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Hava Siegelmann

From Wikipedia, the free encyclopedia
Hava Siegelmann
Born (1964-08-23) August 23, 1964 (age 60)
Haifa
Alma materRutgers University
Known forHypercomputation
AwardsMeritorious Public Service Medal
Scientific career
Fieldscomputer science, neuroscience, system biology, biomedical engineering
InstitutionsUniversity of Massachusetts Amherst
ThesisFoundations of Recurrent Neural Networks (1993)
Doctoral advisorEduardo Daniel Sontag

Hava Siegelmann is an American computer scientist and Provost Professor at the University of Massachusetts Amherst.[1]

Biography

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Siegelmann earned her Ph.D. in Computer Science at Rutgers University (1993) under Eduardo Sontag. Her dissertation was on the topic of Hypercomputation.[2] She earned an M.Sc. in Computer Science at Hebrew University (1992) and a B.A. in Computer Science at the Technion (1988).

Siegelmann was a program manager of several DARPA AI programs including Lifelong Learning Machines,[3] Guaranteeing AI Robustness Against Deception,[4] and Cooperative Secure Learning.[5] DARPA/DoD awarded her with the Meritorious Public Service Medal for her research and leadership.[6]

Selected publications

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  • Ben-Hur, A.; Horn, D.; Siegelmann, H.T.; Vapnik, V. (2001). "Support vector clustering". Journal of Machine Learning Research. 2: 125–137.
  • Siegelmann, H.T. (1995). "Computation Beyond the Turing Limit". Science. 238 (28): 632–637. Bibcode:1995Sci...268..545S. doi:10.1126/science.268.5210.545. PMID 17756722. S2CID 17495161.
  • Siegelmann, Hava T. (1999). Neural networks and analog computation: beyond the Turing limit. Boston, Mass.: Birkhäuser. ISBN 0-8176-3949-7. OCLC 39485184.
  • Siegelmann, H.T.; Ben-Hur, A.; Fishman, S. (1999). "Computational Complexity for Continuous Time Dynamics". Physical Review Letters. 83 (7): 1463–1466. Bibcode:1999PhRvL..83.1463S. doi:10.1103/physrevlett.83.1463.

References

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  1. ^ "Hava T. Siegelmann". Manning College of Information & Computer Sciences. University of Massachusetts Amherst. 20 February 2008. Retrieved 2023-08-05.
  2. ^ Siegelman, Hava (1993). Foundations of Recurrent Neural Networks (PhD thesis). Rutgers University.
  3. ^ "Lifelong Learning Machines (L2M) (Archived)". www.darpa.mil. Retrieved 2023-09-23.
  4. ^ "Guaranteeing AI Robustness Against Deception (GARD)". www.darpa.mil. Retrieved 2023-09-23.
  5. ^ "Cooperative Secure Learning (CSL) (Archived)". www.darpa.mil. Retrieved 2023-09-23.
  6. ^ "DARPA Recognizes UMass Professor Hava Siegelmann for Major Advances in AI" (Press release).
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