Research

I study how social ties, technology, and institutional trust shape people’s evaluation of information and misinformation. My research explores the communication processes that drive public opinion, attitudes, and behavior, particularly in contexts where trust is contested and information is politicized. These questions are increasingly important against the backdrop of rapid technological change and deepening sociopolitical divisions. Emerging technologies, including generative AI and algorithmic personalization, are reshaping how people encounter and interpret information. Combined with declining trust in traditional institutions, this shift threatens our collective ability to make informed decisions and address pressing challenges.

My work draws on multiple methodological frameworks, including computational social science, network science, and survey research.

While I use a variety of tools for data manipulation and statistical analysis, the bulk of my work is done using the R language for statistical computing + RStudio. You can find my R tips & tutorials on my blog or my GitHub page.

My recent papers and publications are available here.

Selected Projects

The Civic Health and Institutions Project (CHIP50)

This multi-university initiative built an innovative infrastructure for large-scale survey data collection with unmatched temporal and spatial granularity across all 50 US states. During the COVID-19 pandemic, we provided insights into health behaviors and policy attitudes. The project has since expanded into a broad civic survey capturing critical trends in health, politics, inequality, trust, and public behavior.

By 2025, CHIP50 had collected 35 waves of survey data, with a typical sample of roughly 25,000 participants per wave, accumulating over 900,000 total responses. Surveys were further augmented with social media and digital behavior data. In 2025, the project received the Warren J. Mitofsky Innovators Award from the American Association for Public Opinion Research (AAPOR), recognizing its novel social science data infrastructure.

CHIP50 provides a rich empirical basis for advancing theory and testing models of information diffusion, media effects, and social influence. As of 2025, it has been cited in over 200 NIH-indexed papers and more than 500 scholarly works overall.

The National AI Opinion Monitor (NAIOM)

This project aims to systematically track public perceptions, knowledge, and trust in artificial intelligence technologies. As AI tools increasingly shape education, healthcare, employment, and governance, understanding public attitudes is critical for equitable and ethical AI policy development. The project relies on a large longitudinal national survey with oversampling for minority populations, as well as youth and older adults. In New Jersey, additional state-specific surveys will focus on key policy sectors and marginalized communities.

Early findings reveal an emerging “AI divide” — younger, wealthier, and more highly educated Americans are significantly more likely to know and trust AI technologies, while older, less affluent, and minority groups report greater skepticism and lower usage (Ognyanova & Singh, 2025).

The COVID States Project (CSP) 

This multi-university NSF-funded project includes a large-scale longitudinal data collection that aims to provide timely information to communities and policymakers. Monthly surveys in all 50 states are combined with social media data from a panel of consenting survey respondents. Network-based survey questions allow us to assess the spread of COVID-19 and its impact not only on our participants, but also on their personal networks and local communities. The study aims to track the prevalence of the virus, as well as its social, technological, and economic consequences. We examine how well the information and communication needs of Americans are met during this crisis. Importantly, the project also seeks to understanding the impact of the pandemic on communities of color which are disproportionately affected by this crisis. Reports, data, and interactive charts from the project are available on covidstates.org.