Carnegie Mellon University

Faculty and Funded Projects

Responding to the pandemic, addressing long-standing racial inequities and ensuring the security and transparency of U.S. elections are existential priorities for our national health, economy and democracy. Each of these challenges have laid bare deeply-entrenched societal problems that have in turn accelerated and amplified the current crisis. Addressing these issues will require new thinking, new technologies and new evidence.

This year, we funded twelve projects aimed at developing technological solutions to the COVID-19 crisis, improving equity through technology, and ensuring accurate and transparent U.S. elections. These projects include using AI to uncover racial bias of EEG tests to supporting hospitality workers navigate technological change to developing novel hiring practices that reduce bias while improving efficiency.

These projects that bring together Carnegie Mellon University’s world-class researchers in collaboration other academic institutions and local and national practioners to apply cutting edge AI and machine learning techniques and proven social science-based approaches to address our most critical and timely societal challenges.

2020 Funded Projects

*Detailed descriptions below.
Developing technological solutions to combat COVID-19

The COVID-19 pandemic has highlighted the need for innovative, targeted solutions that solve both short-term concerns and long-term issues. Through these six projects, our researchers are addressing pivotal issues at the intersection of public health and economic growth in order to support workers, identify weaknesses in supply chains, and ensure that aid is provided to those most impacted by the crisis.

Improving Equity through Technology

Artificial intelligence, machine learning, and advanced automation strategies have the potential to close longstanding societal gaps when applied thoughtfully. These five projects aim to use emergent technological solutions to support fairer, more equitable practices in such sectors as hiring, healthcare, and child welfare.

Ensuring Accurate and Transparent U.S. Elections

The 2020 election season has revealed serious gaps in the public’s trust of the electoral process. In order to promote trust in this cornerstone of democracy, we are supporting research that uses innovative statistical solutions to enhance the transparency, accuracy, and effectiveness of U.S. elections.

Developing technological solutions to combat COVID-19

The Impact of the COVID-19 Shutdown on the Most Venerable Households: New Data to Identify the Greatest Need

In a matter of months, the COVID-19 pandemic and economic shutdown has severely jeopardized already marginalized and at-risk populations in ways unlike other economic recessions. To address the economic downturn, limited resources must be correctly allocated in a rapid and effective manner. Using large-scale GPS data in combination with data records maintained by Allegheny County’s Department of Health and Human Services, this project will equip policymakers and service providers with the tools to respond to the urgent medical, social and economic needs of this unprecedented crisis. For example, this project aims to characterize the loss of both “formal” and “informal” income, going beyond traditional metrics to examine the effect of the shutdown on the gig economy. Additionally, the insights gathered from this project will help to bolster contact tracing efforts and evaluate the impact of the pandemic on income loss, mental health, housing instability, and other social factors that are difficult to identify and measure with existing data resources.

Designing Better Autonomous-Transit Systems for Enhanced Workforce Resilience

From wildfires to the current pandemic, there is an increasing demand to identify new systems that meet critical transport needs without putting drivers at risk. Autonomous vehicles could meet such a need while bolstering a more environmentally friendly transportation infrastructure. During the current COVID-19 crisis, transportation systems sit at the crux of quarantine management and workforce protection, as essential workers that rely on public transportation and transit workers are at high risk of exposure to the virus. Autonomous vehicles could support contactless delivery, emergency response and evacuation, and essential worker commuting. This study will consider the workforce implications and climate impact of integrating autonomous vehicles into transit systems, as well as develop practical tools to inform policy regarding autonomous vehicle adoption and infrastructure development.

Co-Developing Automation Policy for the Post-COVID Hospitality Industry

The COVID-19 pandemic has had a devasting impact on the hospitality industry, causing this major sector to effectively grind to a halt. In April, a staggering 98% of members of UNITE HERE, the largest hospitality workers union in North America, had been furloughed or laid off. While automation is a promising avenue to revitalize this sector and complement human labor, there are reasonable concerns about displacement or a lack of employee input into its integration. In partnership with UNITE HERE, we are co-designing automation policy  and visioning technology that addresses the needs and concerns of workers in the hospitality industry, ensures that work in this sector is safe and fulfilling, and supports policymaking and collective action. 

An Open-Source Decision Tool to Identify and Support Responses to Emergent Constraints in the Medical Supply Chain

In the early stages of the COVID-19 pandemic, a supply chain breakdown resulted in a shortage of the elastic for ear loops, preventing the production of at least 9 million additional medical masks. This is just one example of the myriad ways in which the pandemic has emphasized the significant global interdependencies within the health and manufacturing sectors, as well as hampered these sectors’ ability to meet the increased demand for critical PPE. While identifying and responding to these weaknesses in the medical supply chain is now particularly important; the supply chain issues highlighted by COVID-19 are a long-term problem. In order to overcome existing and future bottlenecks and capacity constraints, we are working with Catalyst Connection, the Manufacturing Extension Partnership of Southwestern Pennsylvania, to develop an open-source decision tool that uses publicly available data to inform domestic and international manufacturing responses to resource demands and guide innovation to meet supply constraints.

PaCE: Developing a Pandemic Consumption Expenditure Index

OpenTable restaurant reservations rapidly collapsed two weeks prior to Pennsylvania Governor Tom Wolf’s Shelter-In-Place order. Movie theaters in Georgia remain empty in spite of the state government’s decision to re-open them on April 27. These are just two examples suggesting that consumer behavior—not government mandate—will be the key driver of the economy’s transition from lock-down back to economic health. Through the development of a Pandemic Consumption Expenditure Index (PaCE), we are examining consumer behaviors in real-time in order to minimize the economic impact of the COVID-19 pandemic and bolster critical decision-making for policymakers, businesses and workers through each phase of lockdown and reopening. Beyond COVID-19, PaCE will help policy makers better understand how other pandemic-like shocks to the economy are impacting consumer behavior at a level far more granular and relevant than existing data sources.

The Role Of Co-Experience And Technology In Mitigating Isolation From Social Distancing

The prolonged isolation caused by COVID-19 has translated into depression and other mental health issues for nearly half of American adults. Though COVID-19 has drastically exacerbated the problem, social isolation was already a long-term problem threatening the elderly, mentally ill and other vulnerable populations, and will continue to be when COVID-19 recedes into the past. While technology will never be a perfect substitute for in-person interaction, it can help mitigate the effects of social isolation. This project considers the impact of virtual interactions on loneliness, social connectedness, and mental wellbeing through a psychological and behavior lens, to identify and test the viability of developing sufficient substitutes for in-person interaction by leveraging the design features inherent to video chat, phone calls, text messaging, and other communication channels. The specific focus of the project is on the importance of the simultaneity of the experience – i.e., knowledge that the individuals are experiencing the same thing at the same time – as well as mutual awareness of that simultaneity.

Equity and Fairness in AI

Fast and Fair Hiring via Segmented Evaluations

On average, a given job listing in the United States will attract applications from 118 candidates. As widespread job losses in certain sectors coupled with surges in labor demand in others have drawn attention to several major flaws in the hiring process, this project considers a novel human resources approach to counteract long-standing biases and inefficiencies within the hiring process. Traditionally, an individual hiring manager or a small group of hiring managers review a complete application to decide whether or not to hire an individual. However, there is significant evidence that the process is not only inefficient, but often biased against people of color, immigrants, and other marginalized demographic groups. To address these issues, the research team has developed a novel blended human-AI approach where applications are evaluated in a segmented manner. Throughout the year, the team will test and study the new approach to measure improvements in equity and efficiency.

Understanding Historical Biases in EEG Data and Neuroscientific Studies Because of Bias in Data Acquisition Systems

Electroencephalography (EEG) systems have been a common medical practice to identify brain conditions for nearly one hundred years, yet only recently a team of researchers at Carnegie Mellon University determined that common EEG machines are less effective for patients with coarse and curly hair, which is common in individuals of African descent. This significant gap has shed light on the sampling bias inherent to past EEG data, which informs clinical decision-making regarding stroke, epilepsy, and other neural conditions. Through a comparative analysis of neuroscientific data gathered using traditional EEG systems and “gold-cup” electrodes developed to work with coarse and curly hair, this project aims to quantify statistical differences between these two methods and explore algorithmic techniques for correcting bias in past EEG data, as well as incentivize clinicians and neuroscientists to switch to newer, fairer EEG systems.

An Integrated Framework for Studying and Regulating Human-AI Hybrid Decision-Making Systems

From generating risk scores that inform lending decisions to helping to narrow a pool of job applicants, algorithms are increasingly used as a predictive tool to improve the choices made by human decision-makers. However, little is known about the ways in which humans interpret, use, and trust AI-supported tools. Through a series of behavioral experiments, this project aims to better understand how AI-supported tools actually impact human judgement and evaluation patterns. These experiments will include examining how participants react when an algorithm supports or refutes their initial judgment, how access to additional information impacts decision-making, and how the benefits of correct judgments and costs of incorrect decisions affect participants’ behavior.

Laying the Technological Groundwork for Child Welfare Decision Support Systems through Advanced NLP

Child welfare agencies across the country are continually looking for ways to use their data to better support families and improve decisions at every stage of their processes.  Recent efforts to develop machine learning tools for child welfare, such as those in Allegheny County, PA and Douglas County, CO, have primarily focussed on structured administrative data.  In this project we’re partnering with the Allegheny County Department of Human Services on an innovative project that will leverage unstructured free-text data to support case management, supervision, and quality improvement efforts.  We are developing advanced natural language processing technologies capable of using high volumes of both structured and unstructured data to assist service providers, caseworkers, supervisors, and other DHS staff.

Making Explainable Machine Learning Work for Public Policy Problems

As applications of machine learning and AI are rapidly expanding into new areas of public life, it is important to consider whether public sector decision makers adequately understand the outcomes of the algorithms in a way that leads to optimal outcomes for society. In order to address this gap, we are exploring the applicability and effectiveness of existing approaches for explainable machine learning in public policy contexts. This project will examine the impact of improved explainability on policy outcomes related to increasing high school graduation rates, preventing adverse police interactions with the public, supporting mental health interventions to break the cycle of incarceration, and reducing long-term unemployment. Based on these results, we aim to generate a set of guidelines for governments, non-profits, and policymakers who are procuring machine learning systems, as well as for the researchers and practitioners who might be developing systems for use in public sector decision-making.

Improving the transparency, accuracy and effectiviness of U.S. Elections

Developing and deploying risk-limiting audits with continuous monitoring

According to a recent Gallup poll, 59% of Americans say they are not confident in the honesty of U.S. elections. Public trust in elections is integral to democracy. At the same time, manual election recounts when voting results are called into question cost time and money, and may not always be possible due to lack of paper trails. Even the process of deciding to perform a recount is generally ad hoc and triggered by post-election concerns. This project aims to use rigorous statistical tools to continuously monitor the audit and potentially end it early, as soon as it can be verified with high confidence that the announced result is correct. The increased efficiency of auditing will lower the time and money involved without sacrificing legitimacy, and encourage more states to normalize the creation of paper trails as well as sound post-election audit processes. Through better election transparency, we aim to support the restoration of trust in the democratic process, while maintaining accuracy in determining election outcomes.

Center Faculty

Henny Admoni

Henny Admoni
Assistant Professor, Robotics Institute

 Laurence Ales

Laurence Ales
Associate Professor of Economics

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Vincent Aleven
Professor and Director of Undergraduate Programs in Human-Computer Interaction

Kasun Amarasinghe

Kasun Amarasinghe
Postdoctoral Research Associate, Machine Learning Department

 Linda Argote

Linda Argote
David M. Kirr and Barbara A. Kirr Professor of Organizational Behavior and Theory

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Saurabh Bhargava
Assistant Professor of Economics, Social and Decision Sciences

Jeffrey Bigham

Jeffrey Bigham
Associate Professor, Human-Computer Interaction Institute

Lee Branstetter

Lee Branstetter
Professor of Economics and Public Policy

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Daragh Byrne
Associate Faculty, School of Architecture and the Integrated Innovation Institute

Stuart Candy

Stuart Candy
Associate Professor, School of Design

Howie Choset

Howie Choset
Kavcic-Moura Professor of Computer Science

Alexandra Chouldechova

Alexandra Chouldechova
Assistant Professor of Statistics and Public Policy

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Laura Dabbish
Associate Professor, Human-Computer Interaction Institute

David Danks

David Danks
Department Head and L.L. Thurstone Professor of Philosophy and Psychology

Anupam Datta

Anupam Datta
Professor of Electrical and Computer Engineering

Alexander Davis

Alexander Davis
Assistant Professor of Engineering and Public Policy

Simon DeDeo

Simon DeDeo
Assistant Professor of Social and Decision Sciences

Jodi Forlizzi

Jodi Forlizzi
Geschke Director and Professor, Human-Computer Interaction Institute

Sarah Fox

Sarah Fox
Presidential Postdoctoral Fellow, Human-Computer Interaction Institute

Erica Fuchs

Erica R.H. Fuchs
Professor of Engineering and Public Policy

Rayid Ghani

Rayid Ghani
Distinguished Career Professor

Seth Goldstein

Seth Goldstein
Associate Professor of Computer Science

Pulkit Grover

Pulkit Grover
Associate Professor of Electrical and Computer Engineering

Oliver Hahl

Oliver Hahl
Frank A. and Helen E. Risch Faculty Development Professor of Business

Corey Harper

Corey Harper
Assistant Professor of Civil and Environmental Engineering

Amelia Haviland

Amelia Haviland
Associate Professor of Statistics and Health Policy

Lauren Herckis

Lauren Herckis
Simon Initiative Research Faculty

Ken Holstein

Ken Holstein
Assistant Professor, Human-Computer Interaction Institute

Mark Kamlet

Mark Kamlet
University Professor of Economics and Public Policy; Provost Emeritus

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Valerie J. Karplus
Associate Professor of Engineering and Public Policy

Shawn Kelly

Shawn K. Kelly
Senior Systems Scientist

Edward Kennedy

Edward Kennedy
Assistant Professor of Statistics

Tae Wan Kim

Tae Wan Kim
Assistant Professor of Business Ethics

Aniket Kittur

Aniket D. Kittur
Professor, Human-Computer Interaction Institute

Kenneth R. Koedinger

Kenneth R. Koedinger
Professor, Human-Computer Interaction Institute

Felix Koenig

Felix Koenig
Assistant Professor Of Economics

Brian Kovak

Brian Kovak
Associate Professor of Economics and Public Policy

Ashwati Krishnan

Ashwati Krishnan
Postdoctoral Researcher, Electrical and Computer Engineering

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Chinmay Kulkarni
Assistant Professor, Human-Computer Interaction Institute

Min Kyung Lee

Min Kyung Lee
Research Scientist, Center for Machine Learning and Health

Rebecca Lessem

Rebecca Lessem
Associate Professor of Economics

Beibei Li

Beibei Li
Assistant Professor of Information Systems and Management

Zachary Chase Lipton

Zachary Chase Lipton
Assistant Professor of Business Technologies

George Loewenstein

George Loewenstein
Herbert A. Simon University Professor of Economics and Psychology

Alex John London

Alex John London
Clara L. West Professor of Ethics and Philosophy

Tom Mitchell

Tom Mitchell
E. Fredkin University Professor of Machine Learning and Computer Science

M. Granger Morgan

M. Granger Morgan
Hamerschlag University Professor of Engineering

Daniel Nagin

Daniel Nagin
Teresa And H. John Heinz III University Professor of Public Policy and Statistics

Destenie Nock

Destenie Nock
Assistant Professor of Engineering and Public Policy

Illah Nourbakhsh

Illah Nourbakhsh
K&L Gates Professor of Ethics and Computational Technologies

Adam Perer

Adam Perer
Assistant Research Professor, Human-Computer Interaction Institute

Raj Rajkumar

Raj Rajkumar
George Westinghouse Professor of Electrical and Computer Engineering

Aaditya Ramdas

Aaditya Ramdas
Assistant Professor of Statistics and Machine Learning

R. Ravi

R. Ravi
Andris A. Zoltners Professor of Business

Kit Rodolfa

Kit Rodolfa
Research Project Scientist, Machine Learning Department

Nihar Shah

Nihar Shah
Assistant Professor of Machine Learning

Michael Shamos

Michael I. Shamos
Distinguished Career Professor of Computer Science

Param Vir Singh

Param Vir Singh
Carnegie Bosch Associate Professor of Business Technologies

Rick Stafford

Rick Stafford
Distinguished Service Professor of Public Policy

Molly Steenson

Molly Wright Steenson
K&L Gates Associate Professor of Ethics & Computational Technologies

David Steier

David Steier
Distinguished Service Professor

Ameet Talwalkar

Ameet Talwalkar
Assistant Professor of Machine Learning

Lowell Taylor

Lowell Taylor
H. John Heinz III Professor of Economics

Rahul Telang

Rahul Telang
Professor of Information Systems

Christopher Telmer

Christopher I. Telmer
Associate Professor of Financial Economics; Head of Economics

Yulia Tsvetkov

Yulia Tsvetkov
Assistant Professor, Language Technologies Institute

Conrad Tucker

Conrad Tucker
Arthur Hamerschlag Career Development Professor

https://www.cmu.edu/epp/people/faculty/parth-vaishnav.html

Parth Vaishnav
Assistant Research Professor of Engineering and Public Policy

Venkat Viswanathan

Venkat Viswanathan
Assistant Professor of Mechanical Engineering

Anita Woolley

Anita Woolley
Associate Professor of Organizational Behavior and Theory

Sevin Yeltekin

Sevin Yeltekin
Professor of Economics; Senior Associate Dean, Education

Erina Ytsma

Erina Ytsma
Assistant Professor of Accounting

Ariel Zetlin-Jones

Ariel Zetlin-Jones
Associate Professor of Economics