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William LaCava, Ph.D.

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Biography
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University of Pennsylvania, Philadelphia, PA, USAPostdoc07/2020Biomedical Informatics
University of Massachusetts Amherst, Amherst, MAPhD09/2016Mechanical Engineering
Cornell University, Ithaca, NY, USAM.Eng.05/2010Mechanical and Aerospace Engineering
Cornell University, Ithaca, NY, USAB.S.05/2009Mechanical and Aerospace Engineering

Overview
overview
Williams runs the Clarity- and Virtue-guided Algorithms Laboratory (Cava Lab) in the Computational Health Informatics Program at Boston Children's Hospital and Harvard Medical School. His research focuses on developing multi-objective learning methods and using them to explain the principles underlying biomedical processes. His lab uses these methods to learn predictive models from electronic health records (EHRs) that are both interpretable to clinicians and fair to the population on which they are deployed. His long-term goals are to positively impact human health by developing methods that are flexible enough to automate entire computational workflows underlying scientific discovery and medicine.
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Mentoring
current student opportunities
Available: 12/07/22, Expires: 08/31/25

Our lab develops machine learning methods that optimize clinical risk prediction models to be fair to patients they are deployed on. In this project, the student will gain experience in this development process and assist in applying these methods to clinical applications. Application areas include: - emergency room admission risk prediction - fair resource allocation for hypertension management An ideal candidate will have some background or experience programming in Python, a solid grasp of statistics and familiarity machine learning. Tasks include data analysis and visualization, contributing to open source software, and running computational experiments on large sets of health records. Students will develop technical skills and a deeper understanding of clinical applications of AI.


Research
research activities and funding
The research activities and funding listed below are automatically derived from NIH ExPORTER and other sources, which might result in incorrect or missing items. Faculty can login to make corrections and additions.
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  1. R00LM012926 (LA CAVA, WILLIAM) Sep 1, 2021 - Aug 31, 2024
    NIH
    Multi-objective representation learning methods for interpetable predictions of patient outcomesusing electronic health records
    Role: Co-Investigator
  2. K99LM012926 (LA CAVA, WILLIAM) Jun 1, 2019 - May 31, 2021
    NIH
    Multi-objective representation learning methods for interpetable predictions of patient outcomesusing electronic health records
    Role: Co-Investigator

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© 2025 President and Fellows of Harvard College
Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.