Funding

Role: PI
Duration: 10/01/2024 – 09/30/2029
Title: Data-driven Subtypes of Alzheimer’s disease progression for targeted treatment
Project Description:
Therapy development for Alzheimer’s disease (AD) has been impeded by significant disease heterogeneity. The objective of this project is to reveal and stratify heterogeneous AD populations into clinically targetable groups using completed clinical trial/registries data. If successful, this project will speed up clinical trials by informing the design of future intervention trials.

Role: PI
Duration: 09/01/2023 - 05/31/2028
Title: Harmonizing multiple clinical trials for Alzheimer’s disease to investigate differential responses to treatment via federated counterfactual learning
Project Description:
This project will develop machine learning models to identify patient subgroups who respond differently to treatments. This will result in smaller, less expensive studies, and more targeted Alzheimer’s disease clinical trials that expose fewer patients to experimental medications to which they are unlikely to respond.

Role: PI
Duration: 09/15/2019 - 09/14/2021
Title: Computational Phenotyping to Better Understand Obesity
Project Description:
This project will develop a dynamic pattern mining model to identify trends/patterns of weight gain (temporal phenotyping) that is most likely to be diabetic.

Role: MPI
Duration: 09/01/2022 – 08/31/2023
Title: Ethically optimize machine learning models with real-world data to improve algorithmic fairness
Project Description:
The project aims at developing novel machine learning solutions to mitigate algorithmic unfairness by addressing data biases.