Master's in Applied AI @ University of Ottawa 🎓 | Machine Learning • LLMs • Software Engineering | Passionate about building intelligent systems that adapt, learn, and make an impact.
Contact Me
I’m Mounira Zitouni, an ML engineer and AI researcher turning ideas into
scalable, production-ready systems. My work spans LLMs,
retrieval-augmented generation (RAG), and online learning that adapts under drift & imbalance.
I’ve co-authored peer-reviewed AI research, built data pipelines processing millions,
and deployed solutions with PyTorch, TensorFlow, River, Azure, and GCP.
My focus: ML that’s accurate, explainable, and truly deployable.
Fundamentals + hands-on training & evaluation
Designing prompts, tools, and evaluation for language systems
Streaming adaptation under feature/concept shifts
Threat modeling & defenses for ML/LLM systems
Building systems that are fair, transparent, and accountable
End-to-end modeling in Python
Reliable shipping of models & services
Model endpoints, data services, and storage
UI & test automation
Playbook for responsible LLMs — bias probes, safety prompting, PII redaction, evaluation harnesses; Python notebooks & templates. Technologies: Python, Jupyter, spaCy, scikit-learn, SHAP/LIME, prompt guardrails.
View
RoBERTa, XLNet & VADER over r/Ottawa; custom score-weighted sentiment, 87% weighted accuracy; bias analysis and strategy insights.
View
RAG over Neo4j graph (User/Message/Channel); prompt templates for Mistral LLM to answer NL queries from retrieved subgraphs.
View
Conversational assistant experiments with persona scaffolding and retrieval; prompt library for coaching/Q&A.
View
Android app in Java with local SQLite + MySQL; simple UX for logging insulin doses and tracking history offline.
ViewMerit scholarships & Dean’s Honour List; foundation in systems, algorithms, and software design.
SEG 2105, SEG 2900, SEG 3102, SEG 3503/3103 — labs, grading, mentoring 350+ students.
LLM-based generation of Symboleo specifications; ~70% authoring speedup; conference talk + slides.
BASc Software Engineering completed with distinction.
Research on online learning under drift & class imbalance under graduate Director; PyTorch / TensorFlow / River.
Delivered labs, grading, and mentoring in both English and French across 4 courses.
“Fantastic work Mounira! This was one of the fastest undergraduate projects I have seen too! You finished early enough to write and submit a good paper to a workshop of the top Software Engineering conference (ICSE) and get it accepted! Your hard work paid off. Congratulations again.”
“Students are telling me excellent things about your tutorials and labs. Congratulations on that. I very rarely get such positive feedback from TAs.”
