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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.
Pages
Peter Washington
Academic website for Peter Washington, Assistant Professor in Computer Science at the University of Hawaii at Manoa
Contact
Contact information for Peter Washington, Assistant Professor in Computer Science at the University of Hawaii at Manoa
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Research Lab
Research projects led by Peter Washington, Assistant Professor in Computer Science at the University of Hawaii at Manoa
Teaching
Courses taught by Peter Washington, Assistant Professor in Computer Science at the University of Hawaii at Manoa
Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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contact
portfolio
Machine Learning for Healthcare
We develop deep learning methods and datasets to support diagnostics, longitudinal outcome tracking, and adaptive health.
Crowdsourcing for Precision Health
We create human-in-the-loop systems to accelerate remote diagnostics and longitudinal outcome tracking for complex neuropsychiatric conditions.
Digital Interventions for Health and Wellbeing
We develop mobile, web, wearable, augmented reality, and virtual reality interventions for psychiatric, developmental, and behavioral conditions.
publications
Paper Title Number 1
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
Paper Title Number 2
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
Paper Title Number 3
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
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
ICS 691D: Human-Centered Artificial Intelligence
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.
ICS 435/635: Machine Learning
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.
ICS 491: Human-Centered Data Science
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.