Peter Washington

I am an Assistant Professor in the Information & Computer Sciences department at the University of Hawaiʻi at Mānoa (UHM). Prior to joining UHM, I completed by PhD in Bioengineering at Stanford University, MS in Computer Science at Stanford University, and BA in Computer Science at Rice University. You can read my PhD thesis here.

My research interests include developing data science methods to support machine learning for health and wellbeing, crowdsourcing for precision health, and precise digital interventions. I am interested in applying these methods to a variety of healthcare problems.

News

  • August 2022: Peter Washington starts the Hawaiʻi Digital Health Lab as an Assistant Professor in Information & Computer Sciences.

Join the lab

The Hawaiʻi Digital Health Lab has an opening for 2 PhD students as well as several openings for masters, undergraduate, and high school students.

For the University of Hawaiʻi PhD students, RAships are available. Send me an email with your resume/CV, a description of which of my previous papers interests you and why, and a few sentences about your “dream research project”.

For University of Hawaiʻi masters students, course credit is available in the form of ICS 499, ICS 699, or ICS 700. Note that masters students completing Plan A must complete 6 units of ICS 700 and masters students completing Plan B must complete 6 units of ICS 699. Send me an email with your resume/CV and which of the following skillsets you would like to contribute to the lab: web development, mobile development (iOS or Android), data analysis, and/or machine learning (TensorFlow/Keras or PyTorch).

For University of Hawaiʻi undergraduate students, course credit is available in the form of ICS 499. In addition, I am actively accepting mentees for the Undergraduate Research Opportunities Program (UROP). If interested in receiving ICS 499 course credits and/or participating in the UROP program, send me an email with your resume/CV and which of the following skillsets you would like to contribute to the lab: web development, mobile development (iOS or Android), data analysis, data mining, and/or machine learning (TensorFlow/Keras or PyTorch).