Competing Risks in Healthcare and Predictive Maintenance
Competing risks occur when more than one event can happen, and one event changes or prevents the chance of observing another.
Recent writing
Paired vs. Independent Samples: The Design Choice Behind the Test
The choice between paired and independent tests is not a software option. It is a statement about the study design and the dependence structure in the data.
Slice-Based Model Evaluation: Finding the Failures Average Metrics Hide
Slice-based evaluation exposes where a machine learning model fails by breaking aggregate performance into meaningful subgroups, conditions, and operational contexts.
Data Drift and Fairness: Monitoring Equity When Populations Change
A fair model at launch can become unfair in production when populations, behavior, policies, or measurement systems change.
Using Unsupervised Learning for Early Data Drift Detection
When labels arrive late, production teams need early signals that the input environment has changed. Unsupervised drift detection can provide those signals, but only if it is designed around operations rather than dashboards.
Active Learning for Machine Learning: Getting More Value from Fewer Labels
Active learning improves machine learning by choosing which examples to label, not merely by asking for more labeled data.
Selective Prediction in Machine Learning: When Models Should Abstain
Selective prediction gives machine learning systems a third option: predict when confidence is adequate and abstain when the cost of being wrong is too high.
Representation Learning for Tabular Data: Beyond Manual Feature Engineering
Representation learning for tabular data is not about replacing feature engineering blindly. It is about learning useful structure while respecting the constraints of business data.
Temporal Validation in Machine Learning: Testing Models Against the Future
Temporal validation evaluates machine learning models the way they will be used: trained on the past and tested on the future.
The Dangerous Push Toward Practical Mathematics: Why Pure Research Must Remain Protected
The pressure to justify mathematics by immediate usefulness misunderstands how mathematical progress works. Pure research needs protection precisely because its value is often invisible at the moment it is created.