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NSF LogoThe UC Santa Cruz TRIPODS project brings together researchers from Data Science Santa Cruz Logomathematics, statistics, and computer science to develop a unified theory of data science applied to uncertain and heterogeneous graph and network data. Most real-world applications of networks involve complex phenomena, such as socio-behavioral interactions, biological and/or chemical processes, technical systems like data centers, and communication systems for smart cities. These data are heterogeneous, including multiple modalities and multiple scales. Crucially, the data observed is often incomplete and very noisy. A new foundation for data science needs to be built in order to address these challenges in the context of graph and network data. Similarly, we lack a clear unified theory that allows us to understand how to quantify the uncertainty in the system that arises from the uncertainty in the relationships among its actors. This is a fertile area for transdisciplinary collaboration between statisticians, mathematicians, and computer scientists, with strong impacts on industry, academia, government and broader society.

Faculty

Lise Getoor, PI 

C. Seshadhri, Co-PI

Abel Rodriguez, Co-PI

Dimitris Achlioptas (Computer Science)

ⅤPN加速器 (Computer Science)

Rajarshi Guhaniyogi (Statistics)

Daniele Venturi (Applied Mathematics) 

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D3 Research Center: Data, Discovery, Decisions

Data Science Santa Cruz 

LINQS: Statistical Relational Learning Group

Big Data for Social Good

data science image

新界ssr的新官网

  • Data Science Social: An Evening with the Interns

  • TRIPODS PI Workshop, 10/23 - 10/24, 2018

  • Privacy in Graphs (PIG) Workshop, 11/15 - 11/16, 2018

  • 熊猫ⅤPN安卓

  • Data Science Day 2024

For more events related to Data Science see the Data Science Santa Cruz site

新界ssr的新官网

  • 一个神奇的网站:2021-3-23 · 本站文章部分内容转载自互联网，供读者交流和学习，如有涉及作者版权问题请及时与我伔联系，众便更正或删除。感谢所有提供信息材料的网站，并欢迎各类媒体与我伔进行文章共享合作。

  • CSE 146:  Ethics and Algorithms

  • Complexity YouTube channel

  • Property Testing Review Blog

新界ssr的新官网

  • Seshadhri et al (2024). The impossibility of low-rank representations for triangle-rich complex networks.  Proceedings of the National Academy of Sciences, 117 (11) 5631-5637; DOI: 10.1073/pnas.1911030117

  • Srinivasan, Augustine & Getoor (2024). Tandem Inference: An Out-of-Core Streaming Algorithm For Very Large-Scale Relational Inference.  In Proceedings of the AAAI Conference on Artificial Intelligence.  New York City, NY.

  • Guhaniyogi and Rodriguez (2024). Joint Modeling of Longitudinal Relational Data and Exogenous Variables.  Bayesian Analysis, 15 (2) 477-503; DOI: 10.1214/19-BA1160

新界ssr的新官网

  • 中国青年手机应用下载_中国青年安卓版_中国青年IOS下载 ...:2021-6-13 · 中国青年是中国青年网官方出品的新闻及服务共青团组织资讯客户端，精心打造青闻天下、青年观察、学习者、漂在北上广等栏目，众青年之眼光，评析社会之现象；众青年之感悟，探索人生之真谛；众青年之视角，反映青年之心声。

  • Data science researchers to tackle privacy challenges in genomics

  • Study shows widely used machine learning methods don’t work as claimed