An ML practitioner with deep specialization in EEG-based machine learning, my work 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, not just benchmarks. Current work includes papers under review on seizure forecasting and sleep monitoring, a presentation accepted at ACNS 2026, and active collaborations with UW-Madison, Harvard, Yale, and Washington University in St. Louis.

My technical range extends well beyond neuro. I work across CNNs, LSTMs, large language models, computer vision, statistical modeling, mathematical optimization, and the full data lifecycle, and I apply that range to whatever the problem demands. I treat the full ML lifecycle seriously, including experiment tracking and model versioning with tools like MLflow, and careful selection of metrics that reflect the true nature of the problem rather than those that are simply easy to optimize. I am drawn to complex, high-stakes data challenges where rigorous methodology produces tangible impact. Seeking Summer 2026 internships and full-time opportunities post-December 2026. Open to relocation.