Hava Siegelmann

Hava Siegelmann is a Computer Scientist at the University of Massachusetts who is Director of their Biologically Inspired Neural and Dynamical Systems Lab [ [http://binds.cs.umass.edu/index.html BINDS Lab] ] . In the early 1990s she proposed a new computational model, the Artificial Recurrent Neural Network (ARNN), and proved that it could perform hypercomputation [ [http://www.cs.math.ist.utl.pt/ftp/pub/CostaJF/01-RCS-iwann.pdf Verifying Properties of Neural Networks] ] . She is considered the originator of the term Super-Turing, a subfield which started after her contribution. Hava has also been oneof the originators of the well-known Support Vector Clustering together with Vladimir Vapnik and colleagues. Lately she has published an original work in the field of System-Biology explicating the inter-clock dynamics causing Jet-lag.

Biography

She earned her BA at Technion, her MSc at Hebrew University and her PhD at Rutgers University, all in Computer Science [ [http://binds.cs.umass.edu/havaBio.html Biography at UMass] ] .

Her initial publications on the computational power of Neural Networks culminated in a sole-author paper in Science [ H.T. Siegelmann, "Analog Computational Power," Science, 271(19), January 1996: 373] as well as monograph book on "Neural Networks and Analog Computation: Beyond the Turing Limit"

Publications

Papers

She has written 44 refereed papers in professional journals including:
* W. Bush and H.T. Siegelmann,"Circadian Synchrony in Networks of Protein Rhythm Driven Neurons" Complexity 12, Issue 1 (Sept/Oct 2006)
* T. Leise and H Siegelmann, "Dynamics of a multistage circadian system," Journal of Biological Rhythms, August, 21:4 (2006), 314-323 - this attracted Media Attention e.g. Boston Globe, Yahoo!News, Forbes, United Press International, National Public Radio etc.
* A. Roitershtein, A. Ben-Hur and H.T. Siegelmann "On probabilistic analog automata," Theoretical Computer Science, 320(2-3) pp. 449-464, June 2004
* A. Ben-Hur, H.T. Siegelmann, "Computing with Gene Networks," Chaos 14(1) pp. 145-151, March 2004 (Work was chosen as the work to describe in physics news)
* A. Ben-Hur, J. Feinberg, S. Fishman and H. T. Siegelmann "Random matrix theory for the analysis of the performance of an analog computer: a scaling theory," Phys. Lett. A. 323(3-4) pp. 204-209, March 2004
* A. Ben-Hur, H.T. Siegelmann and S. Fishman. "A theory of complexity for continuous time dynamics." Journal of Complexity 18(1) : 51-86, 2002
* H.T. Siegelmann, "Neural and Super-Turing Computing," Philosophy 2002
* H.T. Siegelmann, "Analog Computational Power," Science, 271(19), January 1996: 373 - responding to comments on her earlier article
* H.T. Siegelmann, "Computation Beyond the Turing Limit," Science, 238(28), April 1995: 632-637
* H.T. Siegelmann and E.D. Sontag, "Analog Computation via Neural Networks," Theoretical Computer Science, 131, 1994: 331-360
* H.T. Siegelmann and E.D. Sontag, "Turing Computability with Neural Networks," Applied Mathematics Letters, 4(6), 1991: 77-80

and in addition given numerous papers at conferences etc..

Books

* Neural Networks and Analog Computation : Beyond the Turing Limit Birkhauser, Boston, December 1998 ISBN 0-8176-3949-7

She has contributed 18 book chapters including:
* "Neural Computing". New Trends in Computer Science, Gheroge Paul editor, 2003
* "Neural Automata and Computational Complexity," in Handbook of Brain Theory and Neural Networks, Michael A. Arbib (ed.), 2002
* "Finite vs. Infinite Descriptive Length in Neural Networks and the Associated Computational Complexity," in Finite vs. Infinite: Contributions to an Eternal Dilemma, C. Calude and Gh. Paun (eds.), Springer Verlag, 2000
* "Neural Automata and Computational Complexity," in Handbook of Brain Theory and Neural Networks, Michael A. Arbin (ed.), 2000
* "Computability with Neural Networks," in Lectures in Applied Mathematics, Vol. 32, J. Reneger, M. Shub, and S. Smale (eds.), American Mathematical Society, 1996: 733-747
* "Recurrent Neural Networks," in The 1000th Volume of Lecture Notes in Computer Science: Computer Science Today, Jan Van Leeuwen (ed.), Springer Verlag, 1995: 29-45

Notes & References