research community.

Each fellow receives financial and organizational support for a Postdoctoral Networking Tour in AI (Post-NeT-AI) to meet with leading research groups all over Germany and the opportunity for exclusive short-term funding to conduct research at a German institution for a duration between one and three months. Additionally, awardees are granted membership in the global DAAD AInet Fellows & Alumni Network, offering long-term access to German AI research and bringing together bright researchers from all around the world.

Building on two decades of domestic and international experience in clinical research and public health informatics, Reyes Nieva’s research examines and applies methods such as machine learning, natural language processing, and spatiotemporal analysis to enable equitable learning health systems, support precision health, and contribute to knowledge discovery in medicine. His primary interests center around using and interrogating multimodal data sources and the vast toolbox that computational learning offers to better understand, improve, and facilitate the study of health in populations and communities that are marginalized.

Dr. Campbell’s research focuses on complementary uses of data science methods and non-traditional data sources to conventional epidemiological approaches in local and global public health research. Building on her doctoral research in temporal condition pattern mining and machine learning model development with large EHR datasets, Campbell has continued her work as a Postdoctoral Fellow at DBMI using statistical and computational methods to study fairness and bias in virtualized clinical trials and Type 2 Diabetes self-management behaviors amongst medically underserved patients in an mHealth intervention.