Working with
What I use each of these for, and where it shows up in my projects and roles.
Python / Jupyter
My main language for data, machine learning and automation, and the one I teach. Jupyter notebooks are where I explore data and show results.
Where I use it
- World Cup PredictorMatch predictions with XGBoost and Monte Carlo tournament simulations, as Python modules plus Jupyter notebooks.
- OutamationPython workflows for document classification and data extraction (NLP, OCR, PyMuPDF).
- De Anza CollegeTeaching and tutoring 25–30+ students in Python, debugging and core programming concepts.
- Statistics Behind MLA small bias–variance simulation in Python with scikit-learn, used as a figure in the paper.
NumPy · pandas
For loading, cleaning and reshaping tabular data, and for the numerical work behind simulations.
Where I use it
- World Cup Predictorpandas and NumPy load and prepare the historical match data; NumPy drives the Monte Carlo tournament simulations.
CI/CD · DevOps
Automating how code and infrastructure are tested, packaged and deployed, and keeping an eye on how they run.
Where I use it
- MicrosoftAutomated CI/CD load-testing tools and observability dashboards to monitor reliability and performance.
- Google CloudInfrastructure as code with Terraform, and microservices containerized with Docker for deployment to GKE.
Cloud infrastructure
Provisioning and running services on Google Cloud and Azure.
Where I use it
- Google CloudAutomated Google Cloud infrastructure with Terraform; containerized microservices for GKE.
- MicrosoftREST API endpoints in C#/.NET for an internal Azure resource-management service.
Agentic AI
AI systems where language models use tools and retrieved information to carry out tasks.
Related work so far
- OutamationA LlamaIndex retrieval-augmented generation (RAG) system for searching mortgage documents, and evaluation of open-source AI models.
- xAIEvaluating Grok’s responses in Kyrgyz for accuracy, fluency and cultural understanding.
AI/ML Research
Reading, testing and writing about how machine-learning systems behave, and where they go wrong.
Where I use it
- Statistics Behind MLA review paper on bias, uncertainty and fairness in machine learning.
- Auditing the ObjectiveA co-authored technical report on making recommender-system objectives explicit, measurable and auditable.
- ZenodoAI Researcher, 2026.