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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

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  • 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 2023

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

中国上外网ios方法

  • 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伊联系，伍便更正或删除。感谢所有提供信息材料的网站，并欢迎各类媒体与我伊进行文章共享合作。

  • CSE 146:  Ethics and Algorithms

  • Complexity YouTube channel

  • Property Testing Review Blog

中国上外网ios方法

  • Seshadhri et al (2023). 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 (2023). 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 (2023). Joint Modeling of Longitudinal Relational Data and Exogenous Variables.  Bayesian Analysis, 15 (2) 477-503; DOI: 10.1214/19-BA1160

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  • Data science researchers to tackle privacy challenges in genomics

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