Information

Related Research Units

Computational Health Informatics Program Research

Research 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.

Research Background

William received his PhD from UMass Amherst with a focus on interpretable modeling of dynamical systems. Prior to joining CHIP, he was a post-doctoral fellow and research associate in the Institute for Biomedical Informatics at the University of Pennsylvania.

Publications

  1. Intrapartum electronic fetal heart rate monitoring to predict acidemia at birth with the use of deep learning. Am J Obstet Gynecol. 2025 Jan; 232(1):116.e1-116.e9. View Abstract
  2. Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling. Circulation. 2024 03 19; 149(12):917-931. View Abstract
  3. Effects of Race and Gender Classifications on Atherosclerotic Cardiovascular Disease Risk Estimates for Clinical Decision-Making in a Cohort of Black Transgender Women. Health Equity. 2023; 7(1):803-808. View Abstract
  4. A flexible symbolic regression method for constructing interpretable clinical prediction models. NPJ Digit Med. 2023 Jun 05; 6(1):107. View Abstract
  5. Translating Intersectionality to Fair Machine Learning in Health Sciences. Nat Mach Intell. 2023 May; 5(5):476-479. View Abstract
  6. Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record? J Biomed Inform. 2023 03; 139:104306. View Abstract
  7. Fair admission risk prediction with proportional multicalibration. Proc Mach Learn Res. 2023; 209:350-378. View Abstract
  8. PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods. Bioinformatics. 2022 01 12; 38(3):878-880. View Abstract
  9. Contemporary Symbolic Regression Methods and their Relative Performance. Adv Neural Inf Process Syst. 2021 Dec; 2021(DB1):1-16. View Abstract
  10. Evaluating recommender systems for AI-driven biomedical informatics. Bioinformatics. 2021 04 19; 37(2):250-256. View Abstract
  11. Learning feature spaces for regression with genetic programming. Genet Program Evolvable Mach. 2020 Sep; 21(3):433-467. View Abstract
  12. Interpretation of machine learning predictions for patient outcomes in electronic health records. AMIA Annu Symp Proc. 2019; 2019:572-581. View Abstract
  13. Semantic variation operators for multidimensional genetic programming. Genet Evol Comput Conf. 2019 Jul; 2019:1056-1064. View Abstract
  14. Relief-based feature selection: Introduction and review. J Biomed Inform. 2018 09; 85:189-203. View Abstract
  15. A Probabilistic and Multi-Objective Analysis of Lexicase Selection and e-Lexicase Selection. Evol Comput. 2019; 27(3):377-402. View Abstract
  16. Data-driven advice for applying machine learning to bioinformatics problems. Pac Symp Biocomput. 2018; 23:192-203. View Abstract
  17. PMLB: a large benchmark suite for machine learning evaluation and comparison. BioData Min. 2017; 10:36. View Abstract
  18. Monitoring pacemaker patients. J Clin Eng. 1994 Jan-Feb; 19(1):39-47. View Abstract

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