Meet Bioengineering Sophomore and SNF Paideia Fellow Catherine Michelutti

Catherine Michelutti (BSE, BS ’23)

Rising Bioengineering Sophomore Catherine Michelluti (BSE 2023) has been featured on Penn’s SNF Paideia Program Instagram which discusses her diverse interests in machine learning in medicine, computer science, playing the violin and more. Catherine is a pre-med student who is pursuing an uncoordinated dual degree between the School of Engineering and Applied Science and the Wharton School of Business (BS in Economics 2023). She is also an incoming fellow in the SNF Paideia Program, which is supported by the Stavros Niarchos Foundation, is an interdisciplinary program which “encourage[s] the free exchange of ideas, civil and robust discussion of divergent views, and the integration of individual and community wellness, service, and citizenship through SNF Paideia designated courses, a fellows program, and campus events” (SNF Paideia website).

Read more about Catherine and other Fellows on the SNF Paideia Instagram.

Lyle Ungar: ‘Philadelphia Needs More Contact Tracers’

Lyle Ungar, Ph.D. (Photo: Eric Sucar)

In May, Lyle Ungar, Professor of Computer and Information Science and Angela Duckworth, Rosa Lee and Egbert Chang Professor in Penn Arts & Sciences and the Wharton School, contributed to a New York Times op-ed on how to slow the COVID-19 pandemic through a culture of mask-wearing.

As infections continue to rise, Ungar and Duckworth are following up with another op-ed. Writing in the Philadelphia Inquirer, they outline the need to rapidly ramp-up the city and state’s contact tracing capacity:

Guidelines from health officials suggest Pennsylvania needs about 4,000 contact tracers, including 2,000 for the Philadelphia metro area. Our state has been operating with fewer than 200.

Continue reading Ungar and Duckworth’s op-ed at the Philadelphia Inquirer.

Originally posted on the Penn Engineering blog. Media contact Evan Lerner.

Lyle Ungar is a Professor of Computer and Information Science (CIS) and a member of the Penn Bioengineering Graduate Group. Read more stories about the coronavirus pandemic written by Lyle Ungar here.

Language in Tweets Offers Insight Into Community-level Well-being

In a Q&A, researcher Lyle Ungar discusses why counties that frequently use words like ‘love’ aren’t necessarily happier, plus how techniques from this work led to a real-time COVID-19 wellness map.

By Michele W. Berger

Lyle Ungar, Ph.D. (Photo: Eric Sucar)

People in different areas across the United States reacted differently to the threat of COVID-19. Some imposed strict restrictions, closing down most businesses deemed nonessential; others remained partially open.

Such regional distinctions are relatively easy to quantify, with their effects generally understandable through the lens of economic health. What’s harder to grasp is the emotional satisfaction and happiness specific to each place, a notion ’s has been working on for more than five years.

In 2017, the group published the , a free, interactive tool that displays characteristics of well-being by county based on Census data and billions of tweets. Recently, WWBP partnered with ’s Center for Digital Health to create a , which reveals in real time how people across the country perceive COVID-19 and how it’s affecting their mental health.

That map falls squarely in line with a paper published this week in the by computer scientist , one of the principal investigators of the World Well-Being Project, and colleagues from Stanford University, Stony Brook University, the National University of Singapore, and the University of Melbourne.

By analyzing 1.5 billion tweets and controlling for common words like “love” or “good,” which frequently get used to connote a missing aspect of someone’s life rather than a part that’s fulfilled, the researchers found they could discern subjective well-being at the county level. “We have a long history of collecting people’s language and asking people who are happier or sadder what words they use on Facebook and on Twitter,” Ungar says. “Those are mostly individual-level models. Here, we’re looking at community-level models.”

In a conversation with Penn Today, Ungar describes the latest work, plus how it’s useful in the time of COVID-19 and social distancing.

Read Ungar’s Q&A at .

Dr. Lyle Ungar is a Professor of Computer and Information Science and a member of the Department of Bioengineering Graduate Group.