Nina de Lacy

Nina de Lacy, MBA, MD

Languages spoken: English

Academic Information

Departments Primary - Psychiatry

Divisions: Child & Adolescent Psychiatry

Board Certification

  • American Board of Psychiatry & Neurology (Psychiatry)

Nina de Lacy, MD, MBA, Assistant Professor, conducts research at the interface of computational science, artificial intelligence and mental health. Her laboratory develops novel computational approaches with the goal of advancing our understanding of risk trajectories, outcomes and mechanisms in mental health and substance use disorders, enabling more impactful and efficient prevention and intervention. She is particularly known for innovation in sequence data such as digital phenotyping and electronic health records and her neuroscientific work developing innovative mathematical models of brain function and its relationship with psychiatric illness. Dr. de Lacy uses artificial intelligence and machine learning approaches well-suited to problems such as individual prediction, precision psychiatry and the identification of psychiatric biotypes and biomarkers. Dr. de Lacy earned her undergraduate degree at the University of Oxford, her medical degree at UCSF and completed her Adult Psychiatry Residency (Neuroscience Track) and her Fellowship in Child and Adolescent Psychiatry at the University of Washington.

Education History

Fellowship University of Washington
 Fellow
Residency University of Washington
 Resident
Professional Medical University of California, San Francisco School of Medicine
 MD
Graduate Training Northwestern University
 MBA
Diploma Oxford University
 Postgraduate Diploma
Undergraduate Oxford University
 BA, MA

Selected Publications

Journal Article

  1. de Lacy N, Lam WY, Collins T, Danks D, Wilson FA, Smith KR, Pescosolido BA (2026). A century of suicide: Insights from long-term data in the United States. Proc Natl Acad Sci U S A, 123(18), e2519951123.
  2. de Lacy N, Ramshaw M, Lam WY (2025). Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI. Front Psychiatry, 16, 1487894.
  3. de Lacy N, Ramshaw M, Lam WY (2025). RiskPath: Explainable deep learning for multistep biomedical prediction in longitudinal data. Patterns (N Y), 6(8), 101240.
  4. de Lacy N, Ramshaw MJ (2023). Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy. Front Psychiatry, 14, 1280326.
  5. de Lacy N, Ramshaw MJ, McCauley E, Kerr KF, Kaufman J, Nathan Kutz J (2023). Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence. Transl Psychiatry, 13(1), 314.
  6. de Lacy N, Ramshaw MJ, Kutz JN (2022). Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning. Front Artif Intell, 5, 832530.
  7. de Lacy N (2021). Sex/gender differences in the neural substrate of long-term memory. Cogn Neurosci, 12, 1-2.
  8. de Lacy N, Kutz JN, Calhoun VD (2020). Sex-related differences in brain dynamism at rest as neural correlates of positive and negative valence system constructs. Cogn Neurosci, 12, 1-24.
  9. de Lacy N, McCauley E, Kutz JN, Calhoun VD (2019). Sex-related differences in intrinsic brain dynamism and their neurocognitive correlates. Neuroimage, 202, 116116.
  10. Fu Z, Tu Y, Di X, Du Y, Sui J, Biswal BB, Zhang Z, de Lacy N, Calhoun VD (2018). Transient increased thalamic-sensory connectivity and decreased whole-brain dynamism in autism. Neuroimage, 190, 191-204.
  11. de Lacy N, McCauley E, Kutz JN, Calhoun VD (2019). Multilevel Mapping of Sexual Dimorphism in Intrinsic Functional Brain Networks. Front Neurosci, 13, 332.
  12. Mac Donald CL, Barber J, Wright J, Coppel D, De Lacy N, Ottinger S, Peck S, Panks C, Sun S, Zalewski K, Temkin N (2018). Longitudinal Clinical and Neuroimaging Evaluation of Symptomatic Concussion in 10- to 14-year-old Youth Athletes. J Neurotrauma, 36(2), 264-274.
  13. de Lacy N, Calhoun VD (2018). Dynamic connectivity and the effects of maturation in youth with attention deficit hyperactivity disorder. Netw Neurosci, 3(1), 195-216.
  14. Mac Donald CL, Barber J, Wright J, Coppel D, De Lacy N, Ottinger S, Peck S, Panks C, Zalewski K, Sun S, Temkin N (2018). Quantitative Volumetric Imaging and Clinical Outcome Characterization of Symptomatic Concussion in 10- to 14-Year-Old Adolescent Athletes. J Head Trauma Rehabil, 33(6), E1-E10.
  15. Herzig L, de Lacy N, Capone G, Radesky J (2018). Intellectual Disability and Psychotropic Medications. J Dev Behav Pediatr, 39(7), 591-593.
  16. de Lacy N, Kodish I, Rachakonda S, Calhoun VD (2018). Novel in silico multivariate mapping of intrinsic and anticorrelated connectivity to neurocognitive functional maps supports the maturational hypothesis of ADHD. Hum Brain Mapp, 39(8), 3449-3467.
  17. Calhoun VD, de Lacy N (2017). Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis. Neuroimaging Clin N Am, 27(4), 561-579.
  18. de Lacy N, Doherty D, King BH, Rachakonda S, Calhoun VD (2017). Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum. Neuroimage Clin, 15, 513-524.
  19. Bachmann-Gagescu R, Dempsey JC, Phelps IG, ORoak BJ, Knutzen DM, Rue TC, Ishak GE, Isabella CR, Gorden N, Adkins J, Boyle EA, de Lacy N, ODay D, Alswaid A, Ramadevi A R, Lingappa L, Loureno C, Martorell L, Garcia-Cazorla, Ozyrek H, Halilolu G, Tuysuz B, Topu M, University of Washington Center for Mendelian Genomics, Chance P, Parisi MA, Glass IA, Shendure J, Doherty D (2015). Joubert syndrome: a model for untangling recessive disorders with extreme genetic heterogeneity. J Med Genet, 52(8), 514-22.
  20. King BH, de Lacy N, Siegel M (2013). Psychiatric assessment of severe presentations in autism spectrum disorders and intellectual disability. Child Adolesc Psychiatr Clin N Am, 23(1), 1-14.
  21. de Lacy N, King BH (2013). Revisiting the relationship between autism and schizophrenia: toward an integrated neurobiology. Annu Rev Clin Psychol, 9, 555-87.

Letter

  1. de Lacy N, Boyd Z (2026). The doctor is not in, but the Chatbot is: Utah's experience regulating mental health AI. [Letter to the editor]. NPJ Digit Med, 9(1).
  2. Kaufman J, Kobak K, Birmaher B, de Lacy N (2020). KSADS-COMP Perspectives on Child Psychiatric Diagnostic Assessment and Treatment Planning. [Letter to the editor]. J Am Acad Child Adolesc Psychiatry, 60(5), 540-542.