
NSF Org: |
DMS Division Of Mathematical Sciences |
Recipient: |
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Initial Amendment Date: | May 10, 2017 |
Latest Amendment Date: | May 10, 2017 |
Award Number: | 1712580 |
Award Instrument: | Standard Grant |
Program Manager: |
Gabor Szekely
DMS Division Of Mathematical Sciences MPS Directorate for Mathematical and Physical Sciences |
Start Date: | July 1, 2017 |
End Date: | June 30, 2021 (Estimated) |
Total Intended Award Amount: | $124,963.00 |
Total Awarded Amount to Date: | $124,963.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
1960 KENNY RD COLUMBUS OH US 43210-1016 (614)688-8735 |
Sponsor Congressional District: |
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Primary Place of Performance: |
1928 Neil Avenue, Cockins Hall Columbus OH US 43210-1016 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | STATISTICS |
Primary Program Source: |
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Program Reference Code(s): | |
Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.049 |
ABSTRACT
A multimodal paradigm has become increasingly important given today's explosive growth of information, which often arises from, for instance, automatic image categorization and personalized prediction. Multimodal data has a wide spectrum of applications in medical diagnostics, social networking, multimedia, information filtering, personalized advertising, consumers' recommendations, virtually in any electronic commerce and entertainment platform. This research aims to develop statistical theory, methods, and computational tools to integrate multimodal data for prediction and description. The development will lead to the higher accuracy of learning, which will ultimately enhance information storage, sorting and filtering. Moreover, the research project has an education component to train graduate students in emerging areas. The research products will be disseminated through publications and presentations.
The proposed research aims to develop statistical techniques to utilize conditional dependence structures for integrating multimodal data. It will proceed in the areas of collaborative learning and personalized prediction. In each area, regression, classification, and ranking will be performed collaboratively based on pairwise conditional dependencies between the response components, modeled by a directed graph or an undirected graph. Special efforts will be devoted to the joint learning of data of multiple modalities and extraction of latent structures with an adjustment for covariates. Target applications include image categorization and recommender systems, where the proposed techniques will be applied to understand the content of an image and to predict personalized preference over a large number of items. Furthermore, The research will develop computational tools and design methods that have desirable statistical properties.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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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.
The big-data era leads to the creation of data science at the interface of computer science and statistics. While big data demand scalable methods and their associated theories, it also requires extraction and integration of the knowledge and information across heterogeneous data of different types, particularly multimodal data involving images, audios, and texts. The research project sheds some new insight into leveraging multivariate dependence structures to integrate multimodal data to improve our capability of analyzing this type of complex data. On this ground, we develop statistical theory and methodology utilizing the conditional dependence structure, that is, label dependence in classification and pairwise component dependence in regression. As a result of our effort, we can generalize our personalized prediction to unseen and novel examples.
This collaborative project has incorporated research results in teaching to create an exciting opportunity for students to learn state-of-art technology and emerging research areas. During the project period, the principal investigators have mentored 3 M.S. students and 10 Ph.D. students. Concerning research products, the project has produced about 25 journal publications and several publicly available software packages for end-users. The principal investigators have also delivered more than 20 invited presentations at national and international conferences, workshops, and institutional colloquia.
Last Modified: 08/18/2021
Modified by: Yunzhang Zhu
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