The proposed NSF I/UCRC Center for Big Learning (CBL) consists of multi-disciplinary experts at the four founding universities that are geographically distributed across the country: University of Oregon (UO, West), Carnegie Mellon University (CMU, East), University of Missouri at Kansas City (UMKC, Central), and University of Florida (UF, South). The mission of this center is to explore research frontiers in the design of novel algorithms and developing efficient systems for deep learning research and its applications in the era of big data and big systems. Through a multi-site and multi-disciplinary consortium, the CBL center at the UO will focus on key applications of large-scale deep learning involving multi-modal media (i.e., text, image, and Q&A) in various domains (i.e., health, life science, IoT/mobile, and business) relying on strong support from the industry partners. The proposed multidisciplinary center will offer important opportunities for training new scientists and graduate students, and provide an environment for cross-disciplinary engagement.
The research team at the UO includes experts in data science, artificial intelligence, machine learning, high performance computing, IoT, health informatics, and bioinformatics. The CBL at the UO seeks to catalyze the fusion of expertise from academia, government, and industry stakeholders related to the rapid innovation in algorithms, systems, applications as well as education, and technology transfer into cutting-edge products and services with real-world relevance and significance. The UO site will explore several research projects related to health behavior modeling, activity recommendation, social network analysis, and privacy preserving by deploying various deep learning models. The planning activities will lead to a successful proposal for the establishment of the CBL center with a solid consortium across multiple campuses and a large number of industry partners. Our proposed meetings, forums, conferences, and planned training sessions will greatly promote and broaden the research and materialization of large-scale deep learning.
