About me

Hello! My name is Mia, and I’m a recent graduate of UC San Diego, where I studied Mathematics and Computer Science. I’m currently an RA at the Salk Institute, advised by Dr. Yusi Chen, Dr. Margot Wagner and Prof. Terrence Sejnowski, where I worked on developing predicting-coding inspired, bio-plausible learning rules for deep and recurrent neural networks. Currently, I am working on a project exploring the effect of biologically-inspired inductive biases for memorization tasks in modern RNN and SSM architectures.

Broadly, I am interested in applying methods and insights from deep learning, dynamical systems theory, and functional analysis to problems in systems neuroscience. In particular, I am motivated by the questions of how learning, inference and memory (both long-term and working) are implemented in neural circuits, using RNNs as interpretable models of information processing. Conversely, I am also interested in how experimentally-observed paradigms from neuroscience can be translated into more scalable and efficient algorithms for machine learning.

Outside of the lab, I enjoy reading, playing the guitar and going to the beach!

Projects

Publications

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