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I’m interested in deep learning applied to protein engineering and drug discovery and developing better reasoning agents with a universal multimodal token-modified transformer that will reason on any modality or any structured or unstructured data types with the generality of universal graph-to-graph function approximation, capability to reason with boolean circuits, the tropical ergodic dynamics of such models, and the implications on tranformer ASIC development and standardization of the industry to a single arch
I’m currently thinking about composed architecture design and graph-of-thought reasoning in embedding space, with tropical attention and toric geometry methodologies, search methods with GFlowNets on GoT trajectories, etc.
I’m looking to collaborate on AI for Proteins and Medicinal Design and agentic systems that are more expressive, generalizable to OOD data, and universal G2G function approximation