University of Oregon Planning Proposal: I/UCRC for Big Learning

拟议的NSF I/UCRC大学习中心（CBL）由四所创始大学的多学科专家组成，这些大学在地理上分布在全国各地：俄勒冈州大学（UO，西部），卡内基梅隆大学（CMU，东部），密苏里州大学堪萨斯城（UMKC，中部）和佛罗里达大学（UF，南部）。 该中心的使命是在大数据和大系统时代探索深度学习研究及其应用的新算法设计和开发高效系统的研究前沿。通过多站点和多学科联盟，UO的CBL中心将专注于涉及多模态媒体的大规模深度学习的关键应用（即，文本、图像和QA）在各种领域（即，健康、生命科学、物联网/移动的和商业），依靠行业合作伙伴的大力支持。拟建的多学科中心将为培养新科学家和研究生提供重要机会，并为跨学科参与提供环境。UO的研究团队包括数据科学，人工智能，机器学习，高性能计算，物联网，健康信息学和生物信息学方面的专家。UO的CBL旨在促进学术界，政府和行业利益相关者的专业知识的融合，这些专业知识与算法，系统，应用程序以及教育的快速创新以及技术转移到具有现实意义的尖端产品和服务中。UO网站将通过部署各种深度学习模型来探索与健康行为建模、活动推荐、社交网络分析和隐私保护相关的几个研究项目。规划活动将导致建立CBL中心的成功建议，该中心拥有多个校区和大量行业合作伙伴的坚实联盟。我们提议的会议、论坛、大会和计划中的培训课程将极大地促进和拓宽大规模深度学习的研究和实现。

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.