Ignacio Cases

PhD candidate in
Computational Linguistics

Stanford Linguistics and
Stanford NLP Group

Download Resume


How is the brain able to process an infinitude of expressions composed from a small number of elements? What are the computational principles underlying symbolic manipulation in the human mind? How does symbolic computation emerge from computations in neural networks? These questions are intimately related to our ability to learn and deeply understand natural language — arguably one of the key components of human intelligence, and most certainly a required step in our journey for brain-like AI.

As a PhD candidate at Stanford, advised by Dan Jurafsky, Chris Potts and Josh Greene, I study these research questions in the intersection between Computational Linguistics, Computer Science, and Cognitive Neuroscience.

In my work, I develop Deep Reinforcement Learning models that learn compositional semantics in a systematic way. In particular, I’m interested in the role that memory plays in these architectures as a catalyst in the emergence of symbolic computation from the underlying substratum.

What's New

Papers related to Computational Linguistics

About me

Before coming to Stanford I worked during my Masters in the development and application of AI techniques to the process of automated deciphering of writing systems. My undergraduate studies in Physics (Astrophysics) made me passioned about Mathematics and specially Numerical Analysis, Machine Learning, Signal Processing, and Computer Vision.

My email is my surname at my university dot edu.