Steven Haworth
My work centers on EEG-based machine learning for seizure detection, forecasting, and monitoring
states of consciousness in clinical populations, including epilepsy, ABI, coma, sleep,
post–cardiac arrest patients, and stroke. I derive intellectual and personal pleasure in translating neural signals into
insights that guide clinicians, and improve patient.
My current research has demonstrated high seizure detection and artifact rejection performance, with the broader goal of moving toward reliable, real-world clinical support tools rather than purely academic models.
In my work, I've had the honor to collaborate on multi-institutional research with teams from UW-Madison, Harvard, Yale, and Washington University in St. Louis, applying computer vision to EEG waveforms and spectrograms to learn preictal and epileptiform patterns for downstream seizure forecasting in long-term monitoring patients.
I’m motivated by work at the intersection of neuroscience, machine learning, and anything patient centered. I’m always looking to push the boundary between theory and practice and build systems that genuinely improve quality of life.
My current research has demonstrated high seizure detection and artifact rejection performance, with the broader goal of moving toward reliable, real-world clinical support tools rather than purely academic models.
In my work, I've had the honor to collaborate on multi-institutional research with teams from UW-Madison, Harvard, Yale, and Washington University in St. Louis, applying computer vision to EEG waveforms and spectrograms to learn preictal and epileptiform patterns for downstream seizure forecasting in long-term monitoring patients.
I’m motivated by work at the intersection of neuroscience, machine learning, and anything patient centered. I’m always looking to push the boundary between theory and practice and build systems that genuinely improve quality of life.