UChicago Co-Leads $10 Million NSF Institute on Foundations of Data Science
The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL), a multi-institutional collaboration of Chicago universities studying the foundations and applications of data science, was expanded and renewed for five years through a $10 million grant from the National Science Foundation.
The Phase II “Harnessing the Data Revolution” grant allows IDEAL to expand from its founding trio of partners – the University of Chicago, Toyota Technological Institute, and Northwestern University – to include the University of Illinois at Chicago and the Illinois Institute of Technology. The new phase of the consortium, originally founded in 2019, creates a Chicago-wide network of researchers, talks, and courses that explore the frontiers of data science, related fields such as machine learning and security, and their significance for fields such as law, business, and engineering.
“The main, outstanding feature of IDEAL is that it is a collaboration between universities only based in Chicago, within a driving distance of one hour at most,” said Chao Gao, assistant professor of Statistics at the University of Chicago and IDEAL site director for UChicago. “Because of this proximity, we can run non-stop activities: events, workshops, and courses will happen locally and physically throughout the year. It will be a tremendous opportunity for students and faculty in the region, and facilitates our outreach to the local industry and educational communities.”
The consortium includes more than 60 researchers in computer science, statistics, mathematics and electrical engineering, plus application domains such as economics, law, and social sciences. Main research topics of interest include the foundations of machine learning, high-dimensional data analysis and inference, and data science and society, including emerging issues of reliability, fairness, privacy and interpretability.
In addition to Gao, UChicago co-principal investigators for IDEAL include Lek-Heng Lim of the Department of Statistics, and Mladen Kolar, Varun Gupta, and Ozan Candogan from the Booth School of Business. Additional UChicago faculty include Cong Ma, Yi Sun and Victor Veitch from Statistics, Tengyuan Liang and Veronika Rockova from Chicago Booth, and Aloni Cohen from the Department of Computer Science.
Broadly, IDEAL will also interface with other UChicago units focused on data science and related fields, such as the Data Science Institute, the Institute for the Foundations of Data Science, the Departments of Statistics and Computer Science, and the Committee on Computational and Applied Mathematics.
IDEAL will also work with Google’s learning theory team and tech companies both in Chicago and beyond, creating new industry/academic partnerships around data science.
“Our industry affiliates include researchers at Google, Adobe, Nokia and several Chicago-area companies through the P33 Chicago initiative, which aims to develop the Chicago tech ecosystem,” Gao said. “Through this collaboration, we can access interesting data sets and better engage the Chicago-area tech industry on research projects.”
Many of IDEAL’s activities are organized around “special quarters” that offer speaker series, courses, and brainstorming research workshops on a main topic of interest. Previous themes have included “Data Science and Law,” “Inference and Data Science on Networks,” and “Robustness in High-dimensional Statistics and Machine Learning.” The first phase of IDEAL will end with a Fall 2022 special quarter on “Incentives in Shared Data Infrastructure,” followed by a winter special quarter, the first in Phase II, on “Machine Learning and Logic.” It will include kickoff and insights events, postdoctoral opportunities, contacts between faculty and various partners, and a brainstorming session, Gao said.
The institute will also conduct outreach to younger students, encouraging them to pursue careers in data science and other STEM disciplines. Through partnerships with Math Circles of Chicago, the Museum of Science and Industry, and other organizations, the group will organize a yearly workshop for math and science teachers, lectures at Chicago high schools, and museum programming for the general public.
“This will involve collaboration on hands-on museum exhibits demonstrating how algorithms can derive useful insights from data and participating in career-day events at the museum, encouraging attendees to get involved in data science,” Gao said. “These will build new pathways for undergraduate students, high school students, and the broader public from diverse and underrepresented backgrounds to increase participation and engagement with scientific fields related to data science.”
For more on IDEAL, see the Northwestern University announcement.