ACADEMIC RESEARCH & LAB
Exploration of efficient deep learning, medical image analysis, and distributed neural training architectures.
Developing robust, interpretable deep learning models for biomedical signal processing, arrhythmia detection, and clinical diagnostic assistance.
“How can deep neural networks be made robust to noisy multi-lead biosignals while offering real-time inference and clinical explainability?”
This research track is ongoing. Limitations, open questions, and future directions will be documented as experiments progress. Check back for updates or reach out to discuss.