Fiscal Year 2021
Brown University Financial Report

Making an Impact: The Department of Computer Science

Brown’s expanding Department of Computer Science is fortifying its research and teaching excellence in data science, artificial intelligence and socially responsible computing.

The department continues to propel innovation of the foundational and applied technologies that enable productive data-driven discovery.

Professor Eli Upfal, who serves as deputy director of Brown’s Data Science Initiative, works to make data exploration tools more useful and rigorous. As more people use analytics software to explore large datasets, the chances increase that random fluctuations in data will be mistaken for significant patterns. Such “false discoveries” could have dire consequences — particularly in areas such as healthcare or law enforcement. Upfal and colleagues have developed data exploration systems with advanced statistical safeguards that help users avoid false discoveries. The research served as the foundation for a commercially available data exploration product and a new startup company called Einblick.

Student working with robot
Brown’s computer science department continues to propel innovation of the foundational and applied technologies that enable productive data-driven discovery.

In other data science research, Assistant Professor Malte Schwarzkopf and colleagues recently unveiled a new data science framework that dramatically speeds up data exploration. Most data scientists use a computer language called Python for many data analysis tasks. However, industry-standard data science frameworks have trouble processing Python code, which leads to a “performance tax” when using this user-friendly language. The new framework developed by Schwarzkopf, called Tuplex, eliminates that performance tax, enabling the execution of Python queries up to 90 times faster than current systems. That speed increase could vastly improve productivity for data scientists.

Breakthroughs in Artificial Intelligence

Brown researchers are at the leading edge of a field that’s driving many of the recent advances in data and computational science over the past decade: artificial intelligence (AI). Ellie Pavlick, an assistant professor, is working with fellow faculty members Stefanie Tellex, Carsten Eikhoff and others on a groundbreaking approach to language processing. Current AI systems learn language by poring over vast amounts of text. That works well for learning to recognize words, but less well when it comes to actually understanding meaning and context.

Pavlick and her colleagues are working on ways of teaching computers to read that is similar to how children are taught: by letting the computer learn language by connecting words with objects and actions it observes in the real world. The project has earned the largest single funding award in the department’s history — a contract of over $6 million from the U.S. Defense Advanced Research Projects Agency (DARPA).

Also working in AI, Professor Michael Littman recently chaired an international panel of researchers tasked with producing a report on the state of the artificial intelligence field. The report is the second from an organization based at Stanford AI100, which aims to track AI development at regular intervals over the next century.

In this newest edition of the report, released in September 2021, Littman and his colleagues found that advances in computer vision, language processing and other areas mean that more people are interacting with AI on a daily basis than ever before — from getting movie recommendations to receiving medical diagnoses. With that success, however, comes a renewed urgency to understand and mitigate the risks and downsides of AI-driven systems, such as algorithmic discrimination or use of AI for deliberate deception. Computer scientists must work with experts in the social sciences and law to assure that the pitfalls of AI are minimized, the panel concluded.

Technology has created incredible opportunities and wealth, but it has a disparate impact on people. What has motivated me for the last few years is to try to think about what computer science would look like if it centered the needs of marginalized people. That’s the motivation for the Computing for the People project at Brown.

Seny Kamara Professor of Computer Science

Fortunately, Littman says, the computer science world is increasingly taking these concerns seriously, and Brown is at the forefront of that as well. In 2019, the computer science department launched an effort to infuse social responsibility into a wide variety of classes offered by the department. More than a dozen classes offered in the department now include social responsibility components, from addressing algorithmic bias in artificial intelligence classes to thinking about the securing of genomic data in computational biology. The program goes beyond simply adding an ethics module to classes. Rather, it encourages students and faculty to rethink the creation of computing systems in terms of power dynamics, societal benefits and unintended consequences.

In that same spirit, Professor Seny Kamara, an expert in cryptography, has entirely recalibrated his research agenda around a simple yet profound question: What would computer science look like if it centered the needs of marginalized people instead of those in power?

That question is opening new and important research avenues, including a recent collaboration with the office of Oregon U.S. Sen. Ron Wyden to explore methods of creating a secure and decentralized gun database. Gun violence in the U.S. disproportionately affects communities of color, and hundreds of women each year are shot and killed by domestic partners. The ability to trace guns used in crimes may help to prevent some of that violence, researchers say, but gun registries have faced political headwinds from those worried about data security and government control of such a database.

view through etched ICERM window
ICERM: In addition to the computational research happening in the computer science department, Brown’s National Science Foundation (NSF) Institute for Computational and Experimental Research in Mathematics (ICERM) is dedicated to bringing new computational methods to bear in areas of pure and applied mathematics. In 2020, ICERM received a $23.7 million grant renewal, the largest NSF grant in Brown’s history.

Kamara and his students developed a strategy for a fully encrypted and decentralized registry. The encryption scheme allows the database to be searched without being decrypted, which means people querying the database see only the records they’re looking for and nothing else. Meanwhile, the system places control of data in the hands of county-level officials rather than the federal government, meaning county officials have control over which queries are answered, and can even pull the county’s data offline entirely if they are not comfortable with how it is being used. Such a strategy could help to allay some political concerns and aid in making registry legislation a reality.

Kamara also teaches a class called Algorithms for the People, in which students study issues in technology policy — topics like algorithmic bias, inclusion, privacy, technology’s effect on immigration and war and others. Based on that study, they then engage in a technical project in which they build software to address a problem they’ve identified over the course of the semester.

The motivation of centering marginalized populations, and the willingness to engage with entities outside of computer science, serve as a template for Kamara’s latest venture. In cooperation with Brown’s Data Science Initiative, the Center for the Study of Race and Ethnicity in America and the Center for Computational Molecular Biology, Kamara and Suresh Venkatasubramanian, a new faculty member who joined the Department of Computer Science and the Data Science Initiative in Fall 2021, has launched a new effort called “Computing for the People” — a research center aimed at orienting the field of computing to benefit those in need rather than those in power.

The project is precisely the sort of socially responsible action that Littman and his colleagues identified as the next frontier in computing, and it promises to put Brown on the leading edge of a new paradigm for the field.

Pioneers in Data Science Education

A decade ago, computer scientists Kathi Fisler and Shriram Krishnamurthi spearheaded the development of a middle and high school mathematics curriculum called Bootstrap, which teaches kids the basics of algebra in the process of coding their own video games. The curriculum is now used in hundreds of classrooms across the country and was included as part of a national STEM education effort spearheaded by the White House.

In recent years, the team has harnessed the attributes that made Bootstrap Algebra so successful to create Bootstrap Data Science. The new program uses topics and datasets that students are already interested in — sports stats, local crime data or other datasets — and uses them to teach the basics and data exploration and discovery. Just a few years after the program’s creation, it’s already being used in dozens of classrooms around the country. The program is a step toward increasing data fluency in young students.

Bootstrap Data Science was introduced at a time when interest in computational and data science is skyrocketing. That surge of interest is particularly evident at Brown.

In 2010, Brown awarded undergraduate degrees to 48 computer science concentrators. In the 2020-21 academic year, 692 students declared a computer science concentration — one-sixth of the undergraduate students declaring a concentration. Hundreds more non-concentrators take one or more computer science class every semester.

In 2018, Brown launched the largest expansion of the Department of Computer Science in its 40-year history. The expansion will ultimately add 10 new tenure-track faculty and five lecturers to the department, bolstering research and teaching strength in data science, artificial intelligence, machine learning and beyond with an emphasis on socially responsible computing.