Organisms in the natural world display remarkable abilities of adaptation, decision making and control in uncertain, and often hostile, environments. The means by which they achieve this impressive performance is poorly understood, although there is little doubt that much of this is achieved through evolution and learning, occurring at the cellular, individual and population levels. In contrast to the natural world, the performance of current engineered systems in such tasks is brittle and inflexible.
While engineering practice cannot at this stage match the performance of biological systems, engineering principles do provide a solid theoretical foundation in domains such as learning, decision making, signal processing and control. Even though these theories were mostly developed with the ‘artificial world’ in view, my basic premise is that they have important things to tell us about the biological realm, even if they require significant extensions and modifications in order to be applicable. Equally important, biology has much to teach us about novel computational paradigms, and indeed, much of the recent progress in Machine Learning is based on biological inspiration.
The long term goal driving my research is the creation of a conceptual and mathematical framework which will contribute to understanding perception, learning, decision making and control in biological and artificial systems.