Interests
Hava Siegelmann's research focuses on the understanding of biologically inspired computational systems. In particular, she studies the computational and dynamical complexity of neural systems as well as genetic-networks. She would love to advance toward understanding how underlying architecture brings about the dynamics that evolve into intelligent behavior, and how behavior feedback from the dynamics proceeds toward adaptation in the architecture.
Professional Activities
- Associate Editor of Frontiers in Computational Neuroscience
- Editorial Board member of the American Institute of Physics Journal Chaos: An Interdisciplinary Journal of Nonlinear Science [1999-present]
- Reviewer of the Journals (Partial list) : Journal of Theoretical Biology (Inferring Logical Models of Gene Expression Dynamics Authors: Theodore J. Perkins; Michael T. Hallett; and Leon Glass), Neural Computation, Theoretical Computer Science, J. of Complexity, Neural Networks World, Neural Networks, Connection Science, Cognitive Science, IEEE Trans on Neural Networks, Physics Review Letters, Physica D, Physica A, Scholarpedia
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