NSF Org:	CCF Division of Computing and Communication Foundations
Recipient:	UNIVERSITY OF TEXAS AT AUSTIN
Initial Amendment Date:	September 13, 2019
Latest Amendment Date:	September 19, 2021
Award Number:	1934932
Award Instrument:	Continuing Grant
Program Manager:	Anthony Kuh CCF  Division of Computing and Communication Foundations CSE  Directorate for Computer and Information Science and Engineering
Start Date:	October 1, 2019
End Date:	September 30, 2023 (Estimated)
Total Intended Award Amount:	$1,499,999.00
Total Awarded Amount to Date:	$1,499,999.00
Funds Obligated to Date:	FY 2019 = $500,882.00 FY 2020 = $515,627.00 FY 2021 = $483,490.00
History of Investigator:	Sujay Sanghavi (Principal Investigator) sanghavi@mail.utexas.edu Adam Klivans (Co-Principal Investigator) Rachel Ward (Co-Principal Investigator) Purnamrita Sarkar (Co-Principal Investigator)
Recipient Sponsored Research Office:	University of Texas at Austin 110 INNER CAMPUS DR AUSTIN TX  US  78712-1139 (512)471-6424
Sponsor Congressional District:	25
Primary Place of Performance:	University of Texas at Austin 2501 Speedway Austin TX  US  78712-1684
Primary Place of Performance Congressional District:	25
Unique Entity Identifier (UEI):	V6AFQPN18437
Parent UEI:
NSF Program(s):	TRIPODS Transdisciplinary Rese, HDR-Harnessing the Data Revolu
Primary Program Source:	01001920DB NSF RESEARCH & RELATED ACTIVIT 01002021DB NSF RESEARCH & RELATED ACTIVIT 01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	047Z, 062Z
Program Element Code(s):	041Y00, 099Y00
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

This project establishes a new institute on the Foundations of Data Science at the University of Texas at Austin. The Institute will be a collaboration between eight PIs in the electrical engineering, computer science, mathematics and statistics departments at UT Austin, as well as postdocs and graduate students from the new programs this Institute establishes. It will form a central hub for theoretical research into machine learning and data science by looking at foundational approaches to analysis and design. This is necessary to devise novel complex and sophisticated machine-learning and artificial-intelligence theory and algorithms that can handle the accelerating scale of received data and the faster computational speeds of computers. The algorithms and systems will interpret and predict behavior from data and the environment with the goal towards better design methods performed in a principled way. The research will also open avenues for applications in fields such as autonomous vehicles and personalized medicine. The research and education will be integrated to create new inter-departmental postdoctoral and graduate research programs, establish a unified degree and portfolio program in data science at UT Austin, run dedicated seminar series and hold workshops, and partner with industry as well as domain experts in the sciences. It will significantly expand, via funded initiatives, the PIs' ongoing efforts to expand participation of under-represented groups in this important field.

Research focuses on fundamental mathematical theory of machine learning and optimization, including neural networks, robustness, and graphs. The research is organized around three themes: (a) developing an algorithmic theory for deep learning, with new and provable methods for training, doing hyper parameter optimization and developing confidence measures, (b) making machine learning robust to both adversarial and incidental errors in data, and (c) devising new methods for statistical inference using graph algorithms, including fast estimation of graph statistics, and their use in biological and vision applications.

This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.

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.

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This award funded a 3-year institute focused on foundational aspects of data science at the University of Texas at Austin (UT Austin); it brought together four PIs and four senior personnel from four different departments at UT: Electrical and Computer Engineering, Computer Science, Statistics and Mathematics.  While UT Austin had research expertise in foundational aspects of data science and machine learning spread across several departments, there did not exist any formal mechanism bringing the relevant faculty together. This grant filled that role; the awarding of this grant seeded several subsequent and larger scale initiatives that together have changed the landscape of data science and machine learning at UT Austin.  These include  first AI Institute focused on foundations (IFML) which in turn led to the establishment of the interdisciplinary Machine Learning Lab at UT, a larger Phase-2 Tripods grant (ENCORE) where we joined forces with four other universities, and an Amazon Science Hub at UT Austin which provided both significant research support and scientific insight from industry to UT. 
The research areas covered by this award include optimization, online learning, reasoning on graphs, and deep learning. This award also funded several graduate students and postdocs, several of whom went on to tenure track  faculty positions. This grant also supported the development of both basic and topics courses in all aspects of data science, machine learning and AI.

Last Modified: 02/18/2024
Modified by: Sujay Sanghavi

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