Award Abstract # 2023495
TRIPODS: Institute for Foundations of Data Science
NSF Org: |
DMS
Division Of Mathematical Sciences
|
Recipient: |
UNIVERSITY OF CALIFORNIA SANTA CRUZ
|
Initial Amendment Date: |
August 31, 2020 |
Latest Amendment Date: |
August 28, 2023 |
Award Number: |
2023495 |
Award Instrument: |
Continuing Grant |
Program Manager: |
Christopher Stark
cstark@nsf.gov
(703)292-4869
DMS
Division Of Mathematical Sciences
MPS
Direct For Mathematical & Physical Scien
|
Start Date: |
September 1, 2020 |
End Date: |
August 31, 2025 (Estimated) |
Total Intended Award Amount: |
$2,230,384.00 |
Total Awarded Amount to Date: |
$1,809,364.00 |
Funds Obligated to Date: |
FY 2020 = $421,827.00
FY 2021 = $447,493.00
FY 2022 = $468,731.00
FY 2023 = $471,313.00
|
History of Investigator: |
-
Lise
Getoor
(Principal Investigator)
getoor@soe.ucsc.edu
-
Abel
Rodriguez
(Co-Principal Investigator)
-
C Sesh
Seshadhri
(Co-Principal Investigator)
-
Daniele
Venturi
(Co-Principal Investigator)
|
Recipient Sponsored Research Office: |
University of California-Santa Cruz
1156 HIGH ST
SANTA CRUZ
CA
US
95064-1077
(831)459-5278
|
Sponsor Congressional District: |
19
|
Primary Place of Performance: |
UC Santa Cruz
1156 High St
Santa Cruz
CA
US
95064-1077
|
Primary Place of Performance Congressional District: |
19
|
Unique Entity Identifier (UEI): |
VXUFPE4MCZH5
|
Parent UEI: |
|
NSF Program(s): |
TRIPODS Transdisciplinary Rese, Special Projects - CCF
|
Primary Program Source: |
01002122DB NSF RESEARCH & RELATED ACTIVIT
01002223DB NSF RESEARCH & RELATED ACTIVIT
01002324DB NSF RESEARCH & RELATED ACTIVIT
01002021DB NSF RESEARCH & RELATED ACTIVIT
|
Program Reference Code(s): |
048Z,
075Z,
079Z
|
Program Element Code(s): |
041Y00,
287800
|
Award Agency Code: |
4900
|
Fund Agency Code: |
4900
|
Assistance Listing Number(s): |
47.049, 47.070
|
ABSTRACT
Data science is making an enormous impact on science and society, but its success is uncovering pressing new challenges that stand in the way of further progress. Outcomes and decisions arising from many machine learning processes are not robust to errors and corruption in the data; data science algorithms are yielding biased and unfair outcomes, as concerns about data privacy continue to mount; and machine learning systems suited to dynamic, interactive environments are less well developed than corresponding tools for static problems. Only by an appeal to the foundations of data science can we understand and address challenges such as these. Building on the work of three TRIPODS Phase I institutes, the new Institute for Foundations of Data Science (IFDS) brings together researchers from the Universities of Washington, Wisconsin-Madison, California-Santa Cruz, and Chicago, organized around the goal of tackling these critical issues. Members of IFDS have complementary strengths in the TRIPODS disciplines of mathematics, statistics, and theoretical computer science, and a proven record of collaborating to push theoretical boundaries by synthesizing knowledge and experience from diverse areas. Students and postdoctoral members of IFDS will be trained to be fluent in the languages of several disciplines, and able to bridge these communities and perform transdisciplinary research in the foundations of data science. In concert with its research agenda, IFDS will engage the data science community through workshops, summer schools, and hackathons. Its diverse leadership, committed to equity and inclusion, proposes extensive plans for outreach to traditionally underrepresented groups. Governance, management, and evaluation of the institute will build on the successful and efficient models developed during Phase I.
To address critical issues at the cutting edge of data science research, IFDS will organize its research around four core themes. The complexity theme will synthesize various notions of complexity from multiple disciplines to make breakthroughs in the analysis of optimization and sampling methods, develop tools for assessing the complexity of data models, and seek new methods with better complexity properties, to make complexity a more powerful tool for understanding and inventing algorithms in data science. The robustness theme considers data that contains errors or outliers, possibly due to an adversary, and will design methods for data analysis and prediction that are robust in the face of these errors. The theme on closed-loop data science tackles the issues of acquiring data in ways that reveal the information content of the data efficiently, using strategic and sequential policies that leverage information gathered already from past data. The theme on ethics and algorithms addresses issues of fairness and bias in machine learning, data privacy, and causality and interpretability. The four themes intersect in many ways, and most IFDS researchers will work in two or more of them. By making concerted progress on these fundamental fronts, IFDS will lower several of the barriers to better understanding of data science methodology and to its improved effectiveness and wider relevance to application areas. Additionally, IFDS will organize and host activities that engage the data science community at all levels of seniority. Annual workshops will focus on the critical issues identified above and others that are sure to arise over the next five years. Comprehensive plans for outreach and education will draw on previous experience of the Phase I institutes and leverage institutional resources at the four sites. Collaborations with domain science researchers in academia, national laboratories, and industry, so important in illuminating issues in the fundamentals of data science, will continue through the many channels available to IFDS members, including those established in the TRIPODS+X program. Relationships with other institutes at each IFDS site will further extend the impact of IFDS on domain sciences and applications.
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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(Showing: 1 - 42 of 42)
Balaram Behera and Edin Husic and Shweta Jain and Tim Roughgarden and C. Seshadhri
"FPT Algorithms for Finding Near-Cliques in c-Closed Graphs"
Innovations in Theoretical Computer Science (ITCS)
, 2022
Citation Details
Andrew Stolman and Caleb Levy and C. Seshadhri and Aneesh Sharma
"Classic Graph Structural Features Outperform Factorization-Based Graph Embedding Methods on Community Labeling"
SIAM Conference on Data Mining (SDM)
, 2022
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Dickens, Charles and Pryor, Connor and Augustine, Eriq and Abalak, Alon and Getoor, Lise
"Efficient Learning Losses for Deep Hinge-Loss Markov Random Fields"
Workshop on Tractable Probabilistic Modeling (TPM)
, 2022
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Augustine, Eriq and Pryor, Connor and Dickens, Charles and Pujara, Jay and Wang, William and Getoor, Lise
"Visual Sudoku Puzzle Classification: A Suite of Collective Neuro-Symbolic Tasks"
International Workshop on Neural-Symbolic Learning and Reasoning (NeSy)
, 2022
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Raab, Reilly and de Alfaro, Luca and Liu, Yang
"Conjugate Natural Selection"
arXivorg
, 2023
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Tang, Zeyu and Chen, Yatong and Liu, Yang and Zhang, Kun
"Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors"
International Conference on Learning Representations (ICLR)
, 2023
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Dickens, Charles and Miler, Alex and Getoor and Lise
"Online Collective Demand Forecasting for Bike Sharing Services"
Hawaii International Conference on System Sciences (HICSS)
, 2023
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Chen, Yatong and Raab, Reilly and Wang, Jialu and Liu, Yang
"Fairness Transferability Subject to Bounded Distribution Shift"
Neural Information Processing Systems (NeurIPS), 2022.
, 2022
Citation Details
Sosa, Juan and Rodríguez, Abel
"A latent space model for cognitive social structures data"
Social Networks
, v.65
, 2021
https://doi.org/10.1016/j.socnet.2020.12.002
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Yang Liu and Yatong Chen and Zeyu Tang and Kun Zhang
"Model Transferability With Responsive Decision Subjects"
New Frontiers in Adversarial Machine Learning
, 2022
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Jimmy Wu and Yatong Chen and Yang Liu
"Metric-Fair Classifier Derandomization"
International Conference on Machine Learning
, 2022
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Jialu Wang and Eric Xin Wang and Yang Liu
"Understanding Instance-Level Impact of Fairness Constraints"
International Conference on Machine Learning
, 2022
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Jialu Wang and Eric Xin Wang and Yang Liu
"Estimating Instance-dependent Label-noise Transition Matrix using a Deep Neural Network"
International Conference on Machine Learning
, 2022
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Andrew Estornell and Sanmay Das and Yang Liu and Yevgeniy Vorobeychik
"Unfairness Despite Awareness: Group-Fair Classification with Strategic Agents"
AAMAS 2022 Workshop on Learning with Strategic Agents
, 2022
Citation Details
Jialu Wang and Yang Liu and Xin Eric Wang
"Assessing Multilingual Fairness in Pre-trained Multimodal Representations"
Proceedings of Annual Meeting of Association for Computational Linguistics
, 2022
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Reilly Raab and Yang Liu
"Unintended Selection: Persistent Qualification Rate Disparities and Interventions"
Conference on Neural Information Processing Systems
, 2021
Citation Details
Yang Liu and Jialu Wang
"Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial"
Conference on Neural Information Processing Systems
, 2021
Citation Details
Yu, Xingchen and Rodríguez, Abel
"Spatial voting models in circular spaces: A case study of the U.S. House of Representatives"
The Annals of Applied Statistics
, v.15
, 2021
https://doi.org/10.1214/21-AOAS1454
Citation Details
Jialu Wang and Yang Liu and Xin Eric Wang
"Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search"
Empirical Methods in Natural Language Processing
, 2021
Citation Details
Paganin, Sally and Paciorek, Christopher J. and Wehrhahn, Claudia and Rodríguez, Abel and Rabe-Hesketh, Sophia and de Valpine, Perry
"Computational Strategies and Estimation Performance With Bayesian Semiparametric Item Response Theory Models"
Journal of Educational and Behavioral Statistics
, v.48
, 2022
https://doi.org/10.3102/10769986221136105
Citation Details
Sosa, Juan and Rodríguez, Abel
"A Bayesian approach for de-duplication in the presence of relational data"
Journal of Applied Statistics
, 2022
https://doi.org/10.1080/02664763.2022.2118678
Citation Details
Pryor, Connor and Dickens, Charles and Augustine, Eriq and Albalak, Alon and Wang, William and Getoor, Lise
"NeuPSL: Neural Probabilistic Soft Logic"
International Joint Conference on Artificial Intelligence (IJCAI)
, 2023
Citation Details
Pryor, Connor and Dickens, Charles and Getoor, Lise
"Deep Neuro-Symbolic Weight Learning in Neural Probabilistic Soft Logic"
ICML Workshop on Knowledge and Logical Reasoning in the Era of Data-Driven Learning (KLR)
, 2023
Citation Details
Augustine, Eriq and Lise Getoor
"Collective Grounding: Applying Database Techniques to Grounding Templated Models"
International Conference on Very Large Data Bases (VLDB)
, 2023
Citation Details
Guha, Sharmistha and Rodriguez, Abel
"Bayesian Regression With Undirected Network Predictors With an Application to Brain Connectome Data"
Journal of the American Statistical Association
, 2020
https://doi.org/10.1080/01621459.2020.1772079
Citation Details
Yufan Huang, C. Seshadhri
"Theoretical bounds on the network community profile from low-rank semi-definite programming"
International Conference on Machine Learning
, 2023
Citation Details
Hadley Black, Deeparnab Chakrabarty
"Directed Isoperimetric Theorems for Boolean Functions on the Hypergrid and an $~O(n\sqrt{d})$ Monotonicity Tester"
Conference record of the annual ACM Symposium on Theory of Computing
, 2023
Citation Details
Hadley Black, Deeparnab Chakrabarty
"A $d^{1/2+o(1)}$ Monotonicity Tester for Boolean Functions on $d$-Dimensional Hypergrids"
Annual Symposium on Foundations of Computer Science
, 2023
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Embar, Varun and Srinivasan, Sriram and Getoor, Lise
"Learning Explainable Templated Graphical Model"
Uncertainty in artificial intelligence
, 2022
Citation Details
Pryor, Connor and Dickens, Charles Andrew and Augustine, Eriq and Albalak and Alon and Wang and William Yang and Getoor, Lise
"NeuPSL: Neural Probabilistic Soft Logic"
ArXivorg
, 2022
Citation Details
Betancourt, Brenda and Sosa, Juan and Rodríguez, Abel
"A prior for record linkage based on allelic partitions"
Computational Statistics & Data Analysis
, v.172
, 2022
https://doi.org/10.1016/j.csda.2022.107474
Citation Details
Srinivasan, Sriram and Dickens, Charles and Augustine, Eriq and Farnadi, Golnoosh and Getoor, Lise
"A taxonomy of weight learning methods for statistical relational learning"
Machine Learning
, 2021
https://doi.org/10.1007/s10994-021-06069-5
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Embar, Varun and Srinivasan, Sriram and Getoor, Lise
"A comparison of statistical relational learning and graph neural networks for aggregate graph queries"
Machine Learning
, 2021
https://doi.org/10.1007/s10994-021-06007-5
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N. Pashanasangi, C.Seshadhri
"Faster and Generalized Temporal Triangle Counting, via Degeneracy Ordering"
SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2021
, 2021
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Charles Dickens, Connor Pryor
"Context-Aware Online Collective Inference for Templated Graphical Models"
Proceedings of the 38th International Conference on Machine Learning
, 2021
Citation Details
Charles Dickens, Eriq Augustine
"Negative Weights in Hinge-Loss Markov Random Fields."
Workshop on Tractable Probabilistic Modeling (TPM)
, 2021
Citation Details
Yatong Chen, Jialu Wang
"Linear Classifiers that Encourage Constructive Adaptation"
Algorithmic Recourse workshop at ICML'21
, 2021
Citation Details
Wang, Jialu and Liu, Yang and Levy, Caleb
"Fair Classification with Group-Dependent Label Noise"
2021 ACM Conference on Fairness, Accountability, and Transparency
, 2021
https://doi.org/10.1145/3442188.3445915
Citation Details
Suman K. Bera, Noujan Pashanasangi
"Near-Linear Time Homomorphism Counting in Bounded Degeneracy Graphs: The Barrier of Long Induced Cycles"
Symposium on Discrete Algorithms (SODA)
, 2021
Citation Details
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(Showing: 1 - 42 of 42)
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