Developing learning algorithms for adaptive, nonlinear systems in uncertain and dynamic environments. This theme includes supervised, unsupervised, and reinforcement learning approaches applied to real-world systems. Particular emphasis is placed on interpretability, embodiment, and the use of machine learning to model or simulate symbolic structures, including those derived from psychoanalytic theory.
The Machine Learning Control Project explores how modern data-driven techniques can augment or replace traditional control design methodologies. While classical control theory depends heavily on first-principles modeling, many real-world systems …
View projectThe Subject of Robotics Project investigates the symbolic, structural, and material conditions under which human and artificial subjects emerge. Drawing from control systems, robotics, artificial intelligence, and Lacanian psychoanalysis, the …
View projectThe Metastimuli Project develops methods to augment human learning by transforming conventional educational content—text, audio, or video with dialogue—into metastimuli: signals dynamically correlated with the learner’s own personal information management …
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