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

Recent Publications

Optimizing fairness tradeoffs in machine learning with multiobjective meta-models
La Cava, W. G. (2023)
Genetic and Evolutionary Computation Conference (GECCO)
Fair admission risk prediction with proportional multicalibration
La Cava, W., Lett, E., and Wan, G. (2023)
Conference on Health, Inference, and Learning
Proceedings of Machine Learning Research
A flexible symbolic regression method for constructing interpretable clinical prediction models
La Cava, W., Lee, P.C., Ajmal, I., Ding, X., Cohen, J.B., Solanki, P., Moore, J.H., and Herman, D.S (2023)
npj Digital Medicine