I build and validate digital health systems for clinical prediction, currently focused on postoperative monitoring and women’s health applications. My work covers device validation, signal processing, ML modeling, and end-to-end clinical study design.
I’m currently a PhD Candidate at Duke University, where I’m an NSF Fellow in the Traineeship for the Advancement of Surgical Technologies, and a Duke Rhodes Information Initiative Fellow for Interdisciplinary Research.
Currently
| PhD Candidate | Duke University Department of Biomedical Engineering BIG IDEAs Lab (PI: Jessilyn Dunn) Bridging the Perioperative Monitoring Gap: Interpretable Wearable Digital Biomarkers for Recovery Modeling and Risk Prediction |
| Senior Venture Associate | Duke Capital Partners |
Selected Publications
| Lederer et al. | Wearables Anticipate Postoperative Complications: A Prospective Cohort Study medRxiv · 2026 · [preprint] |
| Lederer et al. | VitalWave: An End-to-End Open-Source High-Frequency Wearable Device and Data Collection Platform. IEEE BSN · 2025 · [paper] [code] |
| Wang et al. | Tree-based classification model for long-COVID infection prediction with age stratification using data from the national COVID cohort collaborative JAMIA Open · 2024 · [paper] |
| Lederer et al. | The Importance of Data Quality Control in Using Fitbit Device Data From the All of Us Research Program JMIR mHealth · 2023 · [paper] |
Last Updated: August 26, 2026