Hava Siegelmann is a national and international expert in AI, Neural networks, and Computational Neuroscience. She conducts highly interdisciplinary research in next-generation machine learning, neural networks, and computational studies of the brain - with biomedical, industrial, and defense applications. Among her contributions are the Bio-inspired Replay algorithm to enable Continual Learning, the Signal Propagation algorithm to substitute the back-propagation with forward-only, small size low power hardware that also enables learning during inference, the Support Vector Clustering algorithm for Data Science, delineating jet-lag mechanisms, identifying brain structure that leads to abstract thoughts, and the Super-Turing computation theory which has become the backbone of the latest generation of biologically inspired neural networks and lifelong learning machines.

Siegelmann has been a visiting professor at MIT, Harvard University, the Weizmann Institute, ETH, the Salk Institute, the Mathematical Science Research Institute Berkeley, and the Newton Institute Cambridge University. She consults widely with industry, focusing on BioMedicine and Defense, and serves as an industry speaker, bringing the new frontiers of AI to people of all backgrounds and interests. Siegelmann is highly active in introducing and maintaining diversity in her fields.

Siegelmann recently completed her term as a DARPA PM. One of her key initiatives, Lifelong Learning Machines (L2M), inaugurated “third-wave AI,” pushing major design innovation and a dramatic increase in AI capability. “GARD” is leading to unique advancements in assuring AI robustness against attack. “CSL” is introducing powerful methods of combined learning and information sharing on AI platforms without revealing private data. Other programs include advanced biomedical applications.

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