Computing for Good

Overview

Computing for Good (C4G) presents computer science as a technological platform for improving the quality of life and humans condition. It allows students to apply computing to social and global causes and to see its impact in real terms. Students work on projects in partnership with communities, non-profits, and government agencies and learn how to design, develop, and deploy technical solutions for real world problems. Examples of topics include but not limited to health, economic development, disaster relief, transportation, infrastructure, education, energy, social service, civic engagement, and public safety.

Course Topics:

  • Ubiquitous and Mobile computing

  • AI and Machine learning

  • Data analytics

  • Visualization

  • Stakeholder-centered design and prototyping

  • System Development

  • Crowd-sourcing

  • User studies

  • Project management and teamwork

News Coverage

The new Carnegie Mellon University human-computer interaction course, taught by Afsaneh Doryab and Anind Dey, explored how computing skills can be applied to social and global causes.

CMU students learned in a new course this fall that applying existing technologies in new ways can unite communities or perhaps save lives.

Computing for Good Fall 2018 Projects

Night Owl Bakery: On-demand work recommendation for disadvantaged youth using smartphones

Partner Community: Latham Street Commons; https://www.lathamstcommons.org/

Latham St. Commons (LSC) is a 501(c)3 non-profit organization located on a quarter-acre lot at the heart of the Garfield and Friendship neighborhoods in Pittsburgh with a mission to improve the health of the community by addressing social, educational and economic needs of the members.

Mentor: Afsaneh Doryab

Pittsburgh’s disadvantaged young adults experience greater degrees of economic disparity due to structural barriers between them and economic opportunities and experience devastatingly high levels of violence, physical, and emotional abuse.

We worked closely with disadvantaged young adults, co-designing a program that exposed them to new ways of learning, design thinking and making, advanced technologies, and alternative ways of dealing with stress, self-worth, and care. Our training program, Night Owl Bakers (NOB), provides a holistic approach to self-identity and employment preparedness for young adults between the ages 16-22. The curriculum has 3 phases that progress from a series of cross-disciplinary, hands-on experiences in business and financing, technology and science, food literacy and sourcing, and self-care and mindfulness. Ultimately, the program is designed to prepare participants for the future so they can experience a fuller sense of who they are, what they are a part of, and that they are capable of joining the workforce prepared.

In parallel with the training program, we want to build an intelligent system to support on-demand work that matches individuals skills, preferences, location, and environment. Our on-demand NOB work app uses smartphones to identify and recommend relevant just-in-time work opportunities for the youth to apply their new skills.

Vita: A smartphone app to collect and visualize patient-reported symptoms during cancer treatment

Partner: Biobehavioral Oncology + Technology Lab at the University of Pittsburgh

Mentor: Afsaneh Doryab


Symptoms such as nausea, pain, and fatigue are common during cancer treatment, fluctuate over time, can impair function and quality of life. At UPMC Hillman Cancer Center and most other medical centers, there is no standardized approach to tracking these symptoms over time and between doctor visits. This results in needless patient suffering as most symptoms and side effects are poorly managed. There is a need for solutions designed specifically for cancer patients (who tend to be older and sicker and who may require long-term monitoring over many years) to easily monitor the symptoms that matter most to them, to see clear patterns in how their symptoms vary over time and in response to their activity, sleep, and medications, and to share this information with their doctors.

Cardiaction: Emergency Response in Modern Society

Partner: Leonard S. Weiss, MD, University of Pittsburgh Emergency Medicine

Mentor: Afsaneh Doryab


Cardiaction conceptualized a body worn sensor to detect position or acceleration change or even biometric telemetry changes that are connected to the emergency response system and can generate a welfare check via voice, video, or even 911 emergency responders if necessary. In addition, the group made a monitoring system of the status quo of human behaviors in a crowd. When these behaviors or positions change from the norm, an emergency may be present. Detecting this change could allow an early alert for further investigation, reconnaissance, or emergency response. Lastly, the team conceptualized improvement to direct location-based event transmission to 911 and emergency responders. Imagine that when an emergency occurred and 911 is notified, the exact location is visible to 911 dispatchers and emergency responders, rather than a street address, generalizable location, or nearby intersection.

Inclusion and Diversity: Implicit Bias Detection

Partner: SCS4ALL/Women@SCS in collaboration with Dr. Geoff Kaufman

Mentor: Afsaneh Doryab

Implicit bias continues to be a persistent source of prejudice and discrimination across many domains. This pressing social issue has significant negative consequences, particularly for stereotype-targeted groups, such as women, people with disabilities, low socioeconomic status populations, and racial minority groups. The threat linked with being the target of bias has been shown to activate a variety of anxiety- and stress-related physiological responses, including changes in heart rate acceleration, skin conductance, body temperature, and blood pressure.

We are looking for solutions to link bias experiences with physiological responses to make people aware of their implicit bias. Using mobile and wearable sensors we are looking to detect occurrences of bias and the social contexts and locations in which the experience with bias are more likely to take place in the city of Pittsburgh.

Computing for Good Fall 2017 Projects

West Homestead Walks Together Community Walking App

Client: West Homestead Borough

Mentor: Afsaneh Doryab

The many physical benefits of walking are commonly known – improved circulation, lowered blood pressure, improved bone density, enhanced muscle tone, better sleep, increased stamina. In older adults, regular walking has been shown to slow age-related memory decline, and lower the incidence of dementia and Alzheimers. And of course, for the more than two-thirds of adults who are overweight or obese, walking can contribute to weight loss and weight maintenance. However, at least one study has found that Americans only walk about half of the recommended 10,000 steps per day.

As a “Live Well Allegheny” Community, West Homestead has been exploring more ways to get residents outside and moving. In addition to a planned non-motorized boat ramp on the Monongahela, Borough officials would like to see more people walking in the community.

Enabling High-Throughput Catalyst Design for Renewable Energy

Client: Zachary Ulissi, Assistant Professor, Chemical Engineering, Carnegie Mellon University

Mentor: Dr. Zachary Ulissi, Dr. Afsaneh Doryab

VR/AR for an African Context





Client: Bruce Krogh, Director, CMU-Africa

Mentor: Dr. Afsaneh Doryab

Matching "In Need" Populations with Online Education Institutions

Client: McNeese State University

Mentor: Dr. Mathew Burkus, Associate Professor, McNeese State University

Risk Prediction Tool for Marginal Farmers


Client: NIRMAN, India and KONAM Foundation, India

Mentor: Dr. Anind Dey



Identifying mining sites in conservation areas from satellite imagery

Client: Fei Fang, Assistant Professor, Carnegie Mellon University

Mentor: Dr. Fei Fang, Dr. Afsaneh Doryab

Agromovil – App platform to sustainably Reduce Post-Harvest Loss in the Developing World

Client: AMGlobal Consulting

Mentor: Dr. Afsaneh Doryab


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