معرفی
William La Cava leads the Clarity- and Virtue-guided Algorithms Laboratory (Cava Lab) within the Computational Health Informatics Program at Boston Children's Hospital and Harvard Medical School. His research focuses on developing interpretable and fair machine learning methods for biomedical applications. He holds a PhD from the University of Massachusetts Amherst, with a focus on interpretable modeling of dynamical systems, and previously worked as a post-doctoral fellow and research associate at the University of Pennsylvania’s Institute for Biomedical Informatics.
His work emphasizes multi-objective learning, symbolic regression, and fairness in AI systems deployed in healthcare. Key research interests include leveraging electronic health records (EHRs) to build predictive models that balance clinical interpretability with population-level fairness, as well as automating computational workflows for scientific discovery and medicine.
Publications span topics like deep learning for fetal heart monitoring, algorithmic fairness in cardiovascular risk assessment, and benchmarking machine learning methods via the PMLB dataset collection. His lab’s methods aim to enhance transparency and equity in healthcare AI while advancing translational research.
La Cava has contributed to foundational work in symbolic regression and genetic programming, with applications in clinical decision-making and EHR data analysis. His interdisciplinary approach bridges machine learning, biomedical informatics, and ethical considerations in health equity.
