Jessilyn Dunn

Research Interests

Use of large-scale biomedical datasets to model and guide personalized therapies.

Bio

Developing new tools and infrastructure for multi-modal biomedical data integration to drive precision/personalized methods for early detection, intervention, and prevention of disease.

Education

• Ph.D. Georgia Institute of Technology, 2015

Positions

• Assistant Professor of Biomedical Engineering
• Assistant Professor of Biostatistics & Bioinformatics
• Assistant Professor in the Department of Electrical and Computer Engineering
• Member in the Duke Clinical Research Institute

Courses Taught

• ISS 796T: Bass Connections Information, Society & Culture Research Team
• ISS 795T: Bass Connections Information, Society & Culture Research Team
• ISS 396T: Bass Connections Information, Society & Culture Research Team
• ISS 395T: Bass Connections Information, Society & Culture Research Team
• ISS 290S: Special Topics in Information Science + Studies
• HLTHPOL 796T: Bass Connections Health Policy & Innovation Research Team
• HLTHPOL 795T: Bass Connections Health Policy & Innovation Research Team
• HLTHPOL 396T: Bass Connections Health Policy & Innovation Research Team
• HLTHPOL 395T: Health Policy & Innovation Research Team
• EGR 393: Research Projects in Engineering
• BME 899: Special Readings in Biomedical Engineering
• BME 792: Continuation of Graduate Independent Study
• BME 791: Graduate Independent Study
• BME 590: Special Topics in Biomedical Engineering
• BME 580: An Introduction to Biomedical Data Science (GE)
• BME 494: Projects in Biomedical Engineering (GE)
• BME 493: Projects in Biomedical Engineering (GE)
• BME 290: Intermediate Topics (GE)
• BIOSTAT 707: Statistical Methods for Learning and Discovery

Publications

• Cunningham JW, Abraham WT, Bhatt AS, Dunn J, Felker GM, Jain SS, et al. Artificial Intelligence in Cardiovascular Clinical Trials. Journal of the American College of Cardiology. 2024 Nov;84(20):2051–62.
• Jiang S, Ashar P, Shandhi MMH, Dunn J. Demographic reporting in biosignal datasets: a comprehensive analysis of the PhysioNet open access database. The Lancet Digital health. 2024 Nov;6(11):e871–8.
• Armoundas AA, Ahmad FS, Bennett DA, Chung MK, Davis LL, Dunn J, et al. Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation Genomic and precision medicine. 2024 Jun;17(3):e000095.
• Shin S, Kowahl N, Hansen T, Ling AY, Barman P, Cauwenberghs N, et al. Real-world walking behaviors are associated with early-stage heart failure: a Project Baseline Health Study. J Card Fail. 2024 Apr 4;
• Dunn J, Coravos A, Fanarjian M, Ginsburg GS, Steinhubl SR. Remote digital health technologies for improving the care of people with respiratory disorders. Lancet Digit Health. 2024 Apr;6(4):e291–8.
• Singh K, Armstrong SC, Wagner BE, Counts J, Skinner A, Kay M, et al. Physical activity and sleep changes among children during the COVID-19 pandemic. NPJ Digit Med. 2024 Mar 16;7(1):70.
• Shandhi MMH, Singh K, Janson N, Ashar P, Singh G, Lu B, et al. Assessment of ownership of smart devices and the acceptability of digital health data sharing. NPJ digital medicine. 2024 Feb;7(1):44.
• Holko M, Lunt C, Dunn J. Session Introduction: Digital health technology data in biocomputing: Research efforts and considerations for expanding access (PSB2024). Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing. 2024 Jan;29:163–9.
• Wang WK, Yang J, Hershkovich L, Jeong H, Chen B, Singh K, et al. Addressing wearable sleep tracking inequity: a new dataset and novel methods for a population with sleep disorders. In: Proceedings of Machine Learning Research. 2024. p. 380–96.
• Lauer S, Luo J, Lazar-Stefanita L, Zhang W, McCulloch LH, Fanfani V, et al. Context-dependent neocentromere activity in synthetic yeast chromosome VIII. Cell Genomics. 2023 Nov 8;3(11).
• Lederer L, Breton A, Jeong H, Master H, Roghanizad AR, Dunn J. The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program. JMIR mHealth and uHealth. 2023 Nov;11:e45103.
• Pasquale DK, Welsh W, Olson A, Yacoub M, Moody J, Barajas Gomez BA, et al. Scalable Strategies to Increase Efficiency and Augment Public Health Activities During Epidemic Peaks. J Public Health Manag Pract. 2023 Nov;29(6):863–73.
• Charlton PH, Allen J, Bailón R, Baker S, Behar JA, Chen F, et al. The 2023 wearable photoplethysmography roadmap. Physiological measurement. 2023 Nov;44(11).
• Gill JM, Chico TJ, Doherty A, Dunn J, Ekelund U, Katzmarzyk PT, et al. Potential impact of wearables on physical activity guidelines and interventions: opportunities and challenges. British journal of sports medicine. 2023 Oct;57(19):1223–5.
• Charpignon M-L, Carrel A, Jiang Y, Kwaga T, Cantada B, Hyslop T, et al. Going beyond the means: Exploring the role of bias from digital determinants of health in technologies. PLOS Digit Health. 2023 Oct;2(10):e0000244.
• Chico TJ, Stamatakis E, Ciravegna F, Dunn J, Redwood S, Al-Lamee R, et al. Device-based measurement of physical activity in cardiovascular healthcare: possibilities and challenges. British journal of sports medicine. 2023 Oct;57(19):1225–6.
• Dunn J, Singh K, Armstrong S, Wagner B, Counts J, Skinner A, et al. Physical activity and sleep changes among children with obesity during a period of school closures related to the COVID-19 pandemic. 2023.
• Jiang Y, Spies C, Magin J, Bhosai SJ, Snyder L, Dunn J. Investigating the accuracy of blood oxygen saturation measurements in common consumer smartwatches. PLOS Digit Health. 2023 Jul;2(7):e0000296.
• Sheikh AB, Sobotka PA, Garg I, Dunn JP, Minhas AMK, Shandhi MMH, et al. Blood Pressure Variability in Clinical Practice: Past, Present and the Future. J Am Heart Assoc. 2023 May 2;12(9):e029297.
• Chikwetu L, Daily S, Mortazavi BJ, Dunn J. Automated Diet Capture Using Voice Alerts and Speech Recognition on Smartphones: Pilot Usability and Acceptability Study. JMIR formative research. 2023 May;7:e46659.
• Chikwetu L, Miao Y, Woldetensae MK, Bell D, Goldenholz DM, Dunn J. Does deidentification of data from wearable devices give us a false sense of security? A systematic review. The Lancet Digital health. 2023 Apr;5(4):e239–47.
• Ke Wang W, Cesnakova L, Goldsack JC, Dunn J. Defining the Digital Measurement of Scratching During Sleep or Nocturnal Scratching: Review of the Literature. Journal of medical Internet research. 2023 Apr;25:e43617.
• Hughes A, Shandhi MMH, Master H, Dunn J, Brittain E. Wearable Devices in Cardiovascular Medicine. Circulation research. 2023 Mar;132(5):652–70.
• Popham S, Burq M, Rainaldi EE, Shin S, Dunn J, Kapur R. An Algorithm to Classify Real-World Ambulatory Status From a Wearable Device Using Multimodal and Demographically Diverse Data: Validation Study. JMIR biomedical engineering. 2023 Mar;8:e43726.
• Holko M, Lunt C, Dunn J. Session Introduction: Digital health technology data in biocomputing: Research efforts and considerations for expanding access. In: Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing. 2023. p. 1–6.
• Eom S, Kim S, Jiang Y, Chen RJ, Roghanizad AR, Rosenthal MZ, et al. Investigation of Thermal Perception and Emotional Response in Augmented Reality using Digital Biomarkers: A Pilot Study. In: Proceedings - 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2023. 2023. p. 170–3.
• Erickson ML, North R, Counts J, Wang W, Porter Starr KN, Wideman L, et al. Nightshift imposes irregular lifestyle behaviors in police academy trainees. Sleep Adv. 2023;4(1):zpad038.
• Chen B, Yang J, Wang KW, Jeong H, Ashar P, Hershkovich L, et al. Neurological Outcome Prediction After Cardiac Arrest: A Multi-Level Deep Learning Approach with Feature and Decision Fusion. In: Computing in Cardiology. 2023.
• Lederer L, Breton A, Jeong H, Master H, Roghanizad AR, Dunn J. Considerations while using Fitbit Data in the All of Us Research Program: Tutorial (Preprint). JMIR Publications Inc. 2022.
• Shandhi MMH, Dunn JP. AI in medicine: Where are we now and where are we going? Cell reports Medicine. 2022 Dec;3(12):100861.
• Popham S, Burq M, Rainaldi EE, Shin S, Dunn J, Kapur R. An Algorithm to Classify Real-World Ambulatory Status From a Wearable Device Using Multimodal and Demographically Diverse Data: Validation Study (Preprint). JMIR Publications Inc. 2022.
• Ke Wang W, Cesnakova L, Goldsack JC, Dunn J. Defining the Digital Measurement of Scratching During Sleep or Nocturnal Scratching: Review of the Literature (Preprint). JMIR Publications Inc. 2022.
• Wang WK, Chen I, Hershkovich L, Yang J, Shetty A, Singh G, et al. A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications. Sensors (Basel, Switzerland). 2022 Oct;22(20):8016.
• Shandhi MMH, Cho PJ, Roghanizad AR, Singh K, Wang W, Enache OM, et al. A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19. NPJ Digit Med. 2022 Sep 1;5(1):130.
• Jiang Y, Wang W, Scargill T, Rothman M, Dunn J, Gorlatova M. Digital biomarkers reflect stress reduction after Augmented Reality guided meditation: A feasibility study. In: DigiBiom 2022 - Proceedings of the 2022 Emerging Devices for Digital Biomarkers. 2022. p. 29–34.
• Erickson ML, Wang W, Counts J, Redman LM, Parker D, Huebner JL, et al. Field-Based Assessments of Behavioral Patterns During Shiftwork in Police Academy Trainees Using Wearable Technology. J Biol Rhythms. 2022 Jun;37(3):260–71.
• Goergen CJ, Tweardy MJ, Steinhubl SR, Wegerich SW, Singh K, Mieloszyk RJ, et al. Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data. Vol. 24. 2022.
• Cho PJ, Yi J, Ho E, Shandhi MMH, Dinh Y, Patil A, et al. Demographic Imbalances Resulting From the Bring-Your-Own-Device Study Design. JMIR Mhealth Uhealth. 2022 Apr 8;10(4):e29510.
• Dunn J, Shandhi MH, Cho P, Roghanizad A, Singh K, Wang W, et al. A Method for Intelligent Allocation of Diagnostic Testing by Leveraging Data from Commercial Wearable Devices: A Case Study on COVID-19. Res Sq. 2022 Apr 1;
• Scargill T, Chen Y, Eom S, Dunn J, Gorlatova M. Environmental, User, and Social Context-Aware Augmented Reality for Supporting Personal Development and Change. In: Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022. 2022. p. 155–62.
• Shen Y, Dunn J, Zavlanos MM. Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health. In: Proceedings of the IEEE Conference on Decision and Control. 2022. p. 144–9.
• Omidvar S, Roghanizad AR, Chikwetu L, Ash G, Dunn J, Mortazavi BJ. Enhancing Continuous Glucose Monitoring-based Eating Detection with Wearable Biomarkers. In: BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, Symposium Proceedings. 2022.
• Grzesiak E, Bent B, McClain MT, Woods CW, Tsalik EL, Nicholson BP, et al. Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset. JAMA Netw Open. 2021 Sep 1;4(9):e2128534.
• Shandhi MMH, Goldsack JC, Ryan K, Bennion A, Kotla AV, Feng A, et al. Recent Academic Research on Clinically Relevant Digital Measures: Systematic Review. Journal of medical Internet research. 2021 Sep;23(9):e29875.
• Shandhi MMH, Wang WK, Dunn J. Taking the time for our bodies: How wearables can be used to assess circadian physiology. Cell reports methods. 2021 Aug;1(4):100067.
• Bent B, Cho PJ, Henriquez M, Wittmann A, Thacker C, Feinglos M, et al. Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches. NPJ Digit Med. 2021 Jun 2;4(1):89.
• Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. 2021 Jun;9(1).
• Dunn J, Kidzinski L, Runge R, Witt D, Hicks JL, Schüssler-Fiorenza Rose SM, et al. Wearable sensors enable personalized predictions of clinical laboratory measurements. Nature medicine. 2021 Jun;27(6):1105–12.
• Cho PJ, Yi J, Ho E, Shandhi MMH, Dinh Y, Patil A, et al. Demographic Imbalances Resulting From the Bring-Your-Own-Device Study Design (Preprint). JMIR Publications Inc. 2021.
• Bent B, Enache OM, Goldstein B, Kibbe W, Dunn JP. Reply: Matters Arising 'Investigating sources of inaccuracy in wearable optical heart rate sensors'. NPJ Digit Med. 2021 Feb 26;4(1):39.
• Bent B, Sim I, Dunn JP. Digital Medicine Community Perspectives and Challenges: Survey Study. JMIR mHealth and uHealth. 2021 Feb;9(2):e24570.
• Bent B, Lu B, Kim J, Dunn JP. Biosignal Compression Toolbox for Digital Biomarker Discovery. Sensors (Basel, Switzerland). 2021 Jan;21(2):E516.
• Bent B, Henriquez M, Dunn J. Cgmquantify: Python and R Software Packages for Comprehensive Analysis of Interstitial Glucose and Glycemic Variability from Continuous Glucose Monitor Data. IEEE open journal of engineering in medicine and biology. 2021 Jan;2:263–6.
• Bent B, Dunn JP. Wearables in the SARS-CoV-2 Pandemic: What Are They Good for? JMIR mHealth and uHealth. 2020 Dec;8(12):e25137.
• Bent B, Dunn JP. Wearables in the SARS-CoV-2 Pandemic: What Are They Good for? (Preprint). 2020 Oct 19;
• Bent B, Sim I, Dunn JP. Digital Medicine Community Perspectives and Challenges: Survey Study (Preprint). 2020 Sep 24;
• Bent B, Dunn JP. Optimizing sampling rate of wrist-worn optical sensors for physiologic monitoring. Journal of clinical and translational science. 2020 Aug;5(1):e34.
• Bent B, Wang K, Grzesiak E, Jiang C, Qi Y, Jiang Y, et al. The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data. Journal of clinical and translational science. 2020 Jul;5(1):e19.
• Jiang Y, Qi Y, Wang WK, Bent B, Avram R, Olgin J, et al. EventDTW: An Improved Dynamic Time Warping Algorithm for Aligning Biomedical Signals of Nonuniform Sampling Frequencies. Sensors (Basel, Switzerland). 2020 May;20(9):E2700.
• Bent B, Goldstein BA, Kibbe WA, Dunn JP. Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ Digit Med. 2020 Feb 10;3(1):18.
• Goldsack JC, Coravos A, Bakker JP, Bent B, Dowling AV, Fitzer-Attas C, et al. Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs). NPJ digital medicine. 2020 Jan;3:55.
• Bent B, Goldstein BA, Kibbe WA, Dunn JP. Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ Digit Med. 2020;3:18.
• Cho PJ, Singh K, Dunn J. Roles of artificial intelligence in wellness, healthy living, and healthy status sensing. In: Artificial Intelligence in Medicine: Technical Basis and Clinical Applications. 2020. p. 151–72.
• Pantell MS, Baer RJ, Torres JM, Felder JN, Gomez AM, Chambers BD, et al. Associations between unstable housing, obstetric outcomes, and perinatal health care utilization. American journal of obstetrics & gynecology MFM. 2019 Nov;1(4):100053.
• Zhou W, Sailani MR, Contrepois K, Zhou Y, Ahadi S, Leopold SR, et al. Longitudinal multi-omics of host-microbe dynamics in prediabetes. Nature. 2019 May;569(7758):663–71.
• Schüssler-Fiorenza Rose SM, Contrepois K, Moneghetti KJ, Zhou W, Mishra T, Mataraso S, et al. A longitudinal big data approach for precision health. Nature medicine. 2019 May;25(5):792–804.
• Jiang C, Faroqi L, Palaniappan L, Dunn J. Estimating personal resting heart rate from wearable biosensor data. In: 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. 2019.
• Witt D, Kellogg R, Snyder M, Dunn J. Windows Into Human Health Through Wearables Data Analytics. Current opinion in biomedical engineering. 2019 Mar;9:28–46.
• Rego S, Dagan-Rosenfeld O, Zhou W, Sailani MR, Limcaoco P, Colbert E, et al. High-frequency actionable pathogenic exome variants in an average-risk cohort. Cold Spring Harbor molecular case studies. 2018 Dec;4(6):a003178.
• Dunn J, Runge R, Snyder M. Wearables and the medical revolution. Personalized Medicine. 2018 Sep;15:429–48.
• Kellogg RA, Dunn J, Snyder MP. Personal Omics for Precision Health. Circulation Research. 2018 Apr;122:1169–71.
• Li X, Dunn J, Salins D, Zhou G, Zhou W, Schüssler-Fiorenza Rose SM, et al. Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information. Kirkwood T, editor. PLOS Biology. 2017 Jan;15:e2001402–e2001402.
• Dunn J, Simmons R, Thabet S, Jo H. The role of epigenetics in the endothelial cell shear stress response and atherosclerosis. The International Journal of Biochemistry & Cell Biology. 2015 Oct;67:167–76.
• Dunn J, Thabet S, Jo H. Flow-Dependent Epigenetic DNA Methylation in Endothelial Gene Expression and Atherosclerosis. Arteriosclerosis, thrombosis, and vascular biology. 2015 Jul;35(7):1562–9.
• Dunn J, Qiu H, Kim S, Jjingo D, Hoffman R, Kim CW, et al. Flow-dependent epigenetic DNA methylation regulates endothelial gene expression and atherosclerosis. Journal of Clinical Investigation. 2014 Jul;124:3187–99.
• Annaluru N, Muller H, Mitchell LA, Ramalingam S, Stracquadanio G, Richardson SM, et al. Total Synthesis of a Functional Designer Eukaryotic Chromosome. Science. 2014 Apr;344:55–8.
• Annaluru N, Muller H, Mitchell LA, Ramalingam S, Stracquadanio G, Richardson SM, et al. Total synthesis of a functional designer eukaryotic chromosome (Science (2014) 344, 6179 (55-58)). Science. 2014 Jan 1;344(6181).
• Tarbell JM, Shi Z-D, Dunn J, Jo H. Fluid Mechanics, Arterial Disease, and Gene Expression. Annual Review of Fluid Mechanics. 2014 Jan;46:591–614.
• Dunn J, Gutbrod S, Webb A, Pak A, Jandu SK, Bhunia A, et al. S-Nitrosation of arginase 1 requires direct interaction with inducible nitric oxide synthase. Molecular and Cellular Biochemistry. 2011 Sep;355:83–9.
• Santhanam L, Tuday EC, Webb AK, Dowzicky P, Kim JH, Oh YJ, et al. Decreased S -Nitrosylation of Tissue Transglutaminase Contributes to Age-Related Increases in Vascular Stiffness. Circulation Research. 2010 Jul;107:117–25.
• Dunn JP, Hadjimichael M, Isparyan Y, Manral D, Runge R. MoveIt! Smartphone Application for Promoting Healthy Living. :1–1.
• Yeh H, Dunn J, Prieto T, Luc F, Muppidi S, Delp S, et al. Consumer-grade wrist-worn PPG sensors can be used to detect differences in heart rate variability among a heterogeneous prediabetic population. :1–1.
• Dunn J, Thabet S, Jo H. Flow-Dependent Epigenetic DNA Methylation in Endothelial Gene Expression and Atherosclerosis. :9–9.

In The News

• A Marriage of AI and Photonics to Advance Imaging, Health Care and Public Safety (Jan 30, 2024 | Pratt School of Engineering)
• Fighting Disease with a Smartwatch? That’s Genius (Jan 26, 2024 | Duke Science & Technology)
• How You Can Help Scientists Better Understand COVID Variants With Wearable Devices (Jan 27, 2022 | Duke MEDx)
• Duke Celebrates Women and Girls in Science Day (Feb 10, 2021 | )
• Early Detection of COVID-19: How Your Smartwatch Could Help (Aug 25, 2020 | Duke Magnify)
• School of Medicine Forum Addresses the Role of Data Science During Times of Crisis (Jul 22, 2020 | School of Medicine)
• A COVID-19 Study for Early Detection Expands to Reach New Communities (Jun 15, 2020 | Pratt School of Engineering)
• Here'e How to Make Smartwatch Health Data Useful for Research (May 15, 2020 | )
• Using Smartphones in the Effort for Early Detection of COVID-19 (Apr 8, 2020 | Pratt School of Engineering)
• NC Survey Tracks How Residents Are Changing Behavior In Pandemic (Apr 6, 2020 | )
• Your Skin Tone Won't Affect Your Heart-Tracking Device. Your Activity Might (Feb 11, 2020 | Pratt School of Engineering)
• Jessilyn Dunn: Gaining Insights from Biomedical Big Data (Jun 5, 2018 | Duke University Pratt School of Engineering)