MRI Acquisition of a High Performance Large Memory Computing Cluster for Large Scale Data-Driven Research

用于大规模数据驱动研究的高性能大内存计算集群的 MRI 采集

基本信息

批准号：
1919452
负责人：
Peter Ramadge
金额：
$ 49.9万
依托单位：
Princeton University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1919452&HistoricalAwards=false
关键词：
MRI Acquisition Performance Large Memory

项目摘要

This project will acquire a state-of-the-art High Performance Computing (HPC) cluster to support large scale, data-driven research. The instrument will support a variety of projects from computer science, electrical engineering, ecology, evolutionary biology, neuroscience and genomics. In neuroscience, the cluster will allow the use of advanced statistical techniques at scale to identify and connect anatomical and functional brain-imaging features of diseased and healthy subjects with specific underlying genetic profiles. In computer science, using machine learning algorithms deployed on the instrument, researchers will to seek new ways to protect the security and privacy of users in large-scale networked systems. Finally, the cluster will also enable research that will improve our understanding of evolutionary history and the molecular complexities of traits through the analysis of multi-animal, large-scale genomic datasets. In addition, through short courses and multiday boot-camps, the instrument will provide valuable opportunities for training postdoctoral fellows, graduate students, and advanced undergraduates in large-scale computational data science. The instrument will also be a valuable asset for certificate programs in statistics and machine learning (one for undergraduate students, the other for graduate students) and for a certificate program in computational science, all of which will support broadening participation of groups underrepresented in STEM. The research and training enabled by the instrument is expected to help improve our understanding of human health and well-being, help create new knowledge that will aid economic competitiveness, and help maintain the country's leadership in science and engineering. The computing cluster will be formed of by nodes with very large memory. The system complements the institution's investments in research cyberinfrastructure and will be managed by the Princeton Institute for Computational Science and Engineering (PICSciE) and the Office of Information Technology (OIT). The instrument would initially be used by five research groups, part of the Center for Statistics and Machine Learning (CSML), which will leverage existing programs and partnerships to increase participation in data science. The initial five specific projects are united under a common theme: machine learning will be used for analyzing big data sets that may not be easily broken into smaller pieces for processing. Specifically, they will examine the following: 1) the use of probabilistic models for large-scale scientific analysis and de novo design in applications areas such as mechanical metamaterials and mixed-signal circuit development; 2) statistical machine learning in genomics, biomedicine, and health biostatistics including the analysis of hospital records to aid doctors in taking early action to improve patient outcomes, the heritability of neuropsychiatric diseases and drug responses, and statistical and experimental examination of cardiovascular disease risk; 3) security and privacy challenges in networked systems using machine learning techniques to detect and isolate attackers in networked systems such as social media; 4) large-scale machine learning for neuroscience such as joint analysis of many large-scale, multi-subject fMRI datasets where the size and number of the datasets; 5) evolutionary genomic and epigenome analyses through collection and analysis of large datasets to investigate the evolutionary history and molecular complexities of traits. Collectively, these research groups are composed of forty graduate students, ten postdocs, and include, on average, thirteen undergrad research projects per year. The instrument will also be used by other researchers engaged in large-scale, data-driven research across a wide variety of disciplines. Hence both the capacity and the capability aspects of the proposed instrument will be highly utilized and will enable the continued advancement of research at the University.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.

该项目将获得最先进的高性能计算（HPC）集群，以支持大规模的数据驱动研究。该仪器将支持计算机科学，电气工程，生态学，进化生物学，神经科学和基因组学的各种项目。在神经科学中，该集群将允许使用高级统计技术来识别和连接患病和健康受试者的解剖学和功能性脑成像特征，并与特定的基本遗传特征。在计算机科学方面，使用在仪器上部署的机器学习算法，研究人员将寻求新的方法来保护大型网络系统中用户的安全性和隐私。最后，群集还将通过分析多动物大规模的基因组数据集来提高我们对进化史和性状的分子复杂性的研究。此外，通过简短的课程和多日启动训练营，该仪器将为培训大规模计算数据科学的博士后研究员，研究生和高级本科生提供宝贵的机会。该仪器还将是统计和机器学习证书计划的宝贵资产（一个针对本科生，另一个针对研究生）和计算科学证书计划，所有这些都将支持扩大STEM中代表性不足的小组的参与。该工具支持的研究和培训将有助于提高我们对人类健康和福祉的理解，帮助创建有助于经济竞争力的新知识，并帮助维持该国在科学和工程方面的领导。计算群集将由具有非常大内存的节点组成。该系统补充了该机构在研究网络基础设施上的投资，将由普林斯顿计算科学与工程研究所（PICSCIE）和信息技术办公室（OIT）管理。该工具最初将由五个研究小组（统计与机器学习中心（CSML）的一部分）使用，该小组将利用现有计划和合作伙伴关系来增加参与数据科学。最初的五个特定项目在一个共同的主题下团结在一起：机器学习将用于分析可能不容易将其分成较小的零件进行处理的大数据集。具体而言，他们将研究以下内容：1）在应用机械超材料和混合信号电路开发等应用领域中使用概率模型进行大规模的科学分析和从头设计； 2）基因组学，生物医学和健康生物统计学中的统计机器学习，包括对医院记录的分析，以帮助医生采取早期行动以改善患者的结局，神经精神疾病和药物反应的遗传力，以及对心血管疾病风险的统计和实验检查； 3）使用机器学习技术在网络系统中的安全性和隐私挑战来检测和隔离社交媒体等网络系统中的攻击者； 4）用于神经科学的大规模机器学习，例如许多大型，多主体FMRI数据集的联合分析，其中数据集的大小和数量； 5）通过收集和分析大型数据集的进化基因组和表观基因组分析，以研究特征的进化史和分子复杂性。总的来说，这些研究小组由40名研究生，十个博士后组成，平均每年包括13个本科研究项目。该仪器还将被其他研究人员使用的其他研究人员在各种各样的学科上进行。因此，拟议工具的能力和能力方面都将得到高度利用，并将能够在大学继续进行研究。该奖项反映了NSF的法定任务，并通过基金会的智力优点和更广泛的评估被视为值得支持的支持。影响审查标准。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Training Discrete Deep Generative Models via Gapped Straight-Through Estimator

DOI：
10.48550/arxiv.2206.07235
发表时间：
2022-06
期刊：
影响因子：
0
作者：
Ting-Han Fan;Ta-Chung Chi;Alexander I. Rudnicky;P. Ramadge
通讯作者：
Ting-Han Fan;Ta-Chung Chi;Alexander I. Rudnicky;P. Ramadge

Experiences Deploying Multi-Vantage-Point Domain Validation at Let’s Encrypt

在 Let’s Encrypt 部署多优势点域验证的经验

DOI：
发表时间：
2021
期刊：
Proceedings of the 30th USENIX Security Symposium
影响因子：
0
作者：
Lee, Henry;Wang, Liang;McCarney, Daniel;Shoemaker, Roland;Rexford, Jennifer;Mittal, Prateek
通讯作者：
Mittal, Prateek

An Experimental Study of Balance in Matrix Factorization

矩阵分解平衡的实验研究