Lyle Ungar

About

Lyle Ungar is a Professor of Computer and Information Science, Psychology, Bioengineering, Operations, Information and Decisions, and Genomics and Computational Biology at the University of Pennsylvania. His research interests encompass a broad range of topics including information economics, statistical relational learning, text mining, active learning, market-based methods for distributed scheduling and system optimization involving human and computer agents, and gene and protein expression. Ungar's work also focuses on clustering and collaborative filtering, genomics, regulatory network modeling, information extraction from biological texts and consumer data, machine learning, data mining, and computational biology, with future interests in computer go. Throughout his career, Ungar has contributed to advancing understanding in these fields through research that integrates computational methods with biological and social sciences. His work on information extraction and modeling in biological texts and genomics aims to improve data analysis in biological systems, while his investigations into market-based and active learning methods seek to optimize complex systems and decision-making processes. Ungar's interdisciplinary approach combines insights from computer science, psychology, and biology to address complex scientific and practical problems.

Research topics

• Computer Science
• Artificial Intelligence
• Sociology
• Psychology
• Medicine
• Mathematics
• Social psychology
• Demography
• Psychiatry
• Virology

Selected publications

• Megastudies improve the impact of applied behavioural science

  Nature · 2021 · 254 citations

  PublisherOA PDFDOI

• Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods

  Proceedings of the National Academy of Sciences · 2020 · 227 citations

  = 0.64. We show that the findings generalized to county socioeconomic and health outcomes and were robust when poststratifying the samples to be more representative of the general US population. Regional well-being estimation from social media data seems to be robust when supervised data-driven methods are used.

  DOI

• Characterizing Geographic Variation in Well-Being Using Tweets

  Proceedings of the International AAAI Conference on Web and Social Media · 2021 · 222 citations

  Senior authorCorresponding

  The language used in tweets from 1,300 different US counties was found to be predictive of the subjective well-being of people living in those counties as measured by representative surveys. Topics, sets of co-occurring words derived from the tweets using LDA, improved accuracy in predicting life satisfaction over and above standard demographic and socio-economic controls (age, gender, ethnicity, income, and education). The LDA topics provide a greater behavioural and conceptual resolution into…

  DOI

• The emotional and mental health impact of the murder of George Floyd on the US population

  Proceedings of the National Academy of Sciences · 2021 · 212 citations

  = 319,471). According to the Gallup data, in the week following Floyd's death, anger and sadness increased to unprecedented levels in the US population. During this period, more than a third of the US population reported these emotions. These increases were more pronounced for Black Americans, nearly half of whom reported these emotions. According to the US Census Household Pulse data, in the week following Floyd's death, depression and anxiety severity increased among Black Americans at signifi…

  PublisherOA PDFDOI

• A 680,000-person megastudy of nudges to encourage vaccination in pharmacies

  Proceedings of the National Academy of Sciences · 2022 · 191 citations

  Encouraging vaccination is a pressing policy problem. To assess whether text-based reminders can encourage pharmacy vaccination and what kinds of messages work best, we conducted a megastudy. We randomly assigned 689,693 Walmart pharmacy patients to receive one of 22 different text reminders using a variety of different behavioral science principles to nudge flu vaccination or to a business-as-usual control condition that received no messages. We found that the reminder texts that we tested incr…

  PublisherDOI

Recent grants

• Predicting AOD Relapse and Treatment Completion from Social Media Use

  NIH · $1.6M · 2014–2019

• Training Program in Computational Genomics

  NIH · $9.0M · 1999–2030

Frequent coauthors

• Raina M. Merchant

  University of Pennsylvania

  113 shared

• Sharath Chandra Guntuku

  University of Pennsylvania Health System

  110 shared

• Johannes C. Eichstaedt

  95 shared

• H. Andrew Schwartz

  93 shared

• David A. Asch

  86 shared

• Salvatore Giorgi

  University of Pennsylvania

  75 shared

• Brenda Curtis

  National Institute on Drug Abuse

  60 shared

• João Sedoc

  60 shared

Labs

• Operations, Information and Decisions DepartmentPI

Awards & honors

• A 680,000-Person Megastudy of Nudges to Encourage Vaccinatio…
• Megastudies Improve the Impact of Applied Behavioural Scienc…

Similar researchers at University of Pennsylvania

• Angela Duckworthh-84
• Christian Terwieschh-51
• Lorin M. Hitth-49