Kamran Hussain
Computational neuroscience and applied machine learning researcher
I work on speech brain–computer interfaces, models of neural activity, and tools for analyzing electrophysiology data.

Selected research
Speech brain–computer interfaces
I presented work on bringing intracortical speech BCIs closer to conversational speeds at the 2025 BCI Society Meeting, and was part of the team awarded first place in the 2025 BCI Award.
Neural population forecasting
Two 2026 preprints introduce a large-scale benchmark for forecasting spiking activity and test whether forecasted firing rates help decode behavior.
SpikeLab
A Python library and agent workflow for analyzing spike data, designed so that electrophysiology analyses use vetted methods and can be reproduced.
Background
My research background spans speech neuroprosthetics at Stanford NPTL and neural activity modeling with the UCSC Braingeneers. Earlier experiences at NASA Ames and UC San Diego helped shape my interest in neural interfaces and applied machine learning.
- SURFiN fellow · Speech neuroprosthetics
- Neural activity prediction and organoid electrophysiology
Selected projects
Polaris
A hackathon prototype that uses fishing-vessel sensor data to identify early signs of engine failure. The team won first place at the 2025 Sushi Hackathon.
StatScanner
An iOS app for importing numerical data from images or CSV files, calculating statistics, and creating charts.
Outside research, I fence and enjoy science fiction.