![]() ![]() 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 proposed research will lead to new theoretical frameworks, models, mathematical tools and algorithms for analyzing high-dimensional data, inference and learning. The activities include topical special programs, postdoctoral fellows, co-mentored PhD students, workshops, coordinated graduate courses, visiting fellows, research meetings, and brainstorming sessions. 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. ![]() 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 thrusts of the institute will center around the foundations of machine learning, high-dimensional data analysis and inference, and data science and society. 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. Institute activities will include workshops for undergraduate students, high school teacher workshops, public lectures, and museum exhibit designs. 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. 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. 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. 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. TRIPODS Transdisciplinary Rese, HDR-Harnessing the Data Revolu Primary Place of Performance Congressional District: ![]() Varun Gupta (Co-Principal Investigator). ![]() Lek-Heng Lim (Co-Principal Investigator).Utku Candogan (Co-Principal Investigator).Chao Gao (Principal Investigator) Mladen Kolar (Co-Principal Investigator).Institute for Data, Econometrics, Algorithms and Learning (IDEAL) NSF Org:ĮCCS Div Of Electrical, Commun & Cyber SysĪnthony Kuh (703)292-4714 ECCS Div Of Electrical, Commun & Cyber Sys ENG Directorate For Engineering ![]()
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