معرفی
Javad Hashemi serves as an Adjunct Assistant Professor at Queen's University within the School of Computing (Faculty of Arts and Science), with cross-appointments at the Translational Institute of Medicine (TIME). His primary academic affiliation combines computational expertise with clinical cardiology applications.
His research focuses on cardiovascular electrophysiology, specializing in atrial fibrillation mechanisms, cardiac arrhythmia detection, and ablation therapy optimization. Key methodologies include intracardiac electrogram analysis, dominant frequency mapping, and machine learning applications for ECG/IEGM signal processing. Current work emphasizes
- Real-time arrhythmia classification systems
- Uncertainty-aware diagnostic models
- Novel ablation guidance techniques using electrogram feature analysis
- Continual learning frameworks for medical monitoring
His publication portfolio demonstrates consistent innovation in translational cardiovascular engineering, with recent work (2022-2025) advancing AI-driven ECG analysis while maintaining continuity with earlier electrophysiology research (2014-2019) on atrial fibrillation mapping and ventricular tachycardia ablation strategies.
No scientific awards were documented in the source materials.
Dr. Hashemi maintains active research collaborations across Queen's medical and engineering departments, particularly through TIME. His work integrates clinical electrophysiology data with advanced computational modeling to develop next-generation cardiac diagnostic and therapeutic tools. Current projects focus on uncertainty quantification in arrhythmia classification and continual learning systems for longitudinal patient monitoring.
