Hava Siegelmann is a professor of computer science, and a world leader in the fields of Lifelong Learning, Artificial Intelligence, Machine Learning, Neural Networks, and Computational Neuroscience. Her academic position is in the school of and at the University of Massachusetts Amherst; she is the director of the school's . She was loaned to the federal government DARPA 2016-2019 to initiate and run their most advanced AI programs including her Lifelong Learning Machine program. and Guaranteeing AI Robustness against Deceptions. She received the rarely awarded Meritorious Public Service Medal - one of the highest honors the Department of Defense agency can bestow on a private citizen.
Biography
Siegelmann is an American computer scientist who founded the field of Super-Turing computation. 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. In the early 1990s, she and Eduardo D. Sontag proposed a new computational model, the Artificial Recurrent Neural Network, 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 and her monograph, . In her Science paper, Siegelmann demonstrates how chaotic systems are now described by the Super-Turing model. This is significant since many biological systems not describable by standard means can be described as a chaotic system and can now be modeled mathematically. The theory of Super-Turing computation has attracted attention in physics, biology, and medicine. Siegelmann is also an originator of the Support Vector Clustering http://www.scholarpedia.org/article/Support_vector_clustering, a widely used algorithm in industry, for big data analytics, together with Vladimir Vapnik and colleagues. Siegelmann also introduced a new notion in the field of Dynamical Diseases, "the dynamical health", 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, 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 and is currently studying human memory and cancer 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 , and an editor in the Frontiers on Computational Neuroscience.
Publications
Papers
H.T. Siegelmann and L.E. Holtzman, "Neuronal integration of dynamic sources: Bayesian learning and Bayesian inference," Chaos: Focus issue: Intrinsic and Designed Computation: Information Processing in Dynamical Systems 20 : DOI: 10.1063/1.3491237, September 2010.
