Collaborative research: cise-MSI: DP: III: information integration and association pattern discovery in precision phenomics

This $189,745 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program supports research at West Virginia State University (WVSU) to develop machine learning methods for information integration and association pattern discovery across complex, heterogeneous datasets in precision agriculture and phenomics applications.

The key products and services to be delivered include: (1) new methods for association pattern discovery in loosely-coupled domains, (2) prototype development and evaluation of the new learning models, and (3) capacity building through research training and curriculum development in machine learning and data analytics for undergraduate and graduate students at WVSU, an HBCU. The project will apply the developed methods to two case studies: (1) the genotype-to-phenotype problem in precision agriculture for habanero peppers, and (2) predicting fruit taste and flavor based on phenotype data. No sub-awards are planned as part of this award.

Abstract

This project addresses a general class of machine learning problems involving loosely-coupled heterogeneous domains. Such problems have significant applications in various fields, from precision agriculture (plant genotype-to-phenotype) to precision medicine (genotype-disease associations) to ecology (soil moisture-climate interactions). Beyond the specific challenges in artificial intelligence (AI) and machine learning, an equally important and related challenge is diversifying the talent pool with the technical capability to these problems. The project has three specific goals: (1) develop methods for association pattern discovery in loosely-coupled domains; (2) perform information integration, prototype development, and evaluation of the methods developed; (3) capacity building in research and education in machine learning/artificial intelligence and their applications in precision agriculture at west virginia state university (wvsu). The project includes two case studies. The first is on the genotype-to-phenotype problem in precision agriculture, specifically on fruit phenotypes in habanero pepper, an important fruit crop. The second will apply proposed learning models to the problem of mapping fruit phenotype to sensory perception, which is needed to predict taste and flavor in fruit crops. The project also involves capacity building efforts to improve research capability for faculty at wvsu, while providing research training for students. Educational activities include curriculum development in data literacy and research training in machine learning and data analytics for undergraduate and graduate students. Further, the project will educate faculty, students, and the public within the local region on innovations and trends in artificial intelligence (AI) and its applications. The project includes deliberate plans to involve minority students and faculty from wvsu, an hbcu (historically black college and university) with collaborators at west virginia university (WVU), an R1 institution. By so doing, the project will improve data science literacy across the entire university at wvsu and to develop more of the students? Skills in data-driven analysis. Identifying reliable patterns of association between seemingly disparate domains remains a core challenge at the foundation of data science and machine learning. Doing this when the domains involved are each complex, heterogeneous, and have enormous datasets is an even more arduous task. The project identifies a class of problems involving loosely-coupled heterogeneous domains, and propose an innovative framework for performing large-scale association discoveries across such complex domains. Our framework is built on a strong theoretical foundation, namely, the information bottleneck rooted in information theory. The project makes new theoretical contributions to the foundation of data science and machine learning by developing new methods for learning hidden associations across two heterogeneous domains, and for integrating information across such domains. The project also develops novel applications of the new learning methods to two specific problems in precision agriculture and crop phenomics. The proposed method of discovering association patterns under the identified class of loosely-couple domains can easily be modified for any member of this identified class. This class is quite general, and includes problems in various fields of human endeavor, from agriculture to precision medicine to ecology. This project is jointly funded by the cise MSI research expansion program and the established program to stimulate competitive research (epscor). 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.- subawards are not planned for this award.