Jennifer Mankoff


Interests: Studies of critical social problem areas leading to the creation of technological innovations and generalizable enabling tools and techniques.

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Jennifer Mankoff

Office: NSH 2504A
Phone: +1 (412) 567-7720
Fax: +1 (412) 268-1266
Admin: Brandy Renduels

Human Computer Interaction Institute,
School of Computer Science
Carnegie Mellon University
5000 Forbes Ave
Pittsburgh, PA 15213-3891

Research Interests

Sustainability:

StepGreen.org is a website designed to encourage individuals to reduce their ecological footprints. It is also a deployment platform for a set of projects focused around energy use, including projects to More information about our research agenda, our funders, press, and so on can be found at research.stepgreen.org

Health 2.0:

Chronic illness is defined as an illness or disease that is long-term or permanent, as opposed to acute. For many individuals living with a chronic condition, the web has served as an important medium for researching treatment options, developing a support network, and sharing information and experiences with others.

Our purpose is to learn about how individuals with chronic illness, and their caregivers, use resources available online. Such resources include sites offering medical information as well as research and experience blogs, forums, and others.

Currently, we are focusing our work on individuals with Lyme disease. Because this illness is disputed in both diagnosis and treatment methods among healthcare professionals (Weintraub, 2008), individuals with Lyme disease depend on online advocacy and activism to manage their condition. At the same time, divergent and conflicting information is common online. How does this affect the experience and outcomes for individuals with Lyme disease?

More information about our research agenda and current studies can be found at chronicweb.wordpress.com

Adaptive Assistance tools: sensing and adapting to user need and context:

Our current work in this arena involves gathering hundreds of hours of mouse and keyboard use by individuals with and without disabilities. We are developing analysis tools for finding targets and machine learning algorithms capable of extracting information about performance from this data.

More information about this work can be found at graduating Phd student Amy Hurst's website.

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