People
The Wearable & Implantable Technology (WIT) group is a Johns Hopkins Department of Biostatistics working group. It brings together faculty, students, and collaborators who develop statistical methods and software for data from wearable and implantable sensors.
Contact: Ciprian Crainiceanu (ccraini1@jhu.edu)
Leaders
Ciprian Crainiceanu
Professor of Biostatistics
Statistical methods for wearable and implantable technology and neuroimaging; co-leader of the SMART group.
Vadim Zipunnikov
Professor of Biostatistics
Statistical methods for digital health data from wearables, smartphones, and implantable devices.
John Muschelli
Associate Research Professor of Biostatistics
Open-source pipelines for wrist-worn accelerometry and for relating activity to health outcomes.
Students
See the department’s PhD and ScM student profiles for more.
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Angela Zhao
PhD student, Johns Hopkins Biostatistics (ScM 2024)
Adviser: Ciprian Crainiceanu. Wrist-worn accelerometry as a predictor of incident Alzheimer’s and Parkinson’s disease in the UK Biobank.
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Mingyuan Li
PhD student, Johns Hopkins Biostatistics (ScM 2023)
Advisers: Ciprian Crainiceanu and Martin Lindquist. Functional moments regression for accelerometry data.
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Sam Fansler
PhD student, Johns Hopkins Biostatistics
Adviser: Vadim Zipunnikov. Digital health technologies and aging research.
Alumni
Former WIT students and trainees, linked to their current positions.
Lily Koffman
Assistant Professor of Data Science, University of Virginia
PhD 2026: identity recognition and health prediction using high-resolution accelerometry.
Joe Sartini
Assistant Professor of Biostatistics & Data Science, Wake Forest University School of Medicine
PhD 2026: Bayesian methods for functional and longitudinal data, including continuous glucose monitoring.
Yu Lu
PhD, Johns Hopkins Biostatistics
Functional principal components methods for minute-level NHANES physical activity.
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Marina Hernandez
PhD, Johns Hopkins Biostatistics
Functional data analysis with applications to physical activity and cardiac surgery.
Erjia Cui
Assistant Professor of Biostatistics & Health Data Science, University of Minnesota
PhD 2023: functional data analysis methods for large-scale physical activity studies.
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Lacey Etzkorn
Assistant Scientist, Johns Hopkins Biostatistics
PhD 2022: posture classification with ECG patches and physical activity in men living with HIV.
Marta Karas
Associate Director, Statistics (Digital), Takeda
PhD 2021: statistical methods for wearable device data.
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Andrew Leroux
Assistant Professor of Biostatistics & Informatics, Colorado School of Public Health
PhD 2021: functional data methods, with applications to NHANES and UK Biobank accelerometry.
Junrui Di
Digital Medicine Statistician, Pfizer
PhD 2019: feature engineering and statistical modeling of wearable device data.
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Jacek Urbanek
Director, Biostatistics, Regeneron; Head of Digital Biomarker Data Science, Engineering, and Operations
Accelerometry methods for aging research, including free-living gait cadence and fall risk.
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Jiawei Bai
Associate Director, Quantitative Science, BioMarin Pharmaceutical
PhD 2017: statistical methods for wearable devices in epidemiological studies.
Many WIT members share code through the jhuwit GitHub organization.