Making the Case for More Women in Computer Science

A new grant supports and expands Columbia’s proven track record for inclusivity.

 

There has been an explosion of interest from undergraduate women across Columbia and Barnard who are choosing to major in computer science (CS). In 2019, 39.5% of 1,268 CS declared majors were women (of 1,212 reporting gender across SEAS, Columbia College, General Studies, and Barnard). The CS major is the second largest at Columbia and still growing. This is the result of the centrality of computing to our lives today, as well as the Computer Science department’s initiatives over the past decade to encourage students to explore CS and to major or minor in CS.

In recognition of the high success rate in attracting and retaining women in Computer Science and the potential to do even more, Columbia is part of the first cohort to receive a grant from Northeastern University’s Center for Inclusive Computing. The Center partners with “nonprofit colleges and universities with large computing programs (200 graduates or more per year) to implement evidence-based practices that support the recruitment, enrollment, and graduation of historically underrepresented groups majoring in computing.”

The grant will fund several new joint initiatives as well as expand existing ones that support Columbia Engineering’s and Barnard’s overarching objectives to attract and retain women in introductory CS classes, continuing to improve the climate for diversity across the University’s CS community with the goal of reaching gender parity.

“The department’s programs run by our faculty and students have helped keep women and underrepresented minorities in our introductory classes,” said Julia Hirschberg, the Percy K. and Vida L. W. Hudson Professor of Computer Science. Hirschberg has been advocating for women in CS since she started at Bell Labs in the 1980s. “We are really delighted at how diverse our major has become.”

Columbia has come a long way to become one of the top schools in the US with a high percentage of women in CS. Ten years ago, the percentage of females majoring in computer science was just 8% percent. Columbia and Barnard have worked closely together to make CS more accessible to students with little to no CS background, which disproportionately includes women and underrepresented minorities. The School has been able to retain women in its introductory classes with considerable success through the following initiatives:

  • The development of COMS 1004 Lab, in which students with little programming experience in the introductory CS course get help practicing coding
  • The Emerging Scholars Program, in which students with little CS background attend weekly sessions to discuss CS as problem-solving
  • The Womxn in Computer Science group, a network for women undergraduates, graduate students, postdocs, faculty, and staff; the group promotes interaction on academic, social, and professional issues
  • The Application Development Initiative, which organizes hackathons and other events
  • The development of COMS 1002 Computing in Context, in which students planning other majors can get basic CS training to use on interdisciplinary problems, this course has had an unanticipated effect—leading to an increased number of students with no CS background deciding to major in CS

“My introduction to CS was welcoming, especially for students like me who do not have a CS background,” said Desu Imudia, a second-year student from Columbia College. As an African American woman, she says there is little representation in the School and sees the value of the labs for students to learn in groups and build confidence. Imudia plans to declare CS as her major and to continue working as a teaching assistant. She added, “Even though I know a lot now, I am no expert, so I think more labs in addition to courses will be really helpful for students.”

Barnard has gone from just one graduating CS major in 2013 to 33 in 2019—CS is now one of Barnard’s 10 most popular majors. In 2019, Barnard hired its first CS faculty as part of a comprehensive plan to expand its focus on CS and fully meet students’ needs, as well as to bring computing education to Barnard students outside the CS major, potentially attracting more of them to major or minor in CS or newly developed joint programs.

Rebecca Wright, Druckenmiller Professor of Computer Science at Barnard College and director of the Vagelos Computational Science Center, said, “This is an extremely exciting time for CS at Barnard. In addition to continuing to collaborate with Columbia, we also have the opportunity to explore new models and new kinds of computing curriculum.”

As part of the project, Barnard will develop a Computing Fellows program. Led by the new Vagelos Computational Science Center at Barnard, the Computing Fellows program will support faculty to incorporate computational projects into their courses and provide ongoing support to faculty and students in those courses. Specifically, a number of undergraduates each year will be hired and trained as computing fellows to work with faculty in departments across Barnard and their students.

At Columbia Engineering, the COMS 1004 Lab, Emerging Scholars Program, and Computing in Context programs will continue under the grant, with additional teaching assistants and PhD students expected to be hired. Hirschberg will lead the project and manage the Computing in Context, the 1004 Lab, and the ESP initiatives in collaboration with Adam Cannon and Paul Blaer, lecturers in discipline at Columbia Engineering.

At Barnard, Wright will spearhead the development and execution of the Computing Fellows program and will collaborate with the rest of the team across all of the initiatives.

“We are so grateful to Northeastern University’s  Center for Inclusive Computing for helping us to expand the programs which have so far proven so successful in retaining a diverse group of CS majors,” said Hirschberg.

Emoting While Remoting

Lydia Chilton and Eugene Wu developed a virtual design challenge for students to address the challenges of remote living.

Katy Gero Wins Best Paper Award at CHI 2020

Katy Gero, a third-year PhD student, wins a Best Paper Award from the ACM Computer-Human Interaction (CHI 2020).

Mental Models of AI Agents in a Cooperative Game Setting
Katy Ilonka Gero Columbia University, Zahra Ashktorab IBM Research AI , Casey Dugan IBM Research AI, Qian Pan IBM Research AI, James Johnson IBM Research AI, Werner Geyer IBM Research AI, Maria Ruiz IBM Watson, Sarah Miller IBM Watson, David R Millen IBM Watson, Murray Campbell IBM Research AI, Sadhana Kumaravel IBM Research AI, Wei Zhang IBM Research AI

As more and more forms of artificial intelligence (AI) become prevalent, it becomes increasingly important to understand how people develop mental models of these systems. In this work, the paper studies people’s mental models of AI in a cooperative word guessing game. The researchers wanted to know how people develop mental models of AI agents. 

Mental models refer to how a person “thinks” a system works. For instance, someone’s mental model of driving a car is that when they push on the gas pedal, more gas is pumped into the engine which makes the car go faster. That may not be how a car “actually” works, it’s just one view a person may have as a non-car expert. (The actual way it works is the gas pedal lets more air into the engine; a series of sensors then adjust the amount of gas to match the amount of air.)

“Mental models are known to be incomplete, unstable, and often wrong,” said Katy Gero, a third year PhD student and the study’s lead author. “Though that doesn’t mean they’re not useful!” 

To study the mental models of AI agents, the study’s participants played Passcode a word guessing game with an AI agent. The AI agent had a secret word, like “tomato”, and would give one word hints, like “salad” or “red”, to get the participant to guess the secret word. They learned that people’s mental models had three components:

– Global Behavior: How does the AI agent behave over time?

– Local Behavior: How does the AI agent decide on a single action?

– Knowledge Distribution: How much does the AI agent know about different parts of the world?

There was often a misunderstanding of local behavior. Sometimes the AI agent gave a hint that a participant didn’t understand. For instance, sometimes it gave the hint ‘hectic’ when the participant was meant to guess the word was ‘calm’. A participant might think that hint didn’t make any sense, like the AI agent made a mistake. In reality, the AI agent was giving an antonym hint. This is an example where a person had an incorrect mental model that they corrected over the course of several games.

The researchers conducted two studies – (1) a think-aloud study in which people play the game with an AI agent; through thematic analysis they identified features of the mental models developed by the participant and (2) a large-scale study where participants played the game with the AI agent online and used a post-game survey to probe their mental model

In the first study thematic analysis was used to develop a set of codes which describe the types of concerns participants had when playing with the AI agent. The most prevalent codes show what people think about most when playing with the AI agent: they worry about why it did something unexpected, they try to find patterns in its behavior, and they wonder what kinds of knowledge it has.

For the second study, they found that people who had the best understanding of how the AI agent behaved were able to play the game the best. However, as is expected with complex systems, no one had a perfect understanding of how the AI agent worked.

This work provides a new framework for modeling AI systems from a user-centered perspective: models should detail the global behavior, knowledge distribution, and local behavior of the AI agent. These studies also have implications for the design of AI systems that attempt to explain themselves to the user, especially AI agents that want to explain why they behave in certain ways.

CS Team Headed to ICPC World Championship

For the third consecutive year, a team from the CS department has advanced to the International Collegiate Programming Contest (ICPC) World Finals. The ICPC is the premiere collegiate programming competition that brings together students from around the globe to solve real-world problems, fostering collaboration, creativity, innovation, and the ability to perform under pressure.

Peilin Zhong, Hengjie Zhang, Zhenjia Xu, Runzhou Tao, and Chengyu Lin at the Greater New York Regionals

The team composed of first-year PhD students Hengjie Zhang, Zhenjia Xu, and Runzhou Tao first qualified at the Greater New York regional last October. At the North America Championship held in February, the team competed without their third team member, Zhenjia Xu, who was in China because of visa issues due to the coronavirus pandemic shutdown.

“We were worried about competing with just the two of us,” said Hengjie Zhang. Luckily, he and Runzhou Tao are good friends and knew how to work well with one another. They worked with their coaches, Chengyu Lin and Peilin Zhong, to prepare for the North American Championship in Atlanta, Georgia. Continued Zhang, “It was hard but Runzhou is good at math and I am good at programming so we managed to win!”

Runzhou Tao and Hengjie Zhang at the North American Championship

Even with just two members, team Columbia-ūnus secured a win and is one of 19 teams from North America to compete in the World Finals. Because of the coronavirus pandemic, this year’s World Finals will be held in Moscow, Russia in June 2021.