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Dana Angluin

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Dana Angluin
Alma materUniversity of California, Berkeley
Known for
  • L* Algorithm
  • Query learning
  • Exact learning
  • Population protocols
Scientific career
Fields
InstitutionsYale University
Thesis An Application of the Theory of Computational Complexity to the Study of Inductive Inference  (1976)
Doctoral advisorManuel Blum[1]
Doctoral studentsEhud Shapiro

Dana Angluin is a professor emeritus of computer science at Yale University.[2] She is known for foundational work in computational learning theory[3][4][5] and distributed computing.[6]

Education

Angluin received her B.A. (1969) and Ph.D. (1976) at University of California, Berkeley.[7] Her thesis, entitled "An application of the theory of computational complexity to the study of inductive inference" [8] was one of the first works to apply complexity theory to the field of inductive inference.[9] Angluin joined the faculty at Yale in 1979.[9]

Research

Angluin has written highly cited papers on computational learning theory, where she studied learning from noisy examples[5] and learning regular sets from queries and counterexamples (the L* algorithm).[4] In distributed computing, she co-invented the population protocol model and studied the problem of consensus.[6][10] In probabilistic algorithms, she has studied randomized algorithms for Hamiltonian circuits and matchings.[11][9][12]

Angluin helped found the Computational Learning Theory (COLT) conference, and has served on program committees and steering committees for COLT[13][14][15] She served as an area editor for Information and Computation from 1989–1992.[16][17] She organized Yale's Computer Science Department's Perlis Symposium in April 2001: "From Statistics to Chat: Trends in Machine Learning".[18] She is a member of the Association for Computing Machinery and the Association for Women in Mathematics.

Angluin has also published works on Ada Lovelace and her involvement with the Analytical Engine.[19]

Selected publications

  • Dana Angluin (1988). Queries and concept learning. Machine Learning. 2 (4): 319-342.
  • Dana Angluin (1987). "Learning Regular Sets from Queries and Counter-Examples" (PDF). Information and Control. 75 (2): 87–106. doi:10.1016/0890-5401(87)90052-6. Archived from the original (PDF) on 2013-12-02.
  • Dana Angluin and Philip Laird (1988). Learning from noisy examples. Machine Learning 2 (4), 343-370.
  • Dana Angluin and Leslie Valiant (1979). Fast probabilistic algorithms for Hamiltonian circuits and matchings. Journal of Computer and system Sciences 18 (2), 155-193
  • Dana Angluin (1980). "Finding Patterns Common to a Set of Strings". Journal of Computer and System Sciences. 21: 46–62. doi:10.1016/0022-0000(80)90041-0.
  • Dana Angluin (1980). "Inductive Inference of Formal Languages from Positive Data" (PDF). Information and Control. 45 (2): 117–135. doi:10.1016/s0019-9958(80)90285-5. [4]
  • Dana Angluin, James Aspnes, Zoë Diamadi, Michael J Fischer, René Peralta (2004). Computation in networks of passively mobile finite-state sensors. Distributed computing 18 (4), 235-253.
  • Dana Angluin (1976). An Application of the Theory of Computational Complexity to the Study of Inductive Inference (Ph.D.). University of California at Berkeley.

See also

References

  1. ^ Dana Angluin at the Mathematics Genealogy Project
  2. ^ "Dana Angluin, B.A., Ph.D. University of California at Berkeley, 1969, 1976. Joined Yale Faculty 1979. | Computer Science". cpsc.yale.edu. Retrieved 2021-12-01.
  3. ^ Angluin, Dana (April 1988). "Queries and concept learning". Machine Learning. 2 (4): 319–342. doi:10.1007/bf00116828. ISSN 0885-6125. S2CID 11357867.
  4. ^ a b Angluin, Dana (November 1987). "Learning regular sets from queries and counterexamples". Information and Computation. 75 (2): 87–106. doi:10.1016/0890-5401(87)90052-6. ISSN 0890-5401.
  5. ^ a b Angluin, Dana; Laird, Philip (April 1988). "Learning from noisy examples". Machine Learning. 2 (4): 343–370. doi:10.1007/bf00116829. ISSN 0885-6125. S2CID 29767720.
  6. ^ a b Angluin, Dana; Aspnes, James; Diamadi, Zoë; Fischer, Michael J.; Peralta, René (2006-03-01). "Computation in networks of passively mobile finite-state sensors". Distributed Computing. 18 (4): 235–253. doi:10.1007/s00446-005-0138-3. ISSN 1432-0452. S2CID 2802601.
  7. ^ "Dana Angluin, B.A., Ph.D. University of California at Berkeley, 1969, 1976. Joined Yale Faculty 1979. | Computer Science". cpsc.yale.edu. Retrieved 2020-11-08.
  8. ^ Angluin, Dana Charmian (1976). An Application of the Theory of Computational Complexity to the Study of Inductive Inference (PhD Thesis thesis). University of California, Berkeley.
  9. ^ a b c "Dana Angluin, B.A., Ph.D. University of California at Berkeley, 1969, 1976. Joined Yale Faculty 1979. | Computer Science". cpsc.yale.edu. Retrieved 2016-12-11.
  10. ^ Angluin, Dana; Aspnes, James; Eisenstat, David (2008-07-01). "A simple population protocol for fast robust approximate majority". Distributed Computing. 21 (2): 87–102. doi:10.1007/s00446-008-0059-z. ISSN 1432-0452. S2CID 2652934.
  11. ^ Angluin, Dana; Valiant, Leslie G. (1977). "Fast probabilistic algorithms for hamiltonian circuits and matchings". Proceedings of the Ninth Annual ACM Symposium on Theory of Computing - STOC '77. Stoc '77. New York, New York, USA: ACM Press: 30–41. doi:10.1145/800105.803393. ISBN 9781450374095. S2CID 2624407.
  12. ^ D Angluin (1976). "An Application of the Theory of Computational Complexity to the Study of Inductive Inference." Available from ProQuest Dissertations & Theses Global. (302813707)
  13. ^ [1], COLT '89 Proceedings
  14. ^ [2], COLT '02 Proceedings
  15. ^ [3], COLT '08 Proceedings
  16. ^ "Editorial Board". Information and Computation. 82 (1): i. 1989. doi:10.1016/0890-5401(89)90061-8.
  17. ^ "Editorial Board". Information and Computation. 99 (1): i. 1992. doi:10.1016/0890-5401(92)90023-9.
  18. ^ "Symposium will explore 'trends in machine learning'". Yale Bulletin and Calendar. April 20, 2001. Archived from the original on April 18, 2009.
  19. ^ Case, Bettye Anne; Leggett, Anne M. (2005). Complexities: Women in Mathematics. Princeton University Press. p. 60. ISBN 9781400880164.
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