Hava Siegelmann

From 2008 to 2010 (2 years), Hava Siegelmann was a researcher at Harvard UniversityHarvard University. During her time at Harvard University as a researcher, Siegelmann researched evolutionary dynamics with applications to cellular biology.

Hava Siegelmann is a computer scientist, researcher, and program manager for the Information Innovation Office (I2O) of the Defense Advanced Research Projects Agency (DARPA). Siegelmann's work with DARPA focuses on advancing the intelligence of computerized devices through their GaurenteeingGuaranteeing AI Robustness Against Deception (GARD) program, and their Lifelong Learning Machines (L2M) program. Siegelmann's scientific research is primarily focused on creating biologically inspired computational systems capable of exhibiting intelligent behavior.

In 2019, Siegelmann also created the GuarenteeingGuaranteeing AI Robustness Against Deception (GARD) program with DARPA. The GARD program was created to research the vulnerability of machine learning (ML) platforms, and develop secure ML platforms by making them less vulnerable to adversarial deception attacks. Siegelmann made the following comments regarding the purpose of the GARD program:

Computational models developed using the Super-Turing computational model exhibit a 2 to the power aleph-zero possible behavioursbehaviors, which is much greater than the computational models built using the original Turing model. For example, if a machine built using the Turing model was made to have 500 distinct behaviors, a machine built using the Super-Turing computational model based on the same 500 behaviors would have 2 to the power of 500 possible behaviors.

Hava Siegelmann is a computer scientist, researcher, and program manager for the Information Innovation Office (I2O) of the Defense Advanced Research Projects Agency (DARPA). SiegelmannsSiegelmann's work with DARPA focuses on advancing the intelligence of computerized devices through their Gaurenteeing AI Robustness Against Deception (GARD) program, and their Lifelong Learning Machines (L2M) program. Siegelmann's scientific research is primarily focused on creating biologically inspired computational systems capable of exhibiting intelligent behavior.

Hava Siegelmann is a computer scientist, researcher, and program manager for the Information Innovation Office (I2O) of the Defense Advanced Research Projects Agency (DARPA). Siegelmanns work with DARPA focuses on advancing the intelligence of computerized devices through their Gaurenteeing AI Robustness Against deceptionDeception (GARD) program, and their Lifelong Learning Machines (L2M) program. Siegelmann's scientific research is primarily focused on creating biologically inspired computational systems capable of exhibiting intelligent behavior.

Hava Siegelmann attended the Israel Institute of Technology from 1984 to 1988, and graduated with a bachelor of arts degree in computer science.

Hava Siegelmann attendattended The Hebrew University from 1991 to 1992 where she completed a master of science degree in computer science. For her masters thesis, Siegelmann published a paper in 1992 titled "Document Allocation in Multiprocessor Information Retrieval Systems: An Application of Genetic Algorithms".

From 1994 to 2000 (6 years), Hava Siegelmann served as the head of information systems engineering for the Israel Institute of Technology.

From 2001 to 2001 (1 year), Hava Siegelmann was an assistant professor at the Massachusetts Institute of Technology.

From 2008 to 2010 (2 years), Hava Siegelmann was a researcher at Harvard University. During her time at Harvard University as a researcher, Siegelmann researched evolutionary dynamics with applications to cellular biology.

In 2001 (to present), Hava Siegelmann becamehas been serving as a professor at the University of Massachusetts, and a Core Member of their Neuroscience and Behavior Program. She is also the director of the Biologically Inspired Neural and Dynamical Systems (BINDS) laboratory at the University of Massachusetts Amherst, where she runs computational research on memory, circadian systems, cancer, and neurodegenerative diseases.

In 2019, Siegelmann also created the Guarenteeing AI Robustness Against Deception (GARD) program with DARPA. The GARD program was created to research the vulnerability of machine learning (ML) platforms, and create moredevelop secure ML platforms throughby making them less vulnerable to adversarial deception attacks. Siegelmann made the following comments regarding the purpose of the GARD program:

Hava Siegelmann acts as a professional scientist that reviews submissions to the following scientific journals: Journal of Theoretical Biology, Neural Computation, Theoretical Computer Science, J. of Complexity, Neural networks World, Neural Networks, Connection Science, Cognitive Science, IEEE Trans on Neural Networks, and Physics Review letters. She is also the Associate Editor of Frontiers in Computational Neuroscience, and an editorial board member of the American Institute of Physics Journal Chaos: An Interdisciplinary Journal of Nonlinear Science.

Hava Siegelmann is the creator of Super-Turing computation theory. Her theory was published in Science in 1993 for her thesis to obtain her Ph.D thesis at Rutgers University. The thesis was titled "Foundations of Recurrent Neural Networks. She would later published a book on her theory titled "Neural networks and analog computation: Beyond the Turing Limit" in 1998. Siegelmann came up with the Super-Turing computational theory after re-reading the works of the creator of the Turing model, Alan Turing, and attributes her success building her theory to being young and curious:

Her theory details an adaptive computational system that learns and evolves atas is executes using neural networks. When describing what her Super-Turing computational model offers, Siegelmann says: