Dakotah Lambert



I received a PhD from Stony Brook University for my work in Mathematical Linguistics. Through algebraic and logical analysis, I aim to understand the structures that arise in communication between and among humans and computers, including natural languages (human–human communication), programming languages (human–computer communication), and signaling protocols (computer–computer communication). Parameterization and decomposition of these systems arise out of their structure and inform mechanisms for recognition and learning. I have also applied these techniques to pattern-learning by neural networks, which provides insight and explainability for such models.


Over the years I have been building an interactive theorem-prover for subregular logics. Implemented in Haskell as both a library and supporting command-line tools, the Language Toolkit (LTK) enables somewhat easy translation from constraints to automata (and, in some cases, the reverse translation). For any regular language, the LTK can also determine which of several subregular classes that language is in, including those defined by logical properties or those induced by user-specified pseudovarieties of semigroups or monoids.