HDR TRIPODS: Building the Foundation for a Data-Intensive Studies Center-

HDR TRIPODS：为数据密集型研究中心奠定基础-

• 批准号: 1934553
• 负责人: Lenore Cowen
• 金额: $ 150万
• 依托单位: Tufts University
• 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 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934553&HistoricalAwards=false
• 关键词: HDR; TRIPODS; Building; Foundation; Data

Tufts University is launching the T-TRIPODS institute, that will focus an interdisciplinary effort across multiple departments and campuses to advance the understanding of foundations of data science. The project seeks to support the first three years of the operation of the institute, and will support a culture of interdisciplinary research and learning in data sciences across multiple departments, fostering collaboration between mathematicians, computer scientists, and electrical engineers, as well as with scientists and scholars in a wide range of application domains. The model is built around overlapping three-year focused research topics, with an offset timeline, so that each year, the oldest research topic sunsets while a new research topic is added. For each focused research topic, the project will convene interdisciplinary teams of mathematicians, computer scientists, statisticians and electrical engineers to address timely questions and solve important problems on the frontiers of data science. Complementing and completing the research effort are teaching and curriculum development efforts for data science at the undergraduate, graduate and professional levels. Furthermore, the structure of T-TRIPODS will foster specific and deep connections with application domain experts in several areas, leading to translational research. T-TRIPODS is strongly committed to Data Science for All, and will partner closely with the Tufts Center for STEM Diversity to broaden participation in undergraduate research opportunities in data science at Tufts.T-TRIPODS will address three research thrusts. Research Focus I (Graphs and Tensor Representations of Data) in the first year, which will be joined by Focus II (Collecting, Modeling, and Learning from Data with a Spatial or Temporal Dimension) in the second year, and Focus III (Data Guarantees: Analysis of Data with Assurances of Quality, Transparency, Fairness, Privacy, and Trust) in year three, which will bring the institute up to full capacity with three research foci running simultaneously. All research foci will include cross-disciplinary training of graduate students; workshops that bring together experts and early career scientists from math, computer science, and electrical engineering; training modules in application-specific concerns around ethical safeguards for data usage and analysis; and an Ideas Lab activity to connect researchers from the core research topics to domain experts in four identified broad application areas: 1) Biological and Biomedical data, 2) Education and Cognitive Science, 3) Smart Cities, Development, and Design and 4) Computational Arts and Humanities (including Language and Music). T-TRIPODS will be integrated within Tufts' new Data Intensive Science Center (DISC) and will synergize with and enhance existing Tufts University degree programs in Data Science.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.

塔夫茨大学正在成立 T-TRIPODS 研究所，该研究所将集中跨多个院系和校园的跨学科努力，以增进对数据科学基础的理解。该项目旨在支持该研究所运营的前三年，并将支持跨多个部门的数据科学跨学科研究和学习文化，促进数学家、计算机科学家和电气工程师之间以及与广泛应用领域的科学家和学者之间的合作。该模型围绕重叠的三年重点研究主题构建，并具有偏移时间表，因此每年，最古老的研究主题都会日落，同时会添加新的研究主题。针对每个重点研究课题，该项目将召集由数学家、计算机科学家、统计学家和电气工程师组成的跨学科团队，及时解决问题并解决数据科学前沿的重要问题。本科生、研究生和专业水平的数据科学教学和课程开发工作是对研究工作的补充和完成。此外，T-TRIPODS 的结构将促进与多个领域的应用领域专家的具体而深入的联系，从而促进转化研究。 T-TRIPODS 坚定地致力于全民数据科学，并将与塔夫茨 STEM 多样性中心密切合作，以扩大参与塔夫茨数据科学本科生研究的机会。T-TRIPODS 将解决三个研究重点。第一年的研究重点 I（数据的图形和张量表示），第二年的研究重点 II（空间或时间维度的数据收集、建模和学习），以及第三年的研究重点 III（数据保证：分析数据，保证质量、透明度、公平性、隐私和信任），这将使研究所在三个研究重点同时运行的情况下满负荷运转。所有研究重点将包括研究生的跨学科培训；研讨会汇集了数学、计算机科学和电气工程领域的专家和早期职业科学家；针对数据使用和分析的道德保障的特定应用问题的培训模块；创意实验室活动将核心研究主题的研究人员与四个确定的广泛应用领域的领域专家联系起来：1）生物和生物医学数据，2）教育和认知科学，3）智能城市、开发和设计以及4）计算艺术和人文学科（包括语言和音乐）。 T-TRIPODS 将整合到塔夫茨大学新的数据密集型科学中心 (DISC) 内，并将与塔夫茨大学现有的数据科学学位课程协同并加强。该项目是美国国家科学基金会利用数据革命 (HDR) 大创意活动的一部分。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Easy Variational Inference for Categorical Models via an Independent Binary Approximation

通过独立二元近似对分类模型进行简单的变分推理

• 发表时间: 2022
• 期刊: International Conference on Machine Learning
• 作者: Michael T. Wojnowicz;Shuchin Aeron;Eric L. Miller;Michael C. Hughes
• 通讯作者: Michael C. Hughes

Randomized approaches to accelerate MCMC algorithms for Bayesian inverse problems

加速贝叶斯逆问题 MCMC 算法的随机方法

• DOI: 10.1016/j.jcp.2021.110391
• 发表时间: 2021
• 期刊: Journal of Computational Physics
• 影响因子: 4.1
• 作者: Saibaba, Arvind K.;Prasad, Pranjal;de Sturler, Eric;Miller, Eric;Kilmer, Misha E.
• 通讯作者: Kilmer, Misha E.

Cell shape, and not 2D migration, predicts extracellular matrix-driven 3D cell invasion in breast cancer

• DOI: 10.1063/1.5143779
• 发表时间: 2020-06-01
• 期刊: APL BIOENGINEERING
• 影响因子: 6
• 作者: Baskaran, Janani P.;Weldy, Anna;Oudin, Madeleine J.
• 通讯作者: Oudin, Madeleine J.

GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks

• DOI: 10.1093/bioinformatics/btaa459
• 发表时间: 2020-07-01
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Devkota, Kapil;Murphy, James M.;Cowen, Lenore J.
• 通讯作者: Cowen, Lenore J.

An inner–outer iterative method for edge preservation in image restoration and reconstruction

• DOI: 10.1088/1361-6420/abb299
• 发表时间: 2019-12
• 期刊: Inverse Problems
• 影响因子: 2.1
• 作者: S. Gazzola;M. Kilmer;J. Nagy;O. Semerci;E. Miller