Artificial Intelligence and Machine Learning

Hands holding glucose sensor transmitter, a smart insulin pen and phone displaying DailyDose app.
Peter G. Jacobs, Ph.D., displays the DailyDose app and the diabetes tools with which it works: a glucose sensor transmitter and a smart insulin pen. (OHSU/Christine Torres Hicks)

Our faculty and students explore how artificial intelligence and machine learning can transform health care. We analyze large-scale databases in search of better ways to diagnose and treat conditions including cancer, diabetes and cardiovascular disease.

As a Ph.D. student, you will help design and evaluate new algorithms and advanced learning models. You will also learn the benefits of collaboration in an interdisciplinary environment.

Our projects include:

  • Machine learning and deep learning to examine the distribution and interactions of cancer cells within the tumor microenvironment while integrating multiplex tissue imaging techniques. (Chang Lab)
  • Applying machine learning and deep learning techniques to better integrate imaging and omics data. (Chang Lab)
  • Developing new mathematical models of metabolism and other physiologic processes and integrating these models into drug delivery platforms in the area of diabetes. (Artificial Intelligence for Medical Systems/AIMS Lab)
  • Development and evaluation of machine learning models to predict glucose and prevent exercise-induced hypoglycemia and nocturnal hypoglycemia in type 1 diabetes. (AIMS Lab)
  • Developing new bioinformatics tools for data analysis to identify biological mechanisms in conditions like cancer. (Xia Lab)
  • Using “decision under uncertainty” theory to improve algorithms for use in medical decision support, diagnostics and other interventions, specifically in the field of diabetes. (AIMS Lab)
  • Creating systems that allow the integration of data from large cancer cohorts for synthetic lethality target detection, significant noncoding mutation detection and pathway level mutational effect analysis. (Ellrott Lab)
  • Developing machine-learning tools to analyze single-cell data to identify distinct cell subpopulations associated with biological and clinical phenotypes. (Xia Lab)

Join our program

Your research career starts here.

Artificial intelligence and machine learning faculty