Page Not Found
Page not found. Your pixels are in another canvas.
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Page not found. Your pixels are in another canvas.
Academic website for Peter Washington, Assistant Professor in Computer Science at the University of Hawaii at Manoa
Contact information for Peter Washington, Assistant Professor in the Division of Clinical Informatics and Digital Transformation (DoC-IT) at the University of California, San Francisco (UCSF)
This is a page not in th emain menu
Research projects led by Peter Washington, Assistant Professor in Computer Science at the University of Hawaii at Manoa
Courses taught by Peter Washington, Assistant Professor in Computer Science at the University of Hawaii at Manoa
Working with Peter Washington
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml
and set future: false
.
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
We develop deep learning methods and datasets to support diagnostics, longitudinal outcome tracking, and adaptive health.
We create human-in-the-loop systems to accelerate remote diagnostics and longitudinal outcome tracking for complex neuropsychiatric conditions.
We develop mobile, web, wearable, augmented reality, and virtual reality interventions for psychiatric, developmental, and behavioral conditions.
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Graduate course, ICS Department, University of Hawaiʻi at Mānoa, 2022
This is a discussion-based course covering the latest research in human-centered artificial intelligence (HAI). Topics covered include: interactive ML systems, explainable & interpretable ML, AI for healthcare, fairness & bias, privacy, crowdsourcing, HCI evaluations of AI-powered systems, and ethical frameworks applied to AI.
Undergraduate and graduate course, ICS Department, University of Hawaiʻi at Mānoa, 2023
This is a mathematics and programming heavy introduction to machine learning. Topics include machine learning programming in Python, classical machine learning methods, and an introduction to deep learning. Coursework consists of 5 homeworks (combination of written and Python coding problems), an in-class midterm, a final exam, and a final project.
Undergraduate course, ICS Department, University of Hawaiʻi at Mānoa, 2023
There are several challenges that arise when working with human data, including privacy, fairness, ethics, and new modeling methods - to name just a few. Human-Centered Data Science is a course which covers the human-facing aspects of data science. This course discusses methods developed specifically for the analysis of biological, medical, social, text, and audiovisual data. As such, this course will cover the basics of biomedical informatics, health informatics, social network informatics, media analytics, and natural language processing. These topics require a thorough discussion of study design, research study analysis, privacy-preserving analysis, unbiased analysis, explainable analysis, qualitative analysis, and ethical tradeoffs between analysis decisions. No prior data science knowledge is required.