I'm a recent graduate of St. John's University with a degree in Cybersecurity Systems!
I love working on problems through data analysis and predictive modeling. I’m big on continuously learning and building my skills, and believe taking initiative is key to growth. I recently wrapped up an internship at Rivian as a Cybersecurity Analyst, where I worked on third-party risk assessments and supplier monitoring. I am now an intern for the FBI! Outside of work, I’m actively building machine learning models and deepening my understanding of cybersecurity tools and threat intelligence platforms.
I’m currently looking for a full-time opportunity where I can contribute to innovative projects and continue learning from industry professionals — got any leads? Let’s connect!
What I Did:
Collaborated on a machine learning research project focused on classifying icebergs vs. vessels in Synthetic Aperture Radar (SAR) imagery. Contributed to feature extraction and embedding workflows, compared baseline ML models with pretrained foundation-model embeddings (ViT and ConvNeXt), and supported evaluation and benchmarking to identify the most effective approach for maritime object detection.
Tools: Python, scikit-learn, PyTorch, TensorFlow/Keras, HuggingFace Transformers, NumPy, Pandas, Matplotlib, Jupyter
Result: Achieved 96% classification accuracy and 98.5% ROC-AUC using a baseline MLP model. Demonstrated that while pretrained embeddings improved feature representation, a strong baseline model best met business and performance requirements for this task.
Curious? Check out the full project here
What I Did:
Built a machine learning-powered system that replaced traditional regex-based detection methods, significantly improving both accuracy and efficiency. I engineered a comprehensive dataset, performed extensive preprocessing, and trained multi-label classification models—achieving a 94% accuracy rate using a Random Forest classifier.
Tools: Python, scikit-learn, Pandas, NumPy, TF-IDF, Random Forest
Result: Replaced manual rule-based detection with an automated, intelligent classification system ready for production use.
Curious? Check out the full project here
What I Did
Developed a medical imaging AI system using EfficientNet to analyze chest X-ray images for pneumonia detection with 94%+ accuracy. I trained the model on open datasets, optimized it for Apple Silicon (MPS), and built a CustomTkinter GUI for user-friendly, real-time diagnosis.
Tools: Python, PyTorch, PIL, CustomTkinter
Result: Delivered sub-second inference speed and a pink-themed diagnostic interface designed for research, education, and preliminary screening.
Curious? Check out the full project here
- Languages: Python, Java
- Data Science & ML: Pandas, NumPy, scikit-learn, TensorFlow
- Web Development: HTML, CSS, JavaScript (basic), Node.js
- Tools: Jupyter Notebooks, Google Colab, Git, GitHub
- Databases: SQL, MySQL
🔗 [View Project README]
What I Did:
Designed and developed a sleek, responsive site for the ACM chapter to share updates, events, and technical blog posts. I integrated a Contentful CMS backend so non-technical members could easily manage blog content without editing code.
Tech Stack: Next.js 15, TypeScript, Tailwind CSS, Framer Motion, React 18+, Contentful CMS, Vercel
Learnings:
Learned how to connect a headless CMS to a modern React stack and improved my frontend development and deployment skills.
🔗 [View Project README]
What I Did:
Built a command-based bot that quizzes users on CompTIA Security+, Network+, and A+ exam topics, providing explanations and tracking progress.
Tech Stack: Python, Discord API
Learnings:
Strengthened my understanding of CompTIA topics, API integrations, and user interaction flows within Discord environments.
🔗 [View Project README]
What I Did:
Collaborated with a team of four to build an intelligent Discord bot that helps students prepare for interviews through real-time, personalized guidance. The bot uses open-source LLMs and web scraping to extract key information from resumes and webpages, then delivers customized prep plans and advice.
Tech Stack: Python, HuggingFace Embeddings, Retrieval-Augmented Generation (RAG), OpenAI GPT-4, Discord API
Learnings:
Gained hands-on experience working with large language models, embedding generation, and real-time bot interactions. Also improved my collaboration skills in a fast-paced, team-based project setting.
- Email: hinnazeejah.dev@gmail.com
- LinkedIn: https://www.linkedin.com/in/hinna-zeejah/
- Website/Portfolio: https://www.hinnazeejah.com/
I’ve visited over 15 countries and love finding inspiration in different cultures, conversations, and experiences 🌍