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Carlos Guestrin

From Wikipedia, the free encyclopedia

Carlos Ernesto Guestrin is a computer scientist and a professor at Stanford University. He is best known for his contributions to scalable machine learning algorithms.[1]

Biography

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Guestrin received a Mechatronics Engineer degree from the Polytechnic School of the University of São Paulo,[2] and a Ph.D. in Computer Science from Stanford University, advised by Daphne Koller.[3] Guestrin went on to work as professor at Carnegie Mellon University (2004 to 2012), the University of Washington (2012-2021), and Stanford University (Since 2021).[4] He was a co-founder of Turi (formerly GraphLab), a machine learning startup that was acquired by Apple Inc. in 2016.[5] After selling the startup, Guestrin worked at Apple as the Senior Director of Machine Learning and AI.[6]

Guestrin was involved in the creation of various popular machine learning libraries and methods, including the XGBoost library,[7] the LIME technique for explainable machine learning,[8] and the GraphLab project for scalable machine learning.[9]

Honors and Awards

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Guestin has received multiple honors and awards, including:

  • Receiving an ONR Young Investigator Award, 2008[10]
  • Receiving and IJCAI Computers and Thought Award (2009)[11]
  • Being awarded a Presidential Early Carreer Award (2010)[12]
  • Being elected as a Member of the National Academy of Engineering (2024)[13]
  • Receiving awards at prestigious CS confereces, including KDD 2007[14], KDD 2010[15], ACL 2020[16], and AISTATS 2010[17]

References

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  1. ^ Chen, Tianqi; Guestrin, Carlos (2016-08-13). "XGBoost: A Scalable Tree Boosting System". Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD '16. New York, NY, USA: Association for Computing Machinery. pp. 785–794. doi:10.1145/2939672.2939785. ISBN 978-1-4503-4232-2.
  2. ^ "Brasileiros da IA: Carlos Guestrin já vendeu empresa para a Apple e recebeu medalha de Obama". Estadão (in Brazilian Portuguese). Retrieved 2024-11-30.
  3. ^ "Planning under uncertainty in complex structured environments - ProQuest". www.proquest.com. Retrieved 2024-11-30.
  4. ^ "Carlos Guestrin | Paul G. Allen School of Computer Science & Engineering". www.cs.washington.edu. Retrieved 2024-11-30.
  5. ^ Soper, Taylor (2016-08-05). "Exclusive: Apple acquires Turi in major exit for Seattle-based machine learning and AI startup". GeekWire. Retrieved 2024-11-30.
  6. ^ "Carlos Ernesto Guestrin | Computer Science". profiles.stanford.edu. Retrieved 2024-11-30.
  7. ^ "Story and Lessons Behind the Evolution of XGBoost". 2016-08-07. Archived from the original on 7 August 2016. Retrieved 2024-11-30.
  8. ^ Rothman, Denis (2020-10-07). "Exploring LIME Explanations and the Mathematics Behind It". Codemotion Magazine. Retrieved 2024-11-30.
  9. ^ Low, Yucheng; Gonzalez, Joseph E.; Kyrola, Aapo; Bickson, Danny; Guestrin, Carlos E.; Hellerstein, Joseph (2014-08-09), GraphLab: A New Framework For Parallel Machine Learning, arXiv:1408.2041
  10. ^ "2008 Young Investigators". Office of Naval Research. 2022-03-18. Retrieved 2024-11-30.
  11. ^ "Twenty-First International Joint Conference on Artificial Intelligence". www.ijcai.org. Retrieved 2024-11-30.
  12. ^ "President Obama Honors Outstanding Early-Career Scientists". whitehouse.gov. 2010-01-13. Retrieved 2024-11-30.
  13. ^ "National Academy of Engineering Elects 114 Members and 21 International Members". NAE Website. Retrieved 2024-11-30.
  14. ^ "SIGKDD Awards : 2007 SIGKDD Best Paper Award". kdd.org. Retrieved 2024-11-30.
  15. ^ University, Carnegie Mellon ($dateFormat). "Team Wins Best Research Paper: Innovative Contribution KDD 2010 - Machine Learning - CMU - Carnegie Mellon University". Machine Learning | Carnegie Mellon University. Retrieved 2024-11-30. {{cite web}}: Check date values in: |date= (help)
  16. ^ Chairs, Program (2020-07-08). "Best Paper Awards at ACL 2020". ACL 2020. Retrieved 2024-11-30.
  17. ^ University, Carnegie Mellon. "Best Paper Awards - Machine Learning - CMU - Carnegie Mellon University". Machine Learning | Carnegie Mellon University. Retrieved 2024-11-30.