Brinnae Bent

I am a data scientist training machines to learn digital biomarkers from sensors. I am finishing my thesis on developing digital biomarkers of glycemic health in the Big Ideas Lab at Duke University.

I am currently the lead software developer at the Digital Biomarker Discovery Pipeline (DBDP), an open source software platform for developing digital biomarkers from mHealth and wearables data.

I am the creator and maintainer of two open source Python packages that enable data analysis and feature engineering of wearable sensors: cgmquantify and wearablevar.


Programming Languages & Tools

Operating Systems: Windows (primary), MAC OS (proficient), Linux (proficient)

  • Data Science & Analytics
  • Machine Learning (R and Python Sci-Kit Learn)
  • Deep Learning (PyTorch, Tensorflow)
  • Digital Biomarker Development
  • iOS Development
  • Static Website Development
  • FHIR Standards
  • Project Management

Relevant Experience

For complete list of experience, please contact me.

Lead Developer

Digital Biomarker Discovery Pipeline

Co-established the DBDP (,an open source software platform for the development of digital biomarkers using mHealth and wearabes. My role has been to lead the development of this OSS tool (compiling 50+ functions across 10 modules), lead integration with Open mHealth JSON data schemas and MD2k Cerebral Cortex using Apache/PySpark, and handle day-to-day operations and maintenance. Helped secure funding through the Chan-Zuckerberg Iniative for Open Source Science. Currently leading three teams focusing on different components of the DBDP, including the DBDP Data Compression Toolbox.


Data Scientist/Graduate Researcher

Big Ideas Lab

Led clinical studies at the Duke Endocrinology and Cardiopulmonary clinics

Employed statistical models, machine learning, and deep learning to identify digital biomarkers

Developed digital biomarker development tools and frameworks: determined wearable sensor inaccuracies, helped establish the V3 validation framework, and informed the minimum sampling rate of wearable optical HR sensors


Project Manager

DATA+ (Rhodes Information Iniative)

2019- Led project using machine learning to determine injury risk for Duke student athletes in the Michael W. Krzyzewski Human Performance Laboratory (K-Lab).

2020- Established and led project using deep learning methods for human activity recognition with wearable sensors.

Research Assistant

NSF Nanosystems Engineering Research Center

Contributed to the mission of the Advanced Self Powered Systems of Integrated Sensors and Technologies (ASSIST) to design intelligent power management for battery-free sensing, computation, and wireless communication.

Developed and fabricated photoplethysmography (PPG) probes for heart and respiratory monitoring in a health and environment monitoring medical device system with applications in asthma analysis.

Research Assistant

NC State Integrated Bionic Microsystems Laboratory (iBionicS)

Implemented electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) into a sleep monitoring device designed to replace traditional polysomnography, “SleepiBand”.

Build software for the clinical testing of the SleepiBand device.

Improved injectable biophotonic sensor for physiological monitoring of lemurs at the Duke Lemur Center.


Duke University

PhD Biomedical Engineering
Thesis: Discovering Digital Biomarkers of Glycemic Health from Wearable Sensors


Duke University

Master of Science Biomedical Engineering
Thesis: Developing a Closed Loop Feedback System for Spinal Cord Stimulation using Evoked Compound Action Potentials as a Biomarker


North Carolina State University

Bachelor of Science Biomedical Engineering

magna cum laude | Minors in Biological Sciences and Nanotechnology | University Scholar



My passion for integrating healthcare and engineering was ignited when I was a caregiver at both a nursing home for Alzheimer's patients and at a respite home for children with cerebral palsy. There, I discovered endless problems that needed to be solved. In my work (and beyond work!), I strive to solve meaningful problems that make the world a better place.

I am interested in exploring the intersection of sensors and data science/machine learning for healthcare applications. I am passionate about open source software and strive to make everything I create open-source. At heart, I am an educator: I can usually be found teaching engineering to 'K-100' learners, substitute teaching, and blogging machine learning and data science tutorials!

The intersection of art and machine learning is something that fascinates me. Check out one of my recent exhibits, "America the Beautiful: National Parks + Modern Art with a Neural Transfer Model". I am also a more "traditional" artist, with my main mediums being glasswork and painting.

I am a full-fledged adventurer. I frequently go backpacking on primitive trails, summit mountains, compete in ultra-running races, and participate in a range of adventure sports. I currently live and work in Raleigh-Durham, NC with my husband, Ryan, and our two cats, Tater and Tot.


See most up to date publication list at Google Scholar .

Manuscripts currently under peer-review/revisions

*B. Bent, I. Sim, J.P. Dunn. “Digital Medicine Community Perspectives and Challenges”.

*B. Bent, P. Cho, A. Wittman, M. Snyder, M. Crowley, M. Feinglos, J.P. Dunn. “Feasibility of Using Non-invasive Wearables to Identify Digital Biomarkers of Glycemic Health in Prediabetes”.

*M. Henriquez, J. Sumner, M. Faherty, T. Sell, B. Bent. “Machine Learning to Predict Lower Extremity Musculoskeletal Injury Risk in Student Athletes”.

*B. Bent, J.P. Dunn. “cgmquantify: A Python package for comprehensive analysis of interstitial glucose and glycemic variability from continuous glucose monitor data”.

*B. Bent, A. Young, J. Menendez, P. Vijendra, M. Marathe, W.E. Hammond. “A Systems-Orientated Approach for Tomorrow’s Electronic Health Record”.

*M. Trumpis, C.H. Chiang, A. Osborn, B. Bent, J. Li, J.A. Rogers, B. Pesaran, G. Cogan, J. Viventi. “Sufficient Sampling for Kriging Prediction of Cortical Potential in Rat, Monkey, and Human, uECoG”.

Peer-Reviewed Publications (*indicates journal pub)

*B. Bent, J.P. Dunn. “Optimizing Sampling Rate of Wrist-worn Optical Sensors for Physiologic Monitoring”. Journal of Clinical and Translational Science. (2020).

*B. Bent, K. Wang, E. Gerzesiak, C. Jiang, Y. Qi, Y. Jiang, P. Cho, K. Zingler, F. Ogbeide, A. Zhao, I. Sim, J. Dunn. “Digital Biomarker Discovery Pipeline: An open source software platform for the development of digital biomarkers using mHealth and wearables.” Journal of Clinical and Translational Science. (2020).

*Y. Jiang, Y. Qi*, K. Wang, B. Bent, R. Avram, J. Olgin, J. Dunn. “EventDTW: An Improved Dynamic Time Warping Algorithm for Aligning Biomedical Signals of Uneven Sampling Frequencies.” Sensors. (2020)

*J.C. Goldsack, A. Coravos, J. Bakker, B. Bent, A. Dowling, C. Fitzer-Attas, A. Godfrey, J.G. Godino, N. Gujar, E. Ismailova, C. Manta, B, Peterson, B. Vandendressche, W. Wood, W. Wang, J. Dunn. “Verification, Analytical Validation, and Clinical Validation (V3): The Foundation of Determining Fit-for-Purpose for Biometric Monitoring Technologies (BioMeTs)”. Nature Digital Medicine. (2020).

*B. Bent, B.A. Goldstein, W.A. Kibbe, J.P. Dunn. “Investigating Sources of Inaccuracy in Wearable Optical Heart Rate Sensors.” Nature Digital Medicine. (2020)

*CH. Chiang, S. Won, A. Orsborn, KJ. Yu, M. Trumpis, B. Bent, C. Wang, Y. Xue, S. Min, V. Woods, C. Yu, BH. Kim, SB Kim, R. Huq, J. Li, KJ. Seo, F. Vitale, H. Fang, Y. Huang, K. Shepard, B. Pesaran, JA Rogers, J. Viventi. “Development of a neural interface for high-defintion, long-term recording in rodents and nonhuman primates”. Science Translational Medicine. (2020).

B. Bent, CH. Chiang, C. Wang, N. Lad, A. Kent, J. Viventi. “Simultaneous Recording and Stimulation Instrumentation for Closed Loop Spinal Cord Stimulation.” IEEE Neural Engineering Conference Proceedings. (2019).

B. Bent, A.J. Williams, R. Bolick, K. Chiang, M. Trumpis, J. Viventi. “3D Printed Cranial Window System for Chronic μECoGRecording.” IEEE Engineering Medicine and Biology Conference Proceedings. (2018).

A.J. Williams, M. Trumpis, B. Bent, K. Chiang, J. Viventi. “A Novel μECoG Electrode Interface for Comparison of Local and Common Averaged Referenced Signals.” IEEE Engineering Medicine and Biology Conference Proceedings. (2018).

*V. Woods, M. Trumpis, B. Bent, C.H. Chiang, K. Palopoli-Trojani, C. Wang, M. Insanally, R.C. Froemke, and J. Viventi, “Chronic​reliability of μECoG arrays implanted for greater than one year in rodents,” Journal of Neural Engineering (2018).

M. Sahraee Ardakan, M. Emami, AK Fletcher, M. Trumpis, B. Bent, J. Viventi. “Learning Nonlinear Dynamical Networks in Neural Systems”. Conference on Cognitive Computational Neuroscience Proceedings. (2017).

*J. Dieffenderfer, H. Goodell, S. Mills, M. McKnight, S. Yao, F. Lin, E. Beppler, B. Bent, V. Misra, Y. Zhu, O. Oralkan, J. Strohmaier, J. Muth, D. Peden, and A. Bozkurt. “Low Power Wearable Systems for Continuous Monitoring of Environment and Health for Chronic​ Respiratory Disease.” Journal of Biomedical and Health Informatics (2016).

Laura Gonzales, Katherine Walker, Sindhuja Challa, B. Bent. “Monitoring a Skipped Heartbeat: A Real-time Premature Ventricular Contraction (PVC) Monitor”. IEEE Virtual Conference on Applications of Commercial Sensors (2016).

*B. Bent, Dr. Alper Bozkurt. “Miniaturizing Plethysmography for use in a Multifunctional Health Monitoring Device withApplications for Asthma Analysis.” State of North Carolina Undergraduate Research Journal: Explorations Vol. X (2015).

Dieffenderfer, James P., Henry Goodell, B. Bent, Eric Beppler, Rochana Jayakumar, Murat Yokus, Dr. Jesse S. Jur, and Dr.Alper Bozkurt. "Wearable Wireless Sensors for Chronic Respiratory Disease Monitoring." IEEE Body Sensor Networks (2015).

Awards & Honors

2020 | Featured Woman in STEM, 1MWIS

2020 | Invited Speaker, Digital Medicine Society Webinar Series

2020 | Participant & Speaker, Banff International Research Station Workshop Use of Wearable & Implantable Devices in Health Research [Link]

2020 | Invited Speaker, Duke Center for Health Informatics Seminar Series [Link]

2019 | Career Award, Biomedical Engineering Society (BMES) [Link]

2019 | BMES Travel Award, Duke University Biomedical Engineering Department

2019 | FHIR DevDays Global Hackathon Winner (developed mobile app with interoperability standards), HL7 FHIR [Link]

2019 | Forge Pre-doctoral Fellowship (3+ year competitive fellowship), Duke Forge

2018 | Neural Interfaces Conference Student Travel Award, Neural Interfaces Conference 2018

2018 | Broader Impacts Seminar Student Talk Competition Finalist and Award Recipient, Duke University

2017 | Steven W. Smith Fellowship, Duke University

2017 | BMES Travel Award, Duke University Biomedical Engineering Department

2016 | NIH-IMSD Biosciences Collaborative for Research Engagement (BioCoRE) Graduate Fellowship

2016 | Biomedical Engineering Scholar Award Fellowship, Duke University

2016 | Abrams Scholar ‘Researcher of the Year’ Award, North Carolina State University

2016 | Biomedical Engineering Citizenship and Service Award (Joint UNC-NC State Biomedical Engineering Department)

2016 | i4 Pitch Finalist

2015 | i4 Pitch Award Recipient

2015 | Abrams Scholars Research Fellowship, North Carolina State University

2015 | RiOT Hackathon Honorable Mention

2015 | Society of Women Engineers National Technical Poster Competition Finalist

2015 | IEEE Body Sensor Networks Best Paper Award

2015 | University of Michigan Summer Research Fellowship

2015 | Outstanding Biomedical Engineering Junior 2015 (Joint UNC-NC State Biomedical Engineering Department)

2014 | NSF Undergraduate Research Fellowship

In the News

2020 | Edge Impulse "Choosing the Optimal Sampling Rate for your DIY Heart Rate Monitor"

2020 | Duke Medical School "Early Detection of COVID-19: How Your Smartwatch Could Help"

2020 | TEC TALES "Verifying and validating the clinical usefulness of wearables"

2020 | Medical Xpress "Verifying and validating the clinical usefulness of wearable technology"

2020 | Pratt School of Engineering "Verifying and validating the clinical usefulness of wearable technology"

2020 | Spectrum News 1 (TV) "How accurate are Fitbits and Smart Watches?"

2020 | MobiHealthNews "Duke study: Activity, but not skin tone, can impact wearables’ PPG heart rate accuracy"

2020 | Pratt School of Engineering "Heart Rate Measurements of Wearable Monitors Vary by Activity, Not Skin Color"

2019 | DCHI News "MIDS Program Class Project Selected as the WINNER at HL7 FHR Developer Days Conference"

2019 | Duke Graduate School "I Taught Senior Citizens How To Control Robotics Using Their Minds: A Teaching and Learning Experience with OLLI"

2019 | Ultrarunner Podcast "Ultramarathon Daily News"

2017 | Backyard Brains "Lifelong Learners Perform Neuroscience Experiments!"

2016 | Lung Disease News "NC State Scientists Develop Wearable System to Predict, Prevent Asthma Attacks"

2015 | Joint Department of BME NC State UNC "Congratulations 2015-2016 Abrams Scholars Winners!"

2015 | NC State ECE Department News "Researchers Gain Best Paper Award- Prove Need for ASSIST Technologies"

2014 | NC State Technician "New Smart Bandage Device Measures Quality of Sleep"