Congratulations 2025 Pilot Award Recipients!
The University of Washington Institute of Medical Data Science is excited to announce the winners of the 2025-26 Pilot Awards. In its second year, this initiative aims to transform medicine and enhance public health through innovative data-driven approaches. Awardees were chosen from a wide range of applicants in fields such as informatics, computing, engineering, and medicine. The goal is for funded awards to spark new collaborations between computational science experts and providers.
Pilot funding for the period of July 1, 2025 to June 30, 2026, enables investigators to obtain preliminary data in order to establish a proof of concept and go on to seek larger grants. Investigators supported by the Pilot Program are working toward improving cancer predictions through AI, measuring ankle recovery progress, using machine learning to improve communication between health providers and patients, and improving the health of people with brain injuries.
Continue reading to learn more about their projects and join us at the upcoming IMDS Taskforce meeting, where awardees will further detail their approach for discussion.
Please join us in congratulating our awardees!
“Multimodal Data and AI for Uncertainty-Aware Prediction of Lung Cancer Treatment Response“
Stephen Bowen
Radiation Oncology
John Gennari, PHd
Biomedical Informatics and Medical Education
Faisal Yaseen
Biomedical Informatics and Medical Education (PhD student)
Abstract To Be Posted Soon
“AutoRELATE: Teaching Machines to Recognize Meaningful Connection in Clinical Encounters”
Andrea Hartzler
Biomedical Informatics and Medical Education
Barbara Lam
School of Medicine, Hematology, Oncology Division
Aishwarya Raj
Biomedical Informatics and Medical Education (PhD student)
Abstract To Be Posted Soon
“Quantifying Recovery After Ankle Injury to Advance Precision Medicine“
Katherine Steele
Mechanical Engineering
Cindy Lin
Rehabilitation Medicine
Ally Clarke
Mechanical Engineering (PhD student
Abstract To Be Posted Soon
“Machine Learning-based Analysis of Auditory Signal Processing to Predict Extubation Failure in Patients with Severe Acute Brain Injury“
James Town
Department of Medicine, Pulmonary, Critical Care and Sleep Medicine
Shwetak Patel
Computer Science and Engineering
Alex Ching
Computer Science and Engineering (PhD Student)
Abstract To Be Posted Soon
Thank You to Our Sponsors
A big thanks to the University of Washington School of Medicine, College of Engineering and School of Public Health for their partnership on this initiative. Additionally, we extend our gratitude to the UW Provost’s Office for their funding support and the eScience Institute for sponsoring cloud computing resources for our pilot teams.
IMDS is supported by the Schools of Medicine and Public Health, the College of Engineering and the Allen School for Computer Science and Engineering