‘I Look Like an Engineer’

Penn Engineering students (clockwise) Nyasha Zimunhu, Fahmida Lubna, Celestina Saven, Sanjana Hemdev, Sabrina Green and Sydney Kariuki all participated in the “I Look Like an Engineer” campaign, locally organized by AWE.

Penn Engineering’s Advancing Women in Engineering (AWE) program, dedicated to recruiting, retaining and promoting all female-identified students in the School, participated in the “I Look Like an Engineer” social media movement for the third year in a row. The movement, aimed at promoting diversity around underrepresented groups like women and people of color, was started by software developer Isis Anchalee in 2015.

Francesca Cimino

Francesca Cimino, member of AWE and a rising senior in the Department of Bioengineering, has always been passionate about changing the stereotypes and breaking down the barriers that prevent engineers of diverse backgrounds from thriving. She wanted to continue AWE’s tradition of participating in the movement to showcase the diversity already present within the field and prove that there is no single characteristic that defines an engineer.

At the conclusion of the campaign, Cimino responded to questions about the importance of diversity and what a more equal world in engineering looks like.

Why did you decide to get involved with AWE?

I applied to be a part of AWE’s Student Advisory Board during the spring semester of my freshman year. Being on the board was very enticing to me because I was looking to make connections with more women engineers at the time. I wanted to create my own community of women engineers while also wanting to help foster a community for all. AWE’s message and goals really resonated with me as well, so I knew it would be a perfect fit.

How important has mentorship from other female engineers been for you?

Being able to interact and learn from women who have experience in the industries I am most interested in has been very valuable to me. It has been inspiring to learn about their stories and the fact that I can relate to many of them has definitely allowed me to become more confident as I get closer to starting my career. Mentorship is something AWE really values and the board has worked to develop a mentoring network for women engineers, which I really admire.

Read the full Q&A in Penn Engineering Today.

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.