Wang-Chiew_Tan

Wang-Chiew Tan

Wang-Chiew Tan

Singaporean computer scientist

Wang-Chiew Tan is a Singaporean computer scientist specializing in data management and natural language processing. Her work in data management includes data provenance (or data lineage) and data integration. She is currently a Research Scientist at Facebook AI,[1] and was previously the Director of Research at Megagon Labs in Mountain View, California.[2]

Quick Facts Nationality, Alma mater ...

At Megagon Labs, Tan was the lead researcher on a study with the University of Tokyo that concluded that the company of other people is more effective than pets at making people happy.[3]

Education and career

Tan earned her bachelor's degree in computer science (first-class) at the National University of Singapore, and completed her Ph.D. at the University of Pennsylvania.[2] Her 2002 dissertation, Data Annotations, Provenance, and Archiving, was jointly supervised by Peter Buneman and Sanjeev Khanna.[4][5]

Before working at Megagon, she has been a professor of computer science at the University of California, Santa Cruz beginning in 2002,[6] and, from 2010 to 2012, was on leave from Santa Cruz as a researcher at IBM Research - Almaden.[2]

Recognition

Tan was named a Fellow of the Association for Computing Machinery in 2015 "for contributions to data provenance and to the foundations of information integration".[7]

References

  1. [1]
    "Wang-Chiew Tan's Homepage". wangchiew.github.io.
  2. [2]
    Wang-Chiew Tan, Director of Research, Megagon Labs, retrieved 2018-10-16
  3. [3]
    Foley, Katherine Ellen (March 3, 2018), "Pets don't make humans immediately happy the way other people do", Quartz
  4. [4]
    "Data annotations, provenance, and archiving", ACM Digital Library, Association for Computing Machinery, retrieved 2018-10-16
  5. [5]
    Wang-Chiew Tan at the Mathematics Genealogy Project
  6. [6]
    "New Faculty", UC Santa Cruz Currents, January 20, 2003
  7. [7]
    "Wang-Chiew Tan Wang-Chiew", ACM Fellows, Association for Computing Machinery, retrieved 2018-10-16
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