for comprehensive models, especially valuable in medical diagnostics.
- 🔍 3D Computer Vision: Applying AI for 3D data analysis from CT, MRI, and LiDAR, with applications in medical imaging and autonomous systems.
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Languages:
- Python 🐍 • Java ☕ • C++ • C • R Language • JavaScript • HTML & CSS
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Libraries & Frameworks:
- Deep Learning: PyTorch, TensorFlow, Keras
- Data Science: NumPy, pandas, scikit-learn
- Visualization: Matplotlib, Seaborn, Plotly
- Other: Firebase, MongoDB, MySQL, SQLite, Verilog
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Software Development: Proficient in software design and implementation, following Agile methodologies.
- 🛠️ Full-Stack Development: Building dynamic, responsive applications using ASP.NET and React.
- 💾 Database Management: Hands-on experience with SQL databases and data warehousing.
- 🎨 UI/UX: Translating design wireframes into functional interfaces.
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Automation & Optimization:
- 🔍 Automated data pipelines using Python to streamline data analysis processes.
- 📈 Developed algorithms for real-time data processing and visualization in Power BI and SAP IBP.
- GNN for Disease Classification: Implemented a multimodal model using GNN and convolutional methods to predict and classify disease progression in complex datasets, demonstrating improved classification accuracy for neurodegenerative diseases.
- AI for Genomics: Created ML algorithms for analyzing gene expression, including deep learning models trained on large-scale genetic data.
- Medical Imaging & Diagnostics: Developed multimodal ML models for enhanced Alzheimer's and Parkinson's diagnostics.
- Data Analysis & Business Intelligence: Generated actionable insights through Power BI dashboards, enhancing data-driven decision-making.
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Tools:
- 🛠️ Development: Jupyter, PyCharm, Git, Docker
- 🧬 Bioinformatics: Enformer, ClinVar, LongRoPE
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Certifications:
- AWS Cloud Practitioner 🌩️
- Academic Excellence Award 🎖️
- Deep Learning Specialization (Coursera) 📚
- Data Science Professional Certificate (edX) 📈
- Advanced SQL for Data Scientists (DataCamp) 💻
Feel free to reach out if you'd like to discuss AI, ML, and software engineering or collaborate on projects!