About our recent HUMIES award-winning algorithm for clinical prediction models
The Cava laboratory is a research group in the Computational Health Informatics Program at Boston Children’s Hospital and Harvard Medical School.
We are interested in improving the interpretability and fairness of predictive models deployed in healthcare settings. We create and study algorithms that can embed these notions when working with health data.
Join the Team! If you’re looking to do research in the areas of machine learning, interpretability, fairness, and health informatics, see the opportunities here.
Recent Posts
A new perspective on how this social theory relates to fair machine learning.
We consistently observe lexicase selection running times that are much lower than its worst-case bound of \(O(NC)\). Why?
Recent Publications
Optimizing fairness tradeoffs in machine learning with multiobjective meta-models
Genetic and Evolutionary Computation Conference (GECCO)
Fair admission risk prediction with proportional multicalibration
Conference on Health, Inference, and Learning
Proceedings of Machine Learning Research
A flexible symbolic regression method for constructing interpretable clinical prediction models
npj Digital Medicine