- Assistant Professor of Clinical Psychiatry
- University of Illinois at Chicago
- 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.
University of Illinois at Chicago (UIC)
Northwestern University Feinberg School of Medicine. Chicago, IL
Harvard University. Cambridge, MA
Digital phenotyping, Machine learning, Informatics
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