Steven Haworth
I specialize in EEG-based machine learning for seizure detection, forecasting, and monitoring
states of consciousness in clinical populations. My work spans epilepsy, acute brain injury,
coma, sleep disorders, post–cardiac arrest, and stroke patients. I translate complex neural
signals into actionable clinical insights that directly impact patient outcomes.
My research has achieved high performance in seizure detection and artifact rejection, with focus on building reliable, deployable systems rather than purely academic benchmarks. I currently have papers under review on seizure forecasting and sleep monitoring systems. I've collaborated with research teams at UW-Madison, Harvard, Yale, and Washington University in St. Louis, applying computer vision to EEG waveforms and spectrograms to identify preictal and epileptiform patterns for real-time forecasting in long-term monitoring patients.
While EEG signal processing is my specialty, I bring broad technical skills across the data science stack—optimization, statistical modeling, deep learning architectures, and large-scale data pipelines. I'm drawn to problems at the intersection of neuroscience, ML, and healthcare, but I'm equally equipped to tackle diverse data-driven challenges across domains.
I'm seeking summer 2026 internships and full-time opportunities following my December 2026 graduation, and I'm willing to relocate. I'm looking to apply specialized expertise while continuing to grow as a versatile data scientist.
My research has achieved high performance in seizure detection and artifact rejection, with focus on building reliable, deployable systems rather than purely academic benchmarks. I currently have papers under review on seizure forecasting and sleep monitoring systems. I've collaborated with research teams at UW-Madison, Harvard, Yale, and Washington University in St. Louis, applying computer vision to EEG waveforms and spectrograms to identify preictal and epileptiform patterns for real-time forecasting in long-term monitoring patients.
While EEG signal processing is my specialty, I bring broad technical skills across the data science stack—optimization, statistical modeling, deep learning architectures, and large-scale data pipelines. I'm drawn to problems at the intersection of neuroscience, ML, and healthcare, but I'm equally equipped to tackle diverse data-driven challenges across domains.
I'm seeking summer 2026 internships and full-time opportunities following my December 2026 graduation, and I'm willing to relocate. I'm looking to apply specialized expertise while continuing to grow as a versatile data scientist.