A computer scientist, researcher, and program manager for the Information Innovation Office (I2O) of the Defense Advanced Research Projects Agency (DARPA) where she oversees their Guaranteeing AI Robustness Against Deception (GARD) program and their Lifelong Learning Machines (L2M) program.
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.
A computer scientist, researcher, and program manager for the Information Innovation Office (I2O) of the Defense Advanced Research Projects Agency (DARPA) where she oversees their GuarenteeingGuaranteeing AI Robustness Against Deception (GARD) program and their Lifelong Learning Machines (L2M) program.
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:
July 23, 2019
In July 2016 joined the Defense Advanced Research Projects Agency (DARPA) as the program manager for their information innovation office (I20).
July 2016
In July 2016, Hava Siegelmann was awarded the Hebb Award from the International Neural Network Society.
2008
From 2008 to 2010 (2 years), Hava Siegelmann was a researcher at Harvard University.
2001
From 2001 to 2001 (1 year), Hava Siegelmann was an assistant professor at the Massachusetts Institute of Technology.
2001
In 2001 (to present), Hava Siegelmann became 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
1994
From 1994 to 2000 (6 years), Hava Siegelmann served as the head of information systems engineering for the Israel Institute of Technology.
July 23, 1993
Hava Siegelmann is the creator of Super-Turing computation theory. Her theory was published in Science in 1993 for her Ph.D thesis at Rutgers University.
July 23, 1991
Hava Siegelmann attended Rutgers University from 1991 to 1993 where she completed her Ph.D in computer science.
1991
Hava Siegelmann attended The Hebrew University from 1991 to 1992 where she completed a master of science degree in computer science.
1984
Hava Siegelmann attended the Israel Institute of Technology from 1984 to 1988, and graduated with a bachelor of arts degree in computer science.
In 2016, Hava Siegelmann was awarded the Hebb Award from the International Neural Network Society. She won the award for her contributions to biological learning, and it was presented to her in July 2016 at the World Conference on Computational Intelligence in Vancouver, British ColumbiaBritish Columbia, Canada.
Hava Siegelmann is a computer scientist, researcher, and program manager for the Information Innovation Office (I2O) of the Defense Advanced Research Projects AgencyDefense Advanced Research Projects Agency (DARPADARPA). Siegelmanns 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.
In 2016, Hava Siegelmann was awarded the Hebb Award from the International Neural Network Society. She won the award for her contributions to biological learning, and it was presented to her in July 2016 at the World Conference on Computational Intelligence in Vancouver, British Columbia, CanadaCanada.
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 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 TuringAlan Turing, and attributes her success building her theory to being young and curious:
In 2016, Hava Siegelmann was awarded the Hebb Award from the International Neural Network Society. She won the award for her contributions to biological learning, and it was presented to her in July 2016 at the World Conference on Computational Intelligence in VancouverVancouver, British Columbia, Canada.
Hava Siegelmann attended Rutgers UniversityRutgers University from 1991 to 1993 where she completed her Ph.D in computer science. Her Ph.D thesis was titled "Foundations of Recurrent Neural Networks" and was published in the journal Science in October of 1993, and was written under the direction of Professor Eduardo D. Sontag.
Her theory details an adaptive computational system that learningslearns and evolves at is executes using neural networks. When describing what her Super-Turing computational model offers, Siegelmann says:
Computer scientist
A computer scientist, researcher, and program manager for the Information Innovation Office (I2O) of the Defense Advanced Research Projects Agency (DARPA) where she oversees their Guarenteeing AI Robustness Against Deception (GARD) program and their Lifelong Learning Machines (L2M) program.
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 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 attend 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".
Hava Siegelmann attended Rutgers University from 1991 to 1993 where she completed her Ph.D in computer science. Her Ph.D thesis was titled "Foundations of Recurrent Neural Networks" and was published in the journal Science in October of 1993, and was written under the direction of Professor Eduardo D. Sontag.
In July 2016 joined the Defense Advanced Research Projects Agency (DARPA) as the program manager for their information innovation office (I20). Her role is to develop programs for advancing the intelligence of computerized devices, life-long learning machines, context-aware adaptivity, and user-centered applications.
In 2019, Siegelmann also created the Guarenteeing AI Robustness Against Deception (GARD) program forwith DARPA. The GARD program was created to research the vulnerability of machine learning (ML) platforms and create more secure ML platforms through making them less vulnerable to adversarial deception attacks. Siegelmann made the following comments regarding the purpose of the GARD program:
Hava Siegelmann also oversees the Lifelong Learning Machines (L2M) program for DARPA that launched in 2019. The L2M program focuses on two primary areas: developing computational frameworks for applying learned lessons from new data or circumstances, and to find applicable examples of how biological systems naturally improve and adapt to their environments. Siegelmann made the following comments regarding the L2M program:
Life has had billions of years to develop approaches for learning from experience. There are almost certainly some secrets there that can be applied to machines so they can be not just computational tools to help us solve problems but responsive and adaptive collaborators.
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 shortlyfor afterher shethesis completedto obtain her Ph.D at Rutgers University,. andThe 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:
A generic Approach for Identification of Event Related Brain Potentials via a Competative Neural Network Structure
Daniel Lange, Hava Siegelmann, Hillel Pratt, Gideon Inbar
Journal
A Multi-Agent System that Attains Longevity via Death
Megan Olsen, Hava T Siegelmann
Journal
A Support Vector Method for Clustering
Asa Ben-Hur, David Horn, Hava T Siegelmann, Vladimir Vapnik
Journal
A Support Vector Method for Hierarchical Clustering
Asa Ben-Hur, David Horn, Hava T Siegelmann, Vladimir Vapnik
Journal
Active Information Retrieval
Tommi Jaakkola, Hava Siegelmann
Journal
Adaptive Multi-Modal Sensors
Kyle I Harrington, Hava T Siegelmann
Journal
An Integrated Symbolic and Neural Network Architecture for Machine Learning in the Domain of Nuclear Engineering
Ephraim Nissan, Hava Siegelmann, Alex Galperin
Journal
Applying Modular Networks and Fuzzy-Logic controllers to nonlinear Flexible Structures
Hava T Siegeimam, Azmon Ofki, Hugo Guterman+
Journal
Artificial Death for Attaining System Longevity
Megan Olsen, Hava Siegelmann
Journal
Attractor Systems and analog Computation
C Jain, R K Jain, Hava T Siegelmann, Shmuel Fishman
Journal
April 1998, 1998
BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python
Hananel Hazan*, Daniel J. Saunders*, Hassaan Khan, Devdhar Patel, Darpan T. Sanghavi, Hava T. Siegelmann and Robert Kozma
Journal
December 12, 2018
Computation by Dynamical Systems
Hava T Siegelmaiin
Journal
Computation in Gene Networks
Asa Ben-Hur, Hava T Siegelmann
Journal
Development of Physical Super-Turing Analog Hardware
A. Steven Younger, Emmett Redd, Hava Siegelmann
Chapter
2014
Emotional Robotics: Tug of War
David Grant Cooper, Dov Katz, Hava T Siegelmann
Journal
Emotions for Strategic Real-Time Systems
Megan M Olsen, Kyle Harrington, Hava T Siegelmann
Journal
Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks
Javier Burroni, P. Taylor, Cassian Corey, Tengiz Vachnadze, and Hava Siegelmann
Journal
February 27, 2017
Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning
Adam A. Kohan, Edward A. Rietman, Hava T. Siegelmann
Journal
August 9, 2018
Exact Neural Inference Over Graphical Models
Lars E Holzman, Hava T Siegelmann
Journal
EyeFrame: real-time memory aid improves human multitasking via domain-general eye tracking procedures
P. Taylor, Ze He, Noah Bilgrien, and Hava Siegelmann
Journal
September 2, 2015
Gibbs free energy as a measure of complexity correlates with time within C. elegans embryonic development. - PubMed - NCBI
McGuire SH, Rietman EA, Siegelmann, Tuszynski JA
Journal
September 19, 2017
Gibbs Free Energy, a thermodynamic measure of protein-protein interactions, correlates with neurologic disability
Michael Keegan, Hava T Siegelmann, Edward A Rietman, Giannola Lakka Klement
Journal
January 22, 2019
High Order Eigentensors as Symbolic Rules in Competitive Learning
Hod Lipson, Hava T Siegelmann
Journal
2000
Human strategies for multitasking, search, and control improved via real-time memory aid for gaze location
P. Taylor, Ze He, Noah Bilgrien, and Hava Siegelmann
Journal
September 7, 2015
Input-Driven Dynamic Attractors
Lars E Holzman, Hava T Siegelmann
Journal
A computer scientist, researcher, and program manager for the Information Innovation Office (I2O) of the Defense Advanced Research Projects Agency (DARPA) where she oversees their Guaranteeing AI Robustness Against Deception (GARD) program and their Lifelong Learning Machines (L2M) program.
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 attend The Hebrew University from 1991 to 1992 where she completed a master of science degree in computer science.
Hava Siegelmann attended Rutgers University from 1991 to 1993 where she completed her Ph.D in computer science.
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 became 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 July 2016 joined the Defense Advanced Research Projects Agency (DARPA) as the program manager for their information innovation office. Her role is to develop programs for advancing the intelligence of computerized devices, life-long learning machines, context-aware adaptivity, and user-centered applications.
In 2019, Siegelmann created the Guarenteeing AI Robustness Against Deception (GARD) program for DARPA. The GARD program was created to research the vulnerability of machine learning (ML) platforms and create more secure ML platforms through making them less vulnerable to adversarial deception attacks. Siegelmann made the following comments regarding the purpose of the GARD program:
The GARD program seeks to prevent the chaos that could ensue in the near future when attack methodologies, now in their infancy, have matured to a more destructive level. We must ensure ML is safe and incapable of being deceived. The kind of broad scenario-based defense we’re looking to generate can be seen, for example, in the immune system, which identifies attacks, wins and remembers the attack to create a more effective response during future engagements.
In 2016, Hava Siegelmann was awarded the Hebb Award from the International Neural Network Society. She won the award for her contributions to biological learning, and it was presented to her in July 2016 at the World Conference on Computational Intelligence in Vancouver, British Columbia, Canada.
Hava Siegelmann is one of the co-creators of support vector clustering; a popular clustering algorithm used in industry applications.
Hava Siegelmann is the creator of Super-Turing computation theory. Her theory was published in Science in 1993 shortly after she completed her Ph.D at Rutgers University, and 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:
I was young enough to be curious, wanting to understand why the Turing model looked really strong. I tried to prove the conjecture that neural networks are very weak and instead found that some of the early work was faulty. I was surprised to find out via mathematical analysis that the neural models had some capabilities that surpass the Turing model. So I re-read Turing and found that he believed there would be an adaptive model that was stronger based on continuous calculations.
Her theory details an adaptive computational system that learnings and evolves at is executes using neural networks. When describing what her Super-Turing computational model offers, Siegelmann says:
Each time a Super-Turing machine gets input it literally becomes a different machine,” Siegelmann says. “You don’t want this for your PC. They are fine and fast calculators and we need them to do that. But if you want a robot to accompany a blind person to the grocery store, you’d like one that can navigate in a dynamic environment. If you want a machine to interact successfully with a human partner, you’d like one that can adapt to idiosyncratic speech, recognize facial patterns and allow interactions between partners to evolve just like we do. That’s what this model can offer.
Computational models developed using the Super-Turing computational model exhibit a 2 to the power aleph-zero possible behaviours, 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.
'Super-Turing' machine learns and evolves | Kurzweil
https://kurzweilai.net/
Web
April 9, 2012
'We Paid Little Attention to Vulnerabilities in Machine Learning Platforms': DARPA - The Sociable
Tim Hinchcliffe
Web
February 19, 2019
Can AI Systems Learn How to Learn?
Kevin McCaney
Web
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TruNews Team
Web
Artificial Intelligence Colloquium: Lifelong and Robust Machine Learning
March 26, 2019
ECE 804 - Spring 2012 - Lecture 004 with Dr. Hava Siegelmann - Feb. 24 ,2012
February 28, 2013
ECE 804 - Spring 2012 - Lecture 004 with Dr. Hava Siegelmann - Feb. 24 ,2012
February 28, 2013
Evolving Complex Systems in Biology and Medicine
January 31, 2014
Hava Siegelmann, IJCNN 2017 Plenary Talk: Understanding Some Brain's Computational Mechanisms Pt. 1
July 18, 2017
Hava Siegelmann, IJCNN 2017 Plenary Talk: Understanding Some Brain's Computational Mechanisms Pt. 2
July 20, 2017
Hava Siegelmann, IJCNN 2017 Plenary Talk: Understanding Some Brain's Computational Mechanisms Pt. 3
July 20, 2017
Irakli Beridze, Hava Siegelmann, Seán Ó hÉigeartaigh & Ehrik L. Aldana - AI Race panel
September 13, 2014
Lifelong learning machines (L2M) - Hava Siegelmann keynote at HLAI
March 22, 2019
Lifelong Learning Machines (L2M) Proposers Day
November 29, 2017
Panel Discussion: Scientific Funding for Deep Learning
June 12, 2019
Computer scientist