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  2. John Zulueta

John Zulueta MD

John Zulueta
Designation
  • Assistant Professor of Clinical Psychiatry
  • University of Illinois at Chicago
Contact Information
  • jzulueta [at] uic.edu
  • Neuropsychiatric Institute (NPI)
    912 S. Wood St.
    Department of Psychiatry (MC 913)
    Chicago IL 60612
  • Room #:434

Dr. Zulueta received his medical degree from Northwestern University. He completed his psychiatry residency training at UIC and stayed on to complete a fellowship in clinical informatics.

He is interested in the application of new technologies and data science to the problems of psychiatry.

  • Residency
    University of Illinois at Chicago (UIC)
  • Medical School
    Northwestern University Feinberg School of Medicine. Chicago, IL
  • Undergraduate School
    Harvard University. Cambridge, MA
  • psychiatry

    Digital phenotyping, Machine learning, Informatics

  • BiAffect

    Dr. Zulueta is a collaborator on the BiAffect project which aims to build digital phenotypes of mood and cognitive function from mobile phone keyboard kinematics.


  • Rashidisabet, H., Thomas, P. J., Ajilore, O., Zulueta, J., Moore, R. C., Leow, A. (in press) A systems biology approach to the digital behaviorome. Current Opinion in Systems Biology.

    Vesel, C., Rashidisabet, H., Zulueta, J., Stange, J. P., Duffecy, J., Hussain, F., Piscitello, A., Bark, J., Langenecker, S. A., Young, S., Mounts, E., Omberg, L., Nelson, P. C., Moore, R. C., Koziol, D., Bourne, K., Bennett, C. C., Ajilore, O., Demos, A. P., & Leow, A. (2020). Effects of mood and aging on keystroke dynamics metadata and their diurnal patterns in a large open-science sample: A BiAffect iOS study. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocaa057

    Zulueta, J., Leow, A. D., & Ajilore, O. (2020). Real-Time Monitoring: A Key Element in Personalized Health and Precision Health. FOCUS, 18(2), 175–180. https://doi.org/10.1176/appi.focus.20190042

    Hussain, F., Stange, J. P., Langenecker, S. A., McInnis, M. G., Zulueta, J., Piscitello, A., Cao, B., Huang, H., Yu, P. S., Nelson, P., Ajilore, O. A., & Leow, A. (2019). Passive Sensing of Affective and Cognitive Functioning in Mood Disorders by Analyzing Keystroke Kinematics and Speech Dynamics. In H. Baumeister & C. Montag (Eds.), Digital Phenotyping and Mobile Sensing (pp. 161–183). Springer. https://doi.org/10.1007/978-3-030-31620-4_10

    Stange, J. P., Zulueta, J., Langenecker, S. A., Ryan, K. A., Piscitello, A., Duffecy, J., Mcinnis, M. G., Nelson, P., Ajilore, O., & Leow, A. (2018). Let your fingers do the talking: Passive typing instability predicts future mood outcomes. Bipolar Disorders. https://doi.org/10.1111/bdi.12637

    Zulueta, J., Piscitello, A., Rasic, M., Easter, R., Babu, P., Langenecker, S. A., McInnis, M., Ajilore, O., Nelson, P. C., Ryan, K., & Leow, A. (2018). Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study. Journal of Medical Internet Research, 20(7), e241. https://doi.org/10.2196/jmir.9775

    Cao, B., Zheng, L., Zhang, C., Yu, P. S., Piscitello, A., Zulueta, J., Ajilore, O., Ryan, K., & Leow, A. D. (2017). DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’17, August, 747–755. https://doi.org/10.1145/3097983.3098086