Bin Yu
郁彬
EducationPeking University (BA, 1984)
University of California, Berkeley (MS, 1987; PhD, 1990)
AwardsIMS Fellow (1999)
IEEE Fellow (2001)
ASA Fellow (2005)
AAAS Fellow (2013)
Member of NAS (2014)
Elizabeth L. Scott Award (2018)
COPSS Distinguished Achievement Award and Lectureship (2023)
Scientific career
FieldsStatistics
Machine Learning
InstitutionsUniversity of California, Berkeley
University of Wisconsin–Madison
Bell Labs
Doctoral advisorLucien Le Cam
Terry Speed
Websitewww.stat.berkeley.edu/~binyu/

Bin Yu (Chinese: 郁彬) is a Chinese-American statistician. She is currently Chancellor's Professor in the Departments of Statistics and of Electrical Engineering & Computer Sciences at the University of California, Berkeley.[1][2]

Biography

Yu earned a bachelor's degree in mathematics in 1984 from Peking University, and went on to pursue graduate studies in statistics at Berkeley, earning a master's degree in 1987 and a Ph.D. in 1990. Her dissertation, Some Results on Empirical Processes and Stochastic Complexity, was jointly supervised by Lucien Le Cam and Terry Speed.[3]

After postdoctoral studies at the Mathematical Sciences Research Institute and an assistant professorship at the University of Wisconsin–Madison, she returned to Berkeley as a faculty member in 1993, was tenured in 1997, and became Chancellor's Professor in 2006. She also worked at Bell Labs from 1998 to 2000, while on leave from Berkeley, and has held visiting positions at several other universities. She chaired the Department of Statistics at Berkeley from 2009 to 2012, and was president of the Institute of Mathematical Statistics in 2014.[1][2][4] In 2023, she was awarded the COPSS Distinguished Achievement Award and Lectureship.


Research

Yu's work leverages computational developments to solve scientific problems by combining statistical machine learning approaches with the domain expertise of many collaborators, spanning many fields including statistics, machine learning, neuroscience, genomics, and remote sensing.[5] Her recent work has focused on solidifying a vision for data science, including a framework for veridical data science[6] and a framework for interpretable machine learning.[7] Yu has also developed a PCS (predictability, computability, and stability) framework for veridical data science to unify, streamline and expand on ideas and best practices of machine learning and statistics. Yu has received recent news coverage regarding her veridical data science framework,[8] investigations into the theoretical foundations of deep learning,[9] and work forecasting COVID-19 severity in the US.[10]

Other research included research in the area of statistical machine learning methods/algorithms (and associated statistical inference problems) such as dictionary learning, non-negative matrix factorization (NMF), EM and deep learning (CNNs and LSTMs), and heterogeneous effect estimation in randomized experiments (X-learner).

Honors and awards

Yu is a fellow of the Institute of Mathematical Statistics, the IEEE, the American Statistical Association, the American Association for the Advancement of Science, the American Academy of Arts and Sciences, and the National Academy of Sciences.[1][2][11][12][13] In 2012, she was the Tukey Lecturer of the Bernoulli Society for Mathematical Statistics and Probability.[1][2] In 2018, she was awarded the Elizabeth L. Scott Award. She was invited to give the Breiman lecture at NeurIPS 2019 (formally known as NIPS), on the topic of veridical data science.[14][15][16][17] In 2021, she was awarded an honorary doctorate by the University of Lausanne.[18] And in 2023, she received the COPSS distinguished achievement lecture.[19]

References

  1. 1 2 3 4 Faculty biography, UC Berkeley, accessed 2020-10-18.
  2. 1 2 3 4 "Bin Yu", People News for August 2012, Amstatnews, American Statistical Association, August 1, 2012, archived from the original on July 3, 2013.
  3. ↑ Bin Yu at the Mathematics Genealogy Project
  4. ↑ Current officials Archived 2016-10-31 at the Wayback Machine, Institute of Mathematical Statistics, retrieved 2013-04-24.
  5. ↑ "Google Scholar Profile for Bin Yu".
  6. ↑ Yu, Bin; Kumbier, Karl (2019-11-12). "Veridical Data Science" (PDF). PNAS. 117 (8): 3920–3929. arXiv:1901.08152. doi:10.1073/pnas.1901326117. PMC 7049126. PMID 32054788.
  7. ↑ Murdoch, W. James; Singh, Chandan; Kumbier, Karl; Abbasi-Asl, Reza; Yu, Bin (2019-10-29). "Interpretable machine learning: definitions, methods, and applications". Proceedings of the National Academy of Sciences. 116 (44): 22071–22080. arXiv:1901.04592. doi:10.1073/pnas.1900654116. ISSN 0027-8424. PMC 6825274. PMID 31619572. S2CID 204755862.
  8. ↑ "Bin Yu | Computing, Data Science, and Society". data.berkeley.edu. Retrieved 2020-10-19.
  9. ↑ "UC Berkeley to lead $10M NSF/Simons Foundation program to investigate theoretical underpinnings of deep learning | Computing, Data Science, and Society". data.berkeley.edu. Retrieved 2020-10-19.
  10. ↑ "Getting the right equipment to the right people". Berkeley Engineering. Retrieved 2020-10-19.
  11. ↑ Honored fellows Archived 2016-10-19 at the Wayback Machine, Institute of Mathematical Statistics, retrieved 2013-04-24.
  12. ↑ Directory of IEEE Fellows Archived 2013-01-31 at the Wayback Machine, retrieved 2013-04-24.
  13. ↑ Newly elected members Archived 2013-05-01 at the Wayback Machine, American Academy of Arts and Sciences, April 2013, retrieved 2013-04-24.
  14. ↑ "Elizabeth L. Scott Award". Archived from the original on 15 August 2018. Retrieved 30 March 2019.
  15. ↑ "Yu Award Release". 2018-07-12. Retrieved 30 March 2019.
  16. ↑ "Yu Award Release". 2018-09-11. Retrieved 30 March 2019.
  17. ↑ "Breiman Lecture recording". YouTube. 2020-10-18. Retrieved 18 October 2020.
  18. ↑ "Bin Yu receives a doctorate honoris causa (honorary doctorate) from the Université de Lausanne's Faculty of Business and Economics | Department of Statistics". statistics.berkeley.edu. Retrieved 2023-12-11.
  19. ↑ "ASA Community". community.amstat.org. Retrieved 2023-12-11.
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.