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University of California, Institute for Prediction Technology

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The University of California, Institute for Prediction Technology (UCIPT) is a multidisciplinary organization seeking to accelerate technological research and innovations to predict human behavior and real-world events.

University of California, Institute for Prediction Technology
TypeResearch institute
Established2015
DirectorSean Young, PhD
Location
Los Angeles
,
California
Websitehttp://predictiontechnology.ucla.edu/

History

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Groundwork for UCIPT began in 2013 with a partnership with the UCLA Center for Digital Behavior. In 2014, research results from public health studies by the Institute's research team began to appear in newspapers,[1][2] blogs,[3][4] and other media outlets.[5][6] In January 2015, UCIPT was formally established by Sean Young, who serves as its executive director. Initial funding for the organization was provided by a University of California (UC) President's Research Catalyst Award.[7]

UCIPT has research leaders at four university campuses: UCLA, UC San Diego, UC Santa Cruz, and UC Irvine; UCLA is the hosting institution.

Mission

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Technologies such as social media, wearable devices, and online search engines continuously generate large volumes of public data (social “big data”). UCIPT develops tools to analyze these data to inform public and private sector efforts to solve real-world problems. Areas of focus include public health, finance, cybersecurity, consumer products, politics, and poverty. As of 2016, the primary work of the Institute has progressed in the field of public health, particularly in HIV prevention and detection.[8][9]

Research approach

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1. Big data infrastructure

UCIPT is developing a new open-source platform for ingesting, storing, indexing, querying, and analyzing vast quantities of data. Projects combine ideas from three areas (semi-structured data, parallel databases, and data-intensive computing) in order to create an open-source platform that scales by running on large, shared-nothing commodity computing clusters. An example of work in this area is AsterixDB, which grew out of a collaborative grant awarded by the National Science Foundation to UC Irvine professor and UCIPT member Michael Carey.[10]

2. Machine learning models

UCIPT is working to optimize machine learning models that can improve the accuracy and speed of supervised and unsupervised learning. Biomedical applications, primarily to uncover hidden patterns and correlations within big data, are also being undertaken by UCIPT researchers.

3. Applications to solve real-world problems

UCIPT researchers have developed platforms to analyze social media data that allow real-time predictions about future events (e.g., crime).[11][12] James H. Fowler, a member of UCIPT known for his work on social networks and genopolitics, studies predictors of political opinion as well as public health issues. Sean Young has used social media technologies to predict trends in HIV transmission.[13] Other studies generated by UCIPT focusing on the well-being of transgender persons,[9][14] wearable technology,[15] and substance use[16] are ongoing.

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References

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  1. ^ Gladstone, Mark. "Social media could be used to track HIV" – via The Houston Chronicle.
  2. ^ "Twitter can be used to monitor HIV, drug-related behaviour". Business Standard. Business Standard Private Limited.
  3. ^ "Twitter to monitor HIV and drug-related behavior". International Business Times. Retrieved 2016-03-14.
  4. ^ "Can social media help stop the spread of HIV?". ScienceDaily. Retrieved 2016-03-14.
  5. ^ "Twitter data could be used to prevent HIV?". Sarasota News / ABC 7. Retrieved 2016-03-14.
  6. ^ "Social media -- a soothsayer?". FOX News Radio. Retrieved 2016-03-14.
  7. ^ "2015 Catalyst Award List". University of California. Retrieved 2016-02-22.
  8. ^ "HIV in the Internet Age". The Scientist. Retrieved 2016-03-04.
  9. ^ a b "How Twitter can address public health needs". Medical Daily. 21 May 2015. Retrieved 2016-03-14.
  10. ^ "ASTERIX: A Highly Scalable Parallel Platform for Semistructured Data Management and Analysis". National Science Foundation. Retrieved 2016-02-22.
  11. ^ "New Twitter feature makes it easier to report threats to law enforcement". KGO Radio.
  12. ^ The Opinion Pages (November 18, 2015). "Can predictive policing be ethical and effective?". New York Times.
  13. ^ Young, Sean D. (2015-01-01). "A 'big data' approach to HIV epidemiology and prevention". Preventive Medicine. 70: 17–18. doi:10.1016/j.ypmed.2014.11.002. ISSN 1096-0260. PMC 4364912. PMID 25449693.
  14. ^ "Twitter can help improve transgenders' well-being". The Economic Times. Retrieved 2016-02-22.
  15. ^ Toby, Mekeisha M. (April 2, 2015). "Band Aids: Are fitness trackers really moving human health forward?". Smashd.[permanent dead link‍]
  16. ^ Steyn, Dale (March 3, 2014). "Can Twitter be used to track HIV, drug-related behaviour?". Newsline. Health Newsline.
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