SOTA MORIYAMA
Research Interest
My work focuses on building differentiable combinatorial solvers—primarily SAT and MaxSAT—by leveraging Deep Learning. By integrating these learnable, differentiable solvers directly into neuro-symbolic pipelines, I aim to create frameworks where symbolic reasoning and neural learning are seamlessly combined, leading to more robust and trustworthy AI systems.
Grants/Fellowship
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2026.04 - 2028.03
JSPS Research Fellowship for Young Scientists (DC2) Trustworthy Deep Models with Graph Learning Techniques Based on Logical Constraints
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2025.10 - 2025.11
SOKENDAI Student Dispatch Program Research visit to Professor Thomas Eiter's lab at TU Wien
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2025.04 - 2026.03
SOKENDAI Special Researcher (JST Spring) Neuro-Symbolic AI from Combination of Maximum Satisfiability and Graph Neural Networks