Blog Posts
Essays and reflections on data science, causal reasoning, and the questions worth asking about work and purpose.
- How to Build Explainable Machine Learning models?
Using the SHAP library to make customer-facing ML predictions interpretable and actionable, and how to evaluate whether explanations actually calibrate user trust.
- Causal Inference in Crop Trials
Some early thoughts on how causal reasoning, going beyond correlation, can help interpret crop trial interventions and predict downstream effects on yield and resilience.
- Developing a Work View
Notes from Stanford's Designing Your Life course on building a personal 'work view', the hard questions worth asking about specialization, money, ethics, and purpose.
- Courage, Purpose and Growth
A personal reflection on risk-aversion, incremental growth, and finding purpose that aligns with lasting values rather than external validation.