Hava Siegelmann

Hava Siegelmann is a professor of computer science. Her academic position is in the school of Computer Science and the Program of Neuroscience and Behavior at the University of Massachusetts Amherst; she is the director of the school's Biologically Inspired Neural and Dynamical Systems Lab and is the Provost Professor of the University of Massachusetts. She was loaned to the federal government DARPA 2016-2019 to initiate and run their most advanced AI programs including her Lifelong Learning Machine (L2M) program.[1] and Guaranteeing AI Robustness against Deceptions (GARD).[2] She received the rarely awarded Meritorious Public Service Medal[3] — one of the highest honors the Department of Defense agency can bestow on a private citizen.

Hava Siegelmann
Born (1964-08-23) August 23, 1964
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

Biography

Siegelmann is an American computer scientist who founded the field of Super-Turing computation. As a DAPRA Program Manager she introduced the field of Lifelong Learning Machines, the most recent advancement in Artificial Intelligence, which is a great leap forward from the continual task-learning paradigm. She contributed some of the most significant research results in both Neural Networks and Lifelong Learning. For her lifetime contribution to the field of Neural Networks she was the recipient of the 2016 Donald Hebb Award. She earned her PhD at Rutgers University, New Jersey, in 1993.[4]

In the early 1990s, she and Eduardo D. Sontag proposed a new computational model, the Artificial Recurrent Neural Network (ARNN), which has been of both practical and mathematical interest. They proved mathematically that ARNNs have well-defined computational powers that extend the classical Universal Turing machine. Her initial publications on the computational power of Neural Networks culminated in a single-authored paper in Science[5][6] and her monograph, "Neural Networks and Analog Computation: Beyond the Turing Limit".

In her Science paper,[5] Siegelmann demonstrates how chaotic systems (that cannot be described by Turing computation) are now described by the Super-Turing model. This is significant since many biological systems not describable by standard means (e.g., heart, brain) can be described as a chaotic system and can now be modelled mathematically.[7][8]

The theory of Super-Turing computation has attracted attention in physics, biology, and medicine.[9][10][11] Siegelmann is also an originator of the Support Vector Clustering, a widely used algorithm in industry, for big data analytics, together with Vladimir Vapnik and colleagues.[12] Siegelmann also introduced a new notion in the field of Dynamical Diseases, "the dynamical health",[13] which describes diseases in the terminology and analysis of dynamical system theory, meaning that in treating disorders, it is too limiting to seek only to repair primary causes of the disorder; any method of returning system dynamics to the balanced range, even under physiological challenges (e.g., by repairing the primary source, activating secondary pathways, or inserting specialized signaling), can ameliorate the system and be extremely beneficial to healing. Employing this new concept, she revealed the source of disturbance during shift work and travel leading to jet-lag[14] and is currently studying human memory and cancer[15] in this light.

Siegelmann has been active throughout her career in advancing and supporting minorities and women in the fields of Computer Science and Engineering. Through her career Siegelmann consulted with numerous companies, and has received a reputation for her practical problem solving capabilities. She is on the governing board of the International Neural Networks Society, and an editor in the Frontiers on Computational Neuroscience.

Publications

Papers

Partial List of Applications
  • Sivan, S.; Filo, O.; Siegelman, H. (2007). "Application of Expert Networks for Predicting Proteins Secondary Structure". Biomolecular Engineering. 24 (2): 237–243. doi:10.1016/j.bioeng.2006.12.001. PMID 17236807.
  • Eldar, S; Siegelmann, H. T.; Buzaglo, D.; Matter, I.; Cohen, A.; Sabo, E.; Abrahamson, J. (2002). "Conversion of Laparoscopic Cholecystectomy to open cholecystectomy in acute cholecystitis: Artificial neural networks improve the prediction of conversion". World Journal of Surgery. 26 (1): 79–85. doi:10.1007/s00268-001-0185-2. PMID 11898038. S2CID 189870780.
  • Lange, D.; Siegelmann, H.T.; Pratt, H.; Inbar, G.F. (2000). "Overcoming Selective Ensemble Averaging: Unsupervised Identification of Event Related Brain Potentials". IEEE Transactions on Biomedical Engineering. 47 (6): 822–826. doi:10.1109/10.844236. PMID 10833858. S2CID 10770977.
  • Karniely, H.; Siegelmann, H.T. (2000). "Sensor Registration Using Neural Networks". IEEE Transactions on Aerospace and Electronic Systems. 36 (1): 85–98. Bibcode:2000ITAES..36...85K. doi:10.1109/7.826314.
  • Siegelmann, H.T.; Nissan, E.; Galperin, A. (1997). "A Novel Neural/Symbolic Hybrid Approach to Heuristically Optimized Fuel Allocation and Automated Revision of Heuristics in Nuclear Engineering". Advances in Engineering Software. 28 (9): 581–592. doi:10.1016/s0965-9978(97)00040-9.

Books
  • Neural Networks and Analog Computation: Beyond the Turing Limit, Birkhauser, Boston, December 1998 ISBN 0-8176-3949-7[16]

Notes and references
  1. DARPA Biography
  2. https://www.darpa.mil/program/guaranteeing-ai-robustness-against-deception
  3. "DARPA honors artificial intelligence expert". Federal News Network. 2020-06-30. Retrieved 2021-08-07.
  4. Biography at UMass
  5. Siegelmann, H. T. (28 April 1995). "Computation Beyond the Turing Limit". Science. 268 (5210): 545–548. Bibcode:1995Sci...268..545S. doi:10.1126/science.268.5210.545. PMID 17756722. S2CID 17495161.
  6. Siegelmann, H.T. (1996). "Reply: Analog Computational Power". Science. 271 (5247): 373. doi:10.1126/science.271.5247.373.
  7. Barkai, N.; Leibler, S. (26 June 1997). "Robustness in simple biochemical networks". Nature. 387 (6636): 913–917. Bibcode:1997Natur.387..913B. doi:10.1038/43199. PMID 9202124. S2CID 4311605.
  8. McGowan, PO; Szyf, M (July 2010). "The epigenetics of social adversity in early life: implications for mental health outcomes". Neurobiology of Disease. 39 (1): 66–72. doi:10.1016/j.nbd.2009.12.026. PMID 20053376. S2CID 2822345.
  9. Yasuhiro Fukushima; Makoto Yoneyama; Minoru Tsukada; Ichiro Tsuda; Yutaka Yamaguti; Shigeru Kuroda (2008). "Physiological Evidence for Cantor Coding Output in Hippocampal CA1". In Rubin Wang; Fanji Gu; Enhua Chen (eds.). Advances in cognitive neurodynamics ICCN 2007 proceedings of the International Conference on Cognitive Neurodynamics. Dordrecht: Springer. pp. 43–45. ISBN 978-1-4020-8387-7.
  10. Bodén, Mikael; Alan Blair (March 2003). "Learning the Dynamics of Embedded Clauses" (PDF). Applied Intelligence. 19 (1/2): 51–63. doi:10.1023/A:1023816706954. S2CID 5811984.
  11. Toni, R; Spaletta, G; Casa, CD; Ravera, S; Sandri, G (2007). "Computation and brain processes, with special reference to neuroendocrine systems". Acta Biomedica. 78 Suppl 1: 67–83. PMID 17465326.
  12. Ben-Hur, A.; Horn, D.; Siegelmann, H.T.; Vapnik, V. (2001). "Support vector clustering". Journal of Machine Learning Research. 2: 125–137.
  13. Ben-Hur, A.; Horn, D.; Siegelmann, H.T.; Vapnik, V. (2000). A support vector clustering method. Pattern Recognition, 2000. Proceedings. 15th International Conference on. Vol. 2. pp. 724–727. doi:10.1109/ICPR.2000.906177. ISBN 978-0-7695-0750-7. S2CID 31831024.
  14. Leise, T.; Hava Siegelmann (1 August 2006). "Dynamics of a Multistage Circadian System". Journal of Biological Rhythms. 21 (4): 314–323. doi:10.1177/0748730406287281. PMID 16864651. S2CID 17268433.
  15. Olsen, Megan; Siegelmann-Danieli, Nava; Siegelmann, Hava T.; Ben-Jacob, Eshel (May 13, 2010). Ben-Jacob, Eshel (ed.). "Dynamic Computational Model Suggests That Cellular Citizenship Is Fundamental for Selective Tumor Apoptosis". PLOS ONE. 5 (5): e10637. Bibcode:2010PLoSO...510637O. doi:10.1371/journal.pone.0010637. PMC 2869358. PMID 20498709.
  16. Siegelmann, Hava T. (1999). Neural networks and analog computation: beyond the Turing limit. Boston, Mass.: Birkhäuser. ISBN 0-8176-3949-7. OCLC 39485184.
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