1. Profile
  2. John Zulueta

John Zulueta MD

John Zulueta
  • Assistant Professor of Psychiatry
  • Director, Ambulatory Program
  • University of Illinois Chicago
Contact Information
  • jzulueta [at]
  • 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 Chicago (UIC)
  • Medical School
    Northwestern University Feinberg School of Medicine. Chicago, IL
  • Undergraduate
    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.

    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.

    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.

    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.

    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.

    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.