I’m currently pursuing a Master’s in AI Engineering at the University of San Diego, expected to graduate in April 2026. With a strong foundation in SEO, data analysis, and project management, I’m focused on building applied machine learning solutions—especially in NLP, forecasting, and data-driven decision-making.
| Project | What it does | Tech | Links |
|---|---|---|---|
| Sentiment‑Enhanced Forecasting | Benchmark-driven financial sentiment index + stock return forecasting (FNSPID + FinBERT). | Jupyter Notebook, NLP | Repo |
| AI Keyword Clustering | AI-powered keyword clustering using BERT embeddings to group keywords by semantic similarity. | Python, BERT | Repo |
| Drug Review Sentiment Analysis | NLP model to classify patient drug reviews and extract actionable sentiment signals. | Jupyter Notebook, NLP | Repo |
| Diabetes Stats Model | Health indicators analysis + hypothesis testing and modeling to predict diabetes likelihood. | Jupyter Notebook, ML | Repo |
| Composer Classifier | Classifies music composers from features/data (ML classification project). | Python, ML | Repo |
- Sentiment‑Enhanced Forecasting — Method: combined FinBERT-based sentiment signals with a forecasting workflow; Result: produced an interpretable sentiment index used to support stock return forecasting.
- AI Keyword Clustering — Method: generated BERT embeddings and clustered keywords by semantic similarity; Result: generated clustered keyword groups that can be used for SEO content planning and topic mapping.
- Drug Review Sentiment Analysis — Method: trained an NLP classifier on patient drug-review text; Result: enabled positive/negative sentiment classification to summarize patient experience at scale.
- Diabetes Stats Model — Method: performed EDA + hypothesis testing and trained baseline ML models; Result: identified likely predictors and validated key hypotheses to support diabetes risk modeling.
- Composer Classifier — Method: trained a supervised multi-class classification model on composer-related features; Result: trained a classifier that predicts composer labels from input features.
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Master of Science in AI Engineering
University of San Diego (Expected April 2026) -
Bachelor of Computer Science
Applied Science Private University, Graduated June 2003
Programming Languages
AI/ML Frameworks
Other Tools
- Healthcare Data Science: Using AI to improve patient outcomes and insights.
- Natural Language Processing (NLP): Specialized in sentiment analysis and text classification.
- Machine Learning & AI: Developing predictive models for data-driven decision-making.
- Technical SEO & Analytics: Leveraging data to optimize user experience and organic reach.
I am passionate about exploring intersections between data science, AI, and healthcare. My goal is to create impactful AI solutions that enhance decision-making and drive innovations in healthcare.
- Email: al-hamdan@hotmail.com
- LinkedIn: Iman Hamdan