Steven Haworth

Steven Haworth

  • Machine Learning Engineer, EEG/Neurotech, WashU Medicine Neurology
  • Graduate Researcher, Struck Lab, Washington University in St. Louis
  • MS Data Science, University of Wisconsin–Madison, expected December 2026

About

My work in EEG-based machine learning spans seizure detection, consciousness monitoring, and neural signal classification across epilepsy, acute brain injury, coma, sleep disorders, and post-cardiac arrest populations. I build systems designed for clinical deployment. Current work includes active collaborations with UW-Madison, Harvard, Yale, and Washington University in St. Louis.

I work across CNNs, LSTMs, large language models, computer vision, statistical modeling, mathematical optimization, and the full data lifecycle, including experiment tracking and model versioning with tools like MLflow.

Research

Seizure detection under domain shift
Spatiotemporal deep learning (CNN, BiLSTM, attention) trained on scalp EEG and validated on continuous ICU recordings, with a focus on false-alarm reduction.
Seizure forecasting in the ICU
Interpretable models that estimate near-term seizure risk from continuous EEG, with external validation on independent cohorts.
Neural correlates of consciousness
Topological and spectral signatures of high-density EEG during sleep and serial awakenings.
Sensor optimization
Mixed-integer programming for minimal EEG montages that preserve classification performance.

Publications

  • Neural signatures of conscious experience during sleep: a serial awakening study using high-density EEG

  • Predicting ICU seizures using 1 hour of cEEG

  • Duration of EEG monitoring needed to ensure a low risk of seizure recurrence in hospitalized patients

  • Frequent cannabis use and the association with seizure burden in drug-resistant epilepsy

  • Thermodynamic rigidity of harmonic brain states relates to general mental ability in juvenile myoclonic epilepsy

Abstracts and work in progress Google Scholar

Contact

0218steven@gmail.com

Email preferred for business & collaboration inquiries. Seeking full-time opportunities post-December 2026. Open to relocation.