Hava Siegelmann is an American computer scientist and Provost Professor at the University of Massachusetts Amherst.[1]
Hava Siegelmann | |
|---|---|
| Born | August 23, 1964 |
| Alma mater | Rutgers University |
| Known for | Hypercomputation |
| Awards | Meritorious Public Service Medal |
| Scientific career | |
| Fields | computer science, neuroscience, system biology, biomedical engineering |
| Institutions | University of Massachusetts Amherst |
| Thesis | Foundations of Recurrent Neural Networks (1993) |
| Doctoral advisor | Eduardo Daniel Sontag |
Biography
editSiegelmann 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
edit- 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
edit- ^ "Hava T. Siegelmann". Manning College of Information & Computer Sciences. University of Massachusetts Amherst. 20 February 2008. Retrieved 2023-08-05.
- ^ Siegelman, Hava (1993). Foundations of Recurrent Neural Networks (PhD thesis). Rutgers University.
- ^ "Lifelong Learning Machines (L2M) (Archived)". www.darpa.mil. Retrieved 2023-09-23.
- ^ "Guaranteeing AI Robustness Against Deception (GARD)". www.darpa.mil. Retrieved 2023-09-23.
- ^ "Cooperative Secure Learning (CSL) (Archived)". www.darpa.mil. Retrieved 2023-09-23.
- ^ "DARPA Recognizes UMass Professor Hava Siegelmann for Major Advances in AI" (Press release).