Nature-Inspired Machine Learning (NIMI)
Research Scientist
Nature-Inspired Machine Learning (NIMI)
As a Postdoctoral Researcher at the TIB ‑ Leibniz Institute for Science and Technology, he works on AI agents that learn by exploring and interacting with their environments. His work draws on enactivism, causal inference, and predictive processing to enable AI agents to autonomously discover affordances and rules. By merging these cognitive science principles with program synthesis and world models, his aim is to build systems that generalise across novel ARC‑AGI puzzles and minigames.
Dr. Andrei Aioanei
Research Scientist
Nature-Inspired Machine Learning (NIMI)
As a Postdoctoral Researcher at the TIB ‑ Leibniz Institute for Science and Technology, he works on AI agents that learn by exploring and interacting with their environments. His work draws on enactivism, causal inference, and predictive processing to enable AI agents to autonomously discover affordances and rules. By merging these cognitive science principles with program synthesis and world models, his aim is to build systems that generalise across novel ARC‑AGI puzzles and minigames.
Publications
Research Scientist
Nature-Inspired Machine Learning (NIMI)
As a Postdoctoral Researcher at the TIB ‑ Leibniz Institute for Science and Technology, he works on AI agents that learn by exploring and interacting with their environments. His work draws on enactivism, causal inference, and predictive processing to enable AI agents to autonomously discover affordances and rules. By merging these cognitive science principles with program synthesis and world models, his aim is to build systems that generalise across novel ARC‑AGI puzzles and minigames.
