Alexander Huth

Alexander Huth is an Assistant Professor of Neuroscience and Computer Science at the University of Texas at Austin. His research focuses on using computational methods to model how the brain processes language and represents meaning. He leads the HuthLab, which develops quantitative approaches to understanding brain function through fMRI technology and neural network modeling.

Dr. Huth's research interests span Neuroscience, Computer Science, Artificial Intelligence, and Machine Learning, with specific focus on fMRI technology, Data visualization, and Language processing. His lab uses fMRI to record brain responses while people listen to stories or podcasts, then builds encoding models that predict those responses based on the audio and transcript. Recent work has demonstrated that neural network language models can effectively extract meaningful information from stories to map language representation across the brain, investigate why these models are so effective, and even decode language from fMRI data.

Dr. Huth has been recognized with a Sloan Research Fellowship and has published significant work in both neuroscience and artificial intelligence fields. His recent publications (2023-2024) focus on humans and language models diverging in text prediction, multimodal brain encoding models, and scaling laws for language encoding in fMRI.

As principal investigator of HuthLab, Dr. Huth leads a research team committed to open science, freely sharing datasets and providing code and tutorials on encoding models for language. His work has practical applications for people with communication disorders, as demonstrated by research on AI brain decoders that can reveal stories in people's minds and move closer to real-world use for people with aphasia.