HDR TRIPODS: UC Davis TETRAPODS Institute of Data Science

HDR TRIPODS：加州大学戴维斯分校 TETRAPODS 数据科学研究所

• 批准号: 1934568
• 负责人: Naoki Saito
• 金额: $ 150万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934568&HistoricalAwards=false
• 关键词: HDR; TRIPODS; UC; Davis; TETRAPODS

The project at UC Davis will establish the UC Davis TETRAPODS Institute of Data Science (UCD4IDS), which will be composed of thirty-five researchers (four PIs and thirty-one senior personnel) coming from four departments (Computer Science, Electrical & Computer Engineering, Mathematics, and Statistics) and will break interdepartmental barriers and promote interdisciplinary research collaborations among faculty members, postdocs, and graduate students. The project will encourage innovative and robust research, and provide education and mentoring of graduate students and postdocs in data science. Students and postdocs engaged in this project will be trained to be the next generation of interdisciplinary data scientists: they will gain deep knowledge of some focused areas, and at the same time, broaden their perspectives in other diverse fields. The UCD4IDS will bring in the insights gained by the experience of the faculty members in the four primary departments as well as application fields such as neuroscience, medical and health sciences, and veterinary medicine. The UCD4IDS will organize: a) round-table discussions and breakout sessions after weekly seminars related to data science; b) quarterly colloquia on data science; and c) annual three-day workshops. The project will also coordinate and develop diverse courses at UC Davis, with graduate students involved in the project taking at least one course in each of the four departments. The PI team will also leverage local programs to recruit, support, and retain graduate students, postdocs, and new faculty members from underrepresented groups by matching them to appropriate mentors. For the dissemination of the research and educational results, the PI team plans to: 1) make colloquia and workshop talk slides, lecture notes, and codes available online, which will reach out to our current and future collaborators and the general public; and 2) organize mini-symposia and workshops on foundations of data science at targeted conferences.Research at the UCD4IDS will focus on three broad themes: 1) Fundamentals of machine learning directed toward biological and medical applications; 2) Optimization theory and algorithms for machine learning including numerical solvers for large-scale nontrivial learning problems; and 3) High-dimensional data analysis on graphs and networks. The algorithms and software tools to be developed will make a positive impact in solving practical data-analysis and machine-learning problems in diverse fields, e.g., computer science (analyzing friendship relations in social networks); electrical engineering (monitoring and controlling sensor networks); civil engineering (monitoring traffic flow on a road network); and in particular, biology and medicine (analyzing data measured on real neural networks, detecting changes in the brain structures due to diseases, imaging live biological cells for analyzing their growth, etc.). The technical goals of this project are: 1) geometric understanding of high-dimensional data, which may allow efficient (re)sampling from manifolds representing certain phenomena of interest and classifying subtle yet critical differences that often appear in biological and medical applications; 2) providing theoretical guarantees and efficient numerical algorithms for non-convex optimization, which is crucial to machine learning; and 3) deepening understanding of how local interactions between individual entities (e.g., neurons) lead to global coordination and decision making. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.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.

The project at UC Davis will establish the UC Davis TETRAPODS Institute of Data Science (UCD4IDS), which will be composed of thirty-five researchers (four PIs and thirty-one senior personnel) coming from four departments (Computer Science, Electrical & Computer Engineering, Mathematics, and Statistics) and will break interdepartmental barriers and promote interdisciplinary research collaborations among faculty members, postdocs,和研究生。该项目将鼓励创新和强大的研究，并提供研究生和数据科学博士后的教育和指导。从事该项目的学生和博士后将接受培训，成为下一代跨学科数据科学家：他们将对某些集中的领域获得深入的了解，同时，他们将在其他不同领域扩大他们的观点。 UCD4ID将带来四个主要部门的教师的经验以及神经科学，医学和健康科学以及兽医医学等应用领域所获得的见解。 UCD4ID将组织：a）与数据科学有关的每周一次研讨会后的圆桌讨论和突破性会议； b）数据科学季刊； c）年度为期三天的研讨会。该项目还将在加州大学戴维斯分校协调和开发多样化的课程，研究生参与该项目至少在四个部门中的每个部门中学习一门课程。 PI团队还将利用本地计划来招募，支持和保留研究生，博士后和来自代表性不足的团体的新教师，通过将他们与适当的导师匹配。为了传播研究和教育成果，PI团队计划：1）在线提供座谈会和讲习班谈话幻灯片，讲义和代码，这将与我们当前和未来的合作者以及公众联系；和2）在目标会议上组织有关数据科学基础的迷你群岛和讲习班。搜索UCD4ID的研究将重点关注三个广泛的主题：1）针对生物学和医疗应用的机器学习基础知识； 2）用于机器学习的优化理论和算法，包括用于大规模非平凡学习问题的数值求解器； 3）图和网络上的高维数据分析。要开发的算法和软件工具将对解决不同领域的实际数据分析和机器学习问题产生积极影响，例如计算机科学（分析社交网络中的友谊关系）；电气工程（监视和控制传感器网络）；土木工程（监视道路网络上的交通流）；特别是生物学和医学（分析在实际神经网络上测量的数据，检测疾病引起的大脑结构的变化，成像实时生物细胞以分析其生长等）。该项目的技术目标是：1）对高维数据的几何理解，这可能允许从代表某些感兴趣现象的歧管中进行有效的（重新）采样，并对经常出现在生物学和医疗应用中的细微差异进行分类； 2）为非凸优化提供理论保证和有效的数值算法，这对于机器学习至关重要； 3）加深对各个实体（例如神经元）之间的本地互动的理解，导致全球协调和决策。该项目是国家科学基金会利用数据革命（HDR）的大创意活动的一部分。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来支持。

项目成果

Uniqueness theorems for tomographic phase retrieval with few coded diffraction patterns

具有少量编码衍射图案的层析相位检索的唯一性定理

• DOI: 10.1088/1361-6420/ac77b0
• 发表时间: 2022
• 期刊: Inverse Problems
• 影响因子: 2.1
• 作者: Fannjiang, Albert

GRAND++: Graph Neural Diffusion with A Source Term

• 发表时间: 2022
• 作者: Matthew Thorpe;T. Nguyen;Hedi Xia;T. Strohmer;A. Bertozzi;S. Osher;Bao Wang

A One-Sample Decentralized Proximal Algorithm for Non-Convex Stochastic Composite Optimization

• DOI: 10.48550/arxiv.2302.09766
• 发表时间: 2023-02
• 期刊: ArXiv
• 作者: Tesi Xiao;Xuxing Chen;K. Balasubramanian;Saeed Ghadimi

Natural Graph Wavelet Packet Dictionaries

自然图小波包字典

• DOI: 10.1007/s00041-021-09832-3
• 发表时间: 2021
• 期刊: Journal of Fourier Analysis and Applications
• 影响因子: 1.2
• 作者: Cloninger, Alexander;Li, Haotian;Saito, Naoki
• 通讯作者: Saito, Naoki

Statistical Consistency for Change Point Detection and Community Estimation in Time-Evolving Dynamic Networks

• DOI: 10.1109/tsipn.2022.3156434
• 发表时间: 2022
• 期刊: IEEE Transactions on Signal and Information Processing over Networks
• 影响因子: 3.2
• 作者: Cong Xu;Thomas C.M. Lee