Award Abstract # 2217023
Institute for Data, Econometrics, Algorithms and Learning (IDEAL)

NSF Org: ECCS
Division of Electrical, Communications and Cyber Systems
Recipient: UNIVERSITY OF ILLINOIS
Initial Amendment Date: July 28, 2022
Latest Amendment Date: May 14, 2025
Award Number: 2217023
Award Instrument: Continuing Grant
Program Manager: Eyad Abed
eabed@nsf.gov
 (703)292-2303
ECCS
 Division of Electrical, Communications and Cyber Systems
ENG
 Directorate for Engineering
Start Date: September 1, 2022
End Date: August 31, 2027 (Estimated)
Total Intended Award Amount: $3,180,000.00
Total Awarded Amount to Date: $2,703,000.00
Funds Obligated to Date: FY 2022 = $1,272,000.00
FY 2023 = $1,272,000.00

FY 2024 = $159,000.00
History of Investigator:
  • Lev Reyzin (Principal Investigator)
    lreyzin@uic.edu
  • Natasha Devroye (Co-Principal Investigator)
  • Mesrob Ohannessian (Co-Principal Investigator)
  • Elena Zheleva (Co-Principal Investigator)
  • Ian Kash (Co-Principal Investigator)
  • Yichao Wu (Former Co-Principal Investigator)
  • William Perkins (Former Co-Principal Investigator)
Recipient Sponsored Research Office: University of Illinois at Chicago
809 S MARSHFIELD AVE M/C 551
CHICAGO
IL  US  60612-4305
(312)996-2862
Sponsor Congressional District: 07
Primary Place of Performance: University of Illinois at Chicago
851 S. Morgan
Chicago
IL  US  60607-7054
Primary Place of Performance
Congressional District:
07
Unique Entity Identifier (UEI): W8XEAJDKMXH3
Parent UEI:
NSF Program(s): TRIPODS Transdisciplinary Rese,
HDR-Harnessing the Data Revolu
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
01002324DB NSF RESEARCH & RELATED ACTIVIT

01002425DB NSF RESEARCH & RELATED ACTIVIT

01002526DB NSF RESEARCH & RELATED ACTIVIT

01002627DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 048Z, 062Z, 075Z, 079Z, 9102
Program Element Code(s): 041Y00, 099Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041, 47.049, 47.070

ABSTRACT

The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL) will consolidate and amplify research devoted to the foundations of data science across all the major research-focused educational institutions in the greater Chicago area: the University of Illinois at Chicago, Northwestern University, the Toyota Technological Institute at Chicago, the University of Chicago, and the Illinois Institute of Technology. This transdisciplinary institute involves over 50 researchers working on key aspects of the foundations of data science across computer science, electrical engineering, mathematics, statistics, and several related fields like economics, operations research, and law, and they are complemented by members of Google?s learning theory team. Its research goals range from the core foundations of data science to its interfaces with other disciplines: 1) tackling important challenges related to foundations of machine learning and optimization, 2) addressing statistical, algorithmic and mathematical challenges in dealing with high-dimensional data, and 3) exploring the foundations of aspects of data science that interact with society. The institute will foster strong connections with the community and local high schools, broaden participation in data science locally and nationally, and build lasting research and educational infrastructure through its activities. Institute activities will include workshops for undergraduate students, high school teacher workshops, public lectures, and museum exhibit designs. 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.

The research thrusts of the institute will center around the foundations of machine learning, high-dimensional data analysis and inference, and data science and society. Specific topics include foundations of deep learning, reinforcement learning, machine learning and logic, network inference, high-dimensional data analysis, trustworthiness & reliability, fairness, and data science with strategic agents. The research activities are designed to facilitate collaboration between the different disciplines and across the five Chicago-area institutions, and they build on the extensive experience from previous efforts of the participating universities. The activities include topical special programs, postdoctoral fellows, co-mentored PhD students, workshops, coordinated graduate courses, visiting fellows, research meetings, and brainstorming sessions. The proposed research will lead to new theoretical frameworks, models, mathematical tools and algorithms for analyzing high-dimensional data, inference and learning. Successful outcomes will also lead to a better understanding of the foundations of data science and machine learning in both strategic and non-strategic environments ? including emerging concerns like reliability, fairness, privacy and interpretability as data science interacts with society in various ways. The institute will also have broader impacts of strengthening research and educational infrastructure, developing human resources, broadening participation from underrepresented groups, and by connecting theory to science and industry. The institute will organize activities to engage the community and a diverse group of students at all levels, including introductory workshops for undergraduate research participants, high school student and teacher outreach (through a partnership with the Math Circles of Chicago), and public lectures as part of both our research program and a partnership with the Museum of Science and Industry. The Chicago public institutions that we engage serve a very diverse population, so the outreach, recruitment, and training activities will broaden participation from underrepresented groups. Finally, the institute will have direct engagement with applications and industry through its activities involving Google, other industry partners in the broader Chicago area, and applied data science institutes.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Devroye, Natasha and Mulgund, Abhijeet and Shekhar, Raj and Turan, Gyorgy and Zefran, Milos and Zhou, Yingyao "Interpreting Training Aspects of Deep-Learned Error-Correcting Codes" International Symposium on Information Theory , 2023 Citation Details
Dumoulin, Vincent and Rao, Wenjing and Devroye, Natasha "On the Response Entropy of APUFs" Journal of Hardware and Systems Security , v.8 , 2024 https://doi.org/10.1007/s41635-024-00151-9 Citation Details
Ovaisi, Zohreh and Saadatpanah, Parsa and Sefati, Shahin and Ohannessian, Mesrob and Zheleva, Elena "Fairness of Interaction in Ranking under Position, Selection, and Trust Bias" ACM Transactions on Recommender Systems , 2024 https://doi.org/10.1145/3652864 Citation Details
Zhou, Y and Devroye, N and Turan, Gy and Zefran, M "Higher-order Interpretations of Deepcode, a Learned Feedback Code" , 2024 https://doi.org/10.1109/Allerton63246.2024.10735282 Citation Details
Zhou, Y and Devroye, N and Turán, Gy and efran, M "Interpreting Deepcode, a Learned Feedback Code" , 2024 https://doi.org/10.1109/ISIT57864.2024.10619390 Citation Details

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